Keywords: localization, mobility, handoff management, wireless communications, heterogeneity, network architecture design, performance analysis, WLAN, RFID, WSAN localisation, mobilité, gestion de la handoff, communications sans fil, hétérogénéité, conception d'architecture réseau, analyse de performance, WLAN, RFID, WSAN. Indoor Localization and Mobility Management in the Emerging Heterogenous Wireless Networks localization, mobility, handoff management, wireless communications, heterogeneity, network architecture design, performance analysis, WLAN, RFID, WSAN

Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted.

Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies.

The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis.

Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene

Résumé

Au cours de ces dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseaux sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau. De point de vu de l'application ou l'utilisateur, la provision de services peuvent mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de handoff, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit.

Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision de handoff qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation réussi à l'intérieur est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles liées à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de handoff dans des réseaux hétérogènes sans fil de futur est beaucoup plus difficile que dans les réseaux traditionnels homogènes. La procédure de handoff doit être transparente pour satisfaire la qualité de service requise par les applications de futur et leurs fonctionnalités, cela ne doit pas dépendre de la caractéristique de l'opération des technologies différentes. En outre, les décisions de handoff devraient être suffisamment souples pour tenir compte aux préférences des utilisateurs d'un large éventail de critères proposés par toutes les technologies.

L'objectif principal de cette thèse est de mettre au point précis, le temps et l'emplacement de puissance efficaces et la gestion de handoff afin de mieux satisfaire les applications sensible des utilisateurs en dépendent au contexte dans lequel les utilisateur se trouvent. Pour obtenir une localisation à l'intérieur, le potentiel de réseau sans fil local (WLAN) et Radio Frequency Identification (RFID) comme une technologie autonome pour détection de location sont d'abord ont été étudiés par des expérimentations de plusieurs algorithmes et paramètres dans des plateformes réels ou par de nombreuses simulations, alors que leurs lacunes ont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie a été validée par des analyses profondes sur leur performance si elles fonctionnent d'une manière automne (sans intégration). En ce qui concerne la tâche de gestion de handoff, nous identifions que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux types de systèmes qui utilisent l'information obtenue à partir des systèmes de localisation one été proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. La deuxième méthode repose sur une approche intégrée RFID et réseaux de capteurs / actionneur Network (WSAN) de déploiement pour la localisation physique des utilisateurs et par la suite pour prédire leur prochaine point de handoff aux niveaux des couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil sous-jacent, les deux régimes peuvent être facilement mises en oeuvre dans des réseaux hétérogènes.

L'évaluation de la performance démontre les avantages de nos méthodes proposées par rapport aux protocoles standards concernant l'exactitude de prévision, le temps de latence et l'économie d' énergie. Les mots clés : mobilité, localisation, gestion de handoff, communication des réseaux sans fil, architecture des réseaux hétérogènes, analyse de performance, WLAN, RFID, WSAN.

Mots-clés :

localisation, mobilité, gestion de la handoff, communications sans fil, hétérogénéité, conception d'architecture réseau, analyse de performance, WLAN, RFID, WSAN.

analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogenous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savings.

Introduction

W ith the rapid growth of wireless communication and networking technologies, the great advances in mobile computing and handheld devices, and the overwhelming success of Internet, a revolutionary pervasive and mobile communication era is emerging as the natural successor of current mobile communication systems. The goal of this pervasive or ubiquitous computing vision is to create ambient intelligence with core concept the interaction between human with its environment and ultimate goal the enhancement of the user experience from the network. To that aim, an increasingly large numbers of everyday objects scattered throughout the surrounding environment will become smart by having some kind of simple computation and communication technology embedded into them, which will allow them to be connected to each other within local networks and, ultimately, connected to the Internet. Users will become even more mobile demanding to experience unobtrusive connectivity and ubiquitous access to different applications anywhere, anytime, by using the best technology from a plethora of interfaces available at the future multimode mobile terminals, and without the need for explicit awareness of the underlying communication and computing technology.

For the realization of such ubiquitous environments, location awareness and efficient mobility, in terms of handoff, management are two core concepts. Furthermore, a strong correlation exists between them. The continuous need for determining the unknown location of an entity stems from its mobility capability. Simultaneously, dealing with issues raised due to mobility can benefit if location information is available. This thesis targets at improving both the localization and handoff processes and proposes taking advantage of the availability of several wireless technologies for tackling more effectively the objectives

Introduction

Chapter 1

of future communication networks. In the following, the main objectives, challenges and our approaches for achieving that goal are described.

Objectives and Challenges

Indoor location information is valuable for facilitating the interaction between a user and its environment and consequently the development of location based services (LBS) or more generally speaking context-aware applications where location is a key element of the user's context. Such applications adapt their functionality depending on the user's context and they span from applications in users' everyday life, working environment, commercial and industrial sectors to functions which aim at the performance enhancement of the wireless network functionality. Some typical examples of location-aided applications are:

≤ Ambient Assisted Living: Accurate positioning information is critical for the success of the Ambient Assisted Living (AAL) project [START_REF]Ambinet Assisted Living project (AAL)[END_REF] which aims at enhancing the everyday life of elderly users and people suffering from disabilities, through the use of Information and Communication Technologies (ICT).

≤ Person and Asset Tracking: Tracking of people inside buildings is critical in emergency situations such as fires, earthquakes, or other disasters. Moreover, indoor location systems are useful in hospitals for tracking staff members at any time without their intervention, in museums or schools for keeping track of children location [2].

Tracking of objects or assets is useful for finding the whereabouts of hospital equipment in a hospital, finding books inside a library or products inside a warehouse.

The location of various physical resources such as printers, projects, and copiers also enables resource discovery applications [3].

≤ Navigation: Indoor location information can be used to build navigating tools in unfamiliar buildings [4], such as airports, train stations, museums, campuses, commercial department stores or big office buildings.

≤ Location-Based Advertising and Social Networking: Location methods can be used for selective and targeted advertising [5] and for providing product information inside retail stores [6].

On the other hand, location based social networking may further enhance the Internet

Objectives and Challenges

based social networking services such as Facebook, Friendsters, MySpace, etc. by allowing users forming groups based on their social preference and interest.

≤ Network performance improvement User location information can be also exploited to enhance the functionality and the QoS in wireless networks. Such methods have been proposed for location-based access control [7], location-based handoff and in ad hoc networks in order to optimize routing algorithms and network self configuration [START_REF] Bocquet | Using enhanced-tdoa measurement for indoor positioning[END_REF]. One step further, combining positioning data with user profiles could significantly facilitate network planning, load balancing, caching of information closer to the user, radio resource management and design of other performance enhancement methods [START_REF] Prasithsangaree | On indoor position location with wireless lans[END_REF].

For the success of the above applications, the design of an accurate and reliable location determination system is essential. Wireless localization, i.e. location estimation by using radio signals (RS), has attracted considerable attention in the fields of telecommunication and navigation. The most well known positioning system is the Global Positioning System (GPS) [START_REF] Wellenhoff | Global Positions System: Theory and Practice[END_REF], which is satellite-based and is successful for tracking users in outdoor environments. However, the inability of satellite signals to penetrate buildings cause the complete failure of GPS in indoor environments. For indoor location sensing a number of alternative wireless technologies have been proposed, such as infrared (IR), ultrasound, Wireless LAN (WLAN), UltraWideBand (UWB), Radio Frequency Identification (RFID), Bluetooth, wireless sensor networks (WSNs) [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF]. However, the indoor radio propagation channel is characterized as site specific, exhibiting severe multipath effects and low probability of line-of-sight (LOS) signal propagation between the transmitter and receiver [START_REF] Pahlavan | Wireless Information Networks[END_REF], making accurate indoor positioning very challenging. Moreover, compared to outdoor systems, determining the location of a user or device inside a building is much more difficult not only due to its harsh nature but also due to the requirement of indoor services for higher and more precise accuracy.

Handoff management is the process for keeping active the connection of the mobile user while changing its point of attachment to the network due to mobility. In the future pervasive networks, several heterogeneous wireless technologies will be available and users will demand ubiquitous access and "always best" connectivity to a wide range of applications while on the move. For the harmonized integration of these different technologies under a common framework, the design of intelligent mobility management schemes is required in order to enable mobile users to experience uninterrupted service continuity anywhere,

anytime, regardless their underlying access technology. Furthermore, mobility management schemes should be able to satisfy the requirements of emerging applications which are becoming more and more demanding regarding their QoS constraints.

However, the latency during the handoff processes leads to performance degradation.

For the case of IEEE 802.11 WLAN wireless access, the handoff process requires from the mobile node to search periodically for better access points to associate with, by scanning all WLAN channels. However this process is power consuming and introduces packet loss, since during scanning the mobile node is not able to be served by its current AP. Mobile IP [START_REF] Bhagwat | Network layer mobility: an architecture and survey[END_REF] is a network layer mobility management scheme for IP-based networks. It forwards packets to mobile users that are away from their home networks using IP-in-IP tunnels.

Mobile IP handoff is composed of a sequence of stages, one of which includes the detection of a mobile node's movement to the new network. However, when the mobile node undergoes movement detection, it is unable to receive IP packets, resulting in further performance degradation.

Thesis Overview

Admittedly, location awareness of users and objects or devices in an indoor environment and their mobility management across heterogeneous networks are considered as key milestones towards the realization of future mobile communication networks. Furthermore, the strong correlation between these tasks mandates investigating their aspects in parallel, instead of considering them as two independent processes. This thesis targets the development of location and mobility management schemes with main design goals:

≤ Accuracy. Knowing exactly where someone or something is or moves towards can improve user experience by personalized service delivery and also enhance the network functionality.

≤ Fast time response. Emerging applications will be more demanding in terms of QoS requirements, impelling for fast localization and handoff schemes.

≤ Scalability. The presence of many users should not degrade the system performance.

≤ Generic handoff. The co-existence of heterogeneous networks within which the user can roam, make technology-independent handoffs the most viable solutions.

Workflow and Contributions

21

≤ Energy-awareness. Since mobile devices are battery constrained, energy consumption issues should be taken into account as well.

Future communication systems are envisioned to be heterogeneous offering ubiquitous connectivity, whereby mobile users will be surrounded by diverse but complementary technologies capturing their different needs and requirements. Motivated by this observation, exploring possible synergies and interactions among several technologies was our main approach in order to tackle more effectively our goals.

For indoor location sensing, we focused our attention on two wireless technologies;

WLAN and RFID. WLANs, such as IEEE 802.11, is considered as a promising one for offering a low-cost and reliable solution due to its availability in most indoor environments and capability for coordinated communication when in infrastructure mode. However, its accuracy is highly affected in the presence of severe multipath and environmental changes.

More recently, RFID has emerged as an attractive technology for accurate location sensing due to the low cost of passive tags, the fast reading of multiple tags, the non Line of Sight (LOS) requirement, the less sensitivity in user orientation. However, the main shortcoming of RFID is considered the interference problem among its components, mainly due to the limited capabilities of the passive tags and the inability for direct communication between readers [START_REF] Gp | Survey, Nomenclature and Comparison of Reader Anti-Collision Protocols in RFID[END_REF]. In order to overcome the limitations of both technologies we proposed an integration architecture for improving the localization performance.

Regarding the mobility management problem, we focused on the handoff component and we explored the potential of two popular pervasive technologies: RFID and WSAN, for providing fast handoff solution in the case of IP-based mobility over a WLAN access network. However, our proposed schemes can be applied for different link and network level mobility scenarios, making them viable solutions in heterogeneous networks.

Workflow and Contributions

For achieving the goals and objectives of this thesis the succeeding steps were followed in a chronological order:

≤ Initially, we focused on the case of WLAN fingerprinting positioning approach which is considered as the most popular for low-cost indoor localization. Considering both its deterministic and probabilistic variants, we proposed some simple techniques in order -J1 A. Papapostolou and H. Chaouchi, Scene Analysis Indoor Positioning Enhancements, Annals of Telecommunications journal, October 2010 [START_REF] Papapostolou | Scene analysis indoor positioning enhancements[END_REF].

≤ RFID positioning is considered as another attractive solution for location sensing with higher accuracy than WLAN systems. However, not much attention has been given

to the collision problem, as far as positioning is concerned, which is the Achilles' heel of RFID technology. Therefore, we studied extensively the performance of the most popular RFID positioning algorithms in the presence of multiple users. Reference Networking Conference, Gdansk, Polland, September 2009 [START_REF] Papapostolou | Considerations for RFID-Based Indoor Simultaneous Tracking[END_REF].

-J2 A. Papapostolou and H. Chaouchi, RFID-assisted Indoor Localization and the Impact of Interference on its Performance, in the SI on RFID Technology, Systems, and Applications of the Journal of Network and Computer Applications (Elsevier), April 2010 [19].

≤ Motivated by the benefits but also the limitations of the stand alone solutions, as identified in the previous steps, an integration architecture combining both WLAN and RFID technologies was then proposed. The main idea is to take advantage of the localization accuracy offered by the RFID deployment and the coordination capability of the WLAN infrastructure for minimizing the collision problem on the RFID channel 1 In the enumeration list, the symbols C, J, B stand for publications in Conferences, Journals and Book Chapters, respectively.

Introduction

Organization of the Thesis

The rest of this thesis is organized as follows. In order to facilitate its presentation, we divided it into two parts: part I is devoted to localization, whereas part II focuses on mobility management aspects. The first chapter of both parts, i.e. chapters 2 and 6, include background and related work essential for the comprehension and highlighting of our contributions. Chapter 3 describes our proposed methods for improving the WLAN fingerprinting localization accuracy, chapter 4 studies the performance of RFID when the collision problem comes into place and chapter 5 ends part I by describing a heterogenous system which combines the benefits of both technologies. Chapter 7 describes and compares the two schemes we propose for mobility management. The first scheme relies on the RFID technology for reducing the network layer handoff latency, while the second scheme utilizes key properties of both RFID and WSAN technologies for handoff latency reduction but also energy saving. Finally chapter 8 summarizes our main conclusions, achievements and open issues for future research.

Part I Indoor Localization

Chapter 2

Indoor Localization L ocation awareness is one of the main concepts for the realization of ubiquitous and context-aware communications in the envisioned future wireless networks. From application point of view, location information is essential for enabling Location Based Services (LBS) in commercial, health-care, public safety and military domains [START_REF] Küpper | Location-Based Services: Fundamentals and Operation[END_REF]. From network perspective, location awareness can be utilized for enhancing mobility management functionalities such as routing, mobility prediction, handoff management for quality of service provisioning [7]. Moreover, position information can assist the self-organization, self-configuration of ad-hoc and sensor networks in the future communication networks [START_REF] Bocquet | Using enhanced-tdoa measurement for indoor positioning[END_REF].

Indoor positioning is a complex engineering problem that has been approached by many computing communities: networking, robotics, vision, and signal processing. The wide success and penetration of wireless networks in the realm of consumer applications attracted the attention of most of the research and industry communities for the development of wireless positioning systems, whereby location tracking is achieved with the aid of received radio signal properties. The most well known positioning system is the Global Positioning System (GPS) [START_REF] Wellenhoff | Global Positions System: Theory and Practice[END_REF], which is satellite-based and is successful for tracking users in outdoor environments. However, the inability of satellite signals to penetrate buildings cause the complete failure of GPS in indoor environments. Thus, for indoor location sensing a number of alternative wireless technologies have been proposed, such as Cellular, wireless LAN, infrared, ultrasound, ultra-wideband (UWB), RFID, sensor networks [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF].

Even though location estimation have been investigated extensively in the last few decades, there is still no absolute solution satisfying all performance requirements. This is because, the indoor radio propagation channel is characterized as site specific, exhibiting se-

Indoor Localization

Chapter 2

vere multipath effects and low probability of line-of-sight (LOS) signal propagation between the transmitter and receiver [START_REF] Pahlavan | Wireless Information Networks[END_REF], making accurate indoor positioning very challenging.

The aim of this chapter is to provide essential background regarding positioning principles in conjunction with the relevant state-of-the-art. The rest of this chapter is organized as follows: in section 2.1 we first outline the principle aims and requirements a positioning system should satisfy. Sections 2.2, 2.3 and 2.4 describe the most outstanding signal metrics, positioning techniques and sensing technologies, respectively, that are employed by the majority of the current state-of-the-art indoor location systems. Finally, section 2.5

provides the chapter summary along with our research directions for indoor positioning.

Positioning Aims and Requirements

The localization problem is defined as the process of determining the current position of a mobile node or an object within a specific region, indoor or outdoor. The position can be expressed in several ways, depending on the application requirements or the positioning system specifications. For instance, absolute coordinates, relative or symbolic locations are possible formats.

The general framework of a wireless positioning system is illustrated in For evaluating the efficiency of localization schemes the major performance objectives are summarized in the following [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF],

≤ Accuracy is the most important requirement a location system should satisfy. Its most common metric is the mean distance error, defined as the average of the Euclidean distances between the actual and the estimated locations.

≤ Precision is a metric for evaluating the consistency or reliability of the system over many trials. It is defined as the standard deviation of the distribution of the distance ≤ Scalability refers to the number of nodes that can be simultaneously tracked or the scale of the target area, e.g. metropolitan, campus-wide, within a building, etc.

≤ Deployment and maintenance cost refers to the hardware requirements and labor effort for installing, setting up and maintaining the positioning system.

≤ Power consumption is also an important aspect especially when the positioning process is performed by energy-constrained terminals.

≤ Fault tolerance or robustness refer to the ability of the positioning system to perform well even under harsh conditions such as the failure of a positioning component.

The wide range of applicability of location information and the diversity of performance requirements drove the research over several directions, resulting in a vast variety of proposed positioning systems. Consequently, a classification among them is essential for assisting their differentiation and evaluation. Several classification criteria can be ap-

Indoor Localization Chapter 2

plied with the most prevalent ones being the type of metric of the received radio signal measurements, the positioning technique and the wireless sensing technology.

Received Radio Signal Metrics

The most traditional received radio signal metrics are the Time of Arrival (ToA), Time Difference of Arrival (TDoA), Received Signal Strength (RSS), Signal to Noise Ratio (SNR), Angle of Arrival (AoA) or combinations of more than one of these metrics [START_REF] Guvenc | A survey on toa based wireless localization and nlos mitigation techniques[END_REF], [START_REF] Liu | Survey of Wireless Indoor Positioning Techniques and Systems[END_REF], [START_REF] Gezici | A survey on wireless position estimation[END_REF].

In the following, a more detailed description of these most commonly used signal metrics is provided, followed by a discussion comparing their main advantages and limitations.

Time of Arrival (ToA)

Times of Arrival (ToA) refers to the time needed for a signal to travel from one node to another which indicates their separation distance. Thus, in TOA-based systems, the propagation time of a signal from a FRP to the target node is measured in order to calculate their distance. In order to measure the ToA parameter for a signal traveling between two nodes, these nodes must be timely synchronized and the transmitted signal must be timestamped.

Let s(t) denote the signal transmitted from a node to another at time t. Then, the received signal is expressed as

r(t) = s(t τ ) + n(t), (2.1)
where τ represents the ToA and n(t) is white Gaussian noise with zero mean and a spectral density of N o /2.

The most well-known methods for performing ToA estimations are considered to be the correlator or Matched Filter (MF) receivers [START_REF] Turin | An introduction to matched filters[END_REF]. According to the correlator-based approach the received signal is correlated with a local template s(t τ ) for various delays τ in order to calculate the delay corresponding to the correlation peak. Similarly, the MF approach employs a filter that is matched to the transmitted signal and estimates the instant at which the filter output reaches its higher value. Both approaches are optimal in the maximum likelihood (ML) sense for the signal model in eq. (2.1).

However, in real environments, signal distortion due to multipath characteristics, affect the optimality of these conventional schemes.

Time Difference of Arrival (TDoA)

The TDoA approach is followed to replace the absolute synchronization requirement between the target node and the FRPs with the more moderate requirement for relative clock synchronization among the FRPs only. TDoA estimates the time difference between the arrival times of two signals traveling between the target node and two FRPs, which determines the position of the target node on a hyperbola, with foci at the two FRPs.

One approach for estimating TDoA is to first estimate ToA for each signal traveling between the target node and a FRP, and then to subtract the two estimates. Since the target node and the reference nodes are not synchronized, the ToA estimates include a timing offset, which is the same in all estimates as the reference points are synchronized, in addition to the time of flight. Therefore, the TDoA estimate can be obtained as

τ TDoA =τ 1 τ2 , (2.2)
where τi ,f o ri =1, 2, denotes the ToA estimate for the signal traveling between the target node and the ith reference point.

Another approach for TDOA estimation is to perform cross-correlations of the two signals traveling between the target node and the FRPs, and to calculate the delay corresponding to the largest cross-correlation value. The cross-correlation function of these signals is given by integrating the lag product of two received signals over a time period T

R1,2 (τ )= 1 T r 1 (t)r 2 (t + τ )dt. (2.
3)

The TDOA is the value τ that maximizes R 1,2 (τ ), i.e. the range differences.

Angle of Arrival (AoA)

In AoA-based systems the position is calculated via goniometry. The location of the target node lies on the intersection of several pairs of angle direction lines, each formed by the circular radius from a FRP to the target node. The angle between two nodes can be determined by estimating the AoA parameter of a signal traveling between the nodes. AoA estimates are obtained with the aid of directional antennas based on the beamforming technique [START_REF] Van Veen | Beamforming: A versatile approach to spatial filtering[END_REF] or with the aid of antenna arrays based on the principle that differences in arrival times of an incoming signal at different antenna elements include the angle information if the array geometry is known [START_REF] Caffery | Wireless Location in CDMA Cellular Radio Systems[END_REF].

Indoor Localization Chapter 2

For narrow-band signals, time differences can be represented as phase shifts. Therefore, the combinations of the phase shifted versions of received signals at different array elements can be tested in order to estimate the AOA. However, for wide-band systems, time delayed versions of received signals should be considered, since a time delay cannot be represented by a unique phase value for a wide-band signal.

Received Signal Strength (RSS)

Signal strength refers to the power or energy of the signal traveling between two nodes.

RSS systems are based on propagation-loss equations which are indicative of the distance r between those nodes, due to signal attenuation as their distance increases. The free-space transmission loss, L B , for instance, is proportional to 1/r 2 . Thus, if RSS measurement is used in combination with a path-loss and shadowing model a distance estimate can be obtained. In two-dimensional space, such signal measurement determines the location of one node on the circle centered at the other node with radius their estimated distance.

However, in practice, a signal traveling from one node to another experiences fast (multipath) fading, shadowing and path-loss, resulting in non-deterministic radio propagation models [START_REF] Rappaport | Wireless Communications: Principles and Practice[END_REF]

PL(d)=PL(d o )+10n log d d o + X σ , (2.4)
where PL(d) the path loss for distance d between a FRP and the target node, PL(d o ) the free space path loss at reference distance d o , n the path loss exponent whose value depends on the frequency used, the surroundings and building type, and X σ is a zero-mean Gaussian

Range-free Metrics

All the above metrics belong to the category of range-based measurements. However, there is another type of metrics which do not rely on such ranging measurements and they are known as range-free or anchor-based metrics [START_REF] He | Range-free localization schemes for large scale sensor networks[END_REF]. Usually, a heterogeneous network is considered which consists of two types of nodes: (i) anchors which are powerful nodes with established location information and beacon their position to their neighbors, and (ii)

blind nodes which listen to such beacons in order to calculate their position.

In [START_REF] Bulusu | GPS-less low-cost outdoor localization for very small devices[END_REF] the simple centroid algorithm is proposed for estimating the unknown location of a blind node based on the announced locations of the anchor nodes. In DV-HOP [START_REF] Niculescu | DV Based Positioning in Ad Hoc Networks[END_REF] anchors, instead of single hop broadcasts, flood their location throughout the network maintaining a running hop-count at each node along the way. Blind nodes calculate their position based on the received anchor locations, the hop-count from the corresponding anchor, and the average-distance per hop; a value obtained through anchor communication.

Like DV-Hop, the Amorphous positioning algorithm proposed in [START_REF] Nagpal | Organizing a global coordinate system from local information on an ad hoc sensor network[END_REF] uses offline hopdistance estimations, improving location estimates through neighbor information exchange.

In [START_REF] He | Range-free localization schemes for large scale sensor networks[END_REF], an area-based scheme, called APIT, performs accurately location estimations with the irregular radio pattern and random node placement. The main idea of APIT is to divide the whole network into triangular regions among anchors, and then to determine the possible position of a blind node via the aggregation of the two distinct triangular regions. Consequently, the position of the node can be estimated by calculating the Center of Gravity (CoG) of the intersections of the triangles where the node resides.

Comparison

Both ToA and TDoA metrics require strict time synchronization, either between both target nodes and the FRPs or between the FRPs only, respectively. Thus, such metrics are most suited for cellular networks since the receiving nodes are typically synchronized to base stations.

Obtaining AoA measurements is more expensive in implementation compared to ToA and TDoA due to the utilization of special hardware such as antenna arrays, and complex transmission techniques such as beamforming. Moreover, it requires a minimum distance between the receivers which results in additional costs and larger node sizes. Furthermore, this technique is highly sensitive to multipath, NLOS conditions, and array precision.

The main advantages of RSS metric are its low cost and ease of obtaining such mea-

Indoor Localization Chapter 2

surements for most of the receivers. Thus, obtaining RSS information is much simpler than applying signal processing techniques to extract the time or angle of arrival. Since RSS positioning is based on theoretical or empirical models in order to convert the received signal strength measurements to distance estimates its performance depends highly on the channel behavior and the accuracy of the employed radio propagation model. However, node mobility and unpredictable variations in channel behavior, which are even more intense in a complicated indoor space, can occasionally lead to large errors in distance evaluation.

Also, this technique is very susceptible to noise and interference.

Finally, comparing range-based and range-free metrics, one would say that selecting the optimal one depends on the assumptions for the network. For instance, range-free metrics are considered as cost-effective solution and thus, more suitable for wireless sensor networks where sensor nodes have limited hardware capabilities. The performance of range-free metrics depends on the density of the anchor nodes and the complexity of the positioning algorithm.

Principle Location Estimation Techniques

Indoor location systems can be classified based on the principle approach followed by the positioning algorithm. [START_REF] Hightower | Location Systems for Ubiquitous Computing[END_REF] and [START_REF] Liu | Survey of Wireless Indoor Positioning Techniques and Systems[END_REF] provide interesting surveys on the basic positioning techniques and taxonomies of localization systems based on them. There are three principle classes of positioning techniques, namely proximity, triangulation, either lateration or angulation, and scene analysis, which are employed either alone or in combination and in either their baseline or enhanced version by any location determination system. In the sequence, we describe the general mechanism, performance advantages and limitations of each approach.

Proximity

Proximity-based localization approaches provide symbolic relative location information and their key concept is the "nearness" to objects with known positions, as shown in Figure 2.2(a). Usually, proximity-based algorithms rely on a dense grid of antennas, each having a well-known position and employ range-free signal metrics where location-aware objects are considered as the anchor nodes. The identification of such objects such as credit card point of cell-ID, Cell of Origin (CoO) [START_REF] Sahi | Geolocation on cellular networks[END_REF], topology or connectivity information [START_REF] Want | The Active Badge Location System[END_REF] and physical contact detection with the aid of sensors [START_REF] Hinckley | Touch-Sensing Input Devices[END_REF] are examples of proximity-based approaches that can be employed for retrieving location information.

Principle Location Estimation Techniques

In general, proximity methods are considered as simple techniques but with limited capabilities regarding their accuracy performance. For enhancing their accuracy, hardwarebased solutions, such as a denser deployment of sensors or identifiable objects are required, resulting in higher cost for the development and maintenance of the positioning system.

Triangulation

Triangulation uses the geometric properties of triangles to estimate the target location.

It has two derivations: lateration and angulation which differ in the method for obtaining range estimations between the target node and each of the fixed reference points.

Lateration

Lateration or distance estimation techniques determine the position of an object by measuring its distance from several fixed reference points. For a two dimensional space at least three such reference points are required which do not lie in the same line. In Lateration methods employ the range-based signal measurements of RSS, ToA, TDoA or combinations, and can be further classified accordingly.

Lateration systems require coverage from at least three reference points in order to provide a reliable location estimate. Moreover, the performance of a specific lateration approach shares the advantages and disadvantages of the corresponding signal measurements,

Indoor Localization Chapter 2

i.e. RSS, ToA or TDoA metrics.

Angulation

Angulation techniques are very similar to lateration methods, with the difference that angles instead of distances are measured. For a two-dimensional space, two at least reference points are required which are equipped with directional antennas or support advanced transmission techniques such as beamforming. Based on the received angle of arrival (AoA) measurement of both transmitted signals, the known distance between the reference points and triangle properties, the unknown position of the receiver is calculated. Angulation-based approaches are very accurate and precise. However, their dependency on advanced hardware and transmission techniques increases the cost and complexity of the positioning system and limits their adoption and incorporation for a low cost, simple and fast positioning solution.

Scene Analysis

The main concept of scene analysis methods, also known as fingerprinting, is that special features of the scene observed at a specific position are exploited for describing and subsequently identifying that position. Thus, the location of an unknown point can be inferred based on the similarity of such observed scene characteristics. The main advantage of these methods is that they do not rely on any theoretical model,

Popular Location Sensing Technologies

All aforementioned positioning techniques can be based on any available technology by taking advantage of the characteristics of the corresponding emitted signals [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF]. The most common technology types applied for positioning technologies are outlined in this section.

Satellite signals are very successful for outdoor positioning with GPS [START_REF] Wellenhoff | Global Positions System: Theory and Practice[END_REF] being the most famous system employing them. However, their inability to penetrate inside buildings cause their complete failure for indoor positioning and therefore are excluded from our relevant survey. This section also provides the current state-of-the art in location systems, classified

Indoor Localization Chapter 2

based on their employed wireless technology. [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF], [START_REF] Liu | Survey of Wireless Indoor Positioning Techniques and Systems[END_REF] and [START_REF] Hightower | Location Systems for Ubiquitous Computing[END_REF] provide a more extensive survey of location systems.

Infrared (IR)

IR technology is attractive for indoor positioning systems because it is available on board of various, very common wired and wireless devices, such as TV, printer, mobile phones, PDAs, etc. The Active Badge location system [START_REF] Want | The Active Badge Location System[END_REF] developed at AT & T Cambridge in 1990s is one of the first indoor badge sensing systems. It uses diffuse infrared technology and follows the proximity based positioning approach. Each person to be located wears an active badge which emits on demand a globally unique infrared signal every 10 seconds.

Within each target area such as a room, one or more pre-build infrared sensors detect the emitted signals from the badges and forward this information to a central server to which they are connected via wired links. The server upon reception of this data, estimates the position of the detected badges and provides it to several location-aware applications.

However, an IR-based positioning system, which offers absolute position estimations, needs LOS communication between transmitters and receivers without strong light interference. Thus, the coverage range per infrastructure device is limited within a room.

Furthermore, an IR signal is influenced by fluorescent light and sunlight. Finally, the wired links for connecting the sensors increase its deployment cost.

Ultrasound

Ultrasound positioning systems provide a kind of inexpensive positioning solutions.

Usually the ultrasound signals used to locate objects need to be combined with RF signals, which perform synchronization and coordination in the system. These ultrasound positioning systems increase the system coverage area.

The Active Bat positioning system [START_REF] Harter | The anatomy of a context-aware application[END_REF] also developed at AT & T Cambridge uses ultrasonic technology and follows the ToA-based lateration technique for determining the position of an active bat, which is a tag carried by a person or attached to an object. Sensor nodes are mounted on the ceiling of the target area in a grid fashion. A controller sends requests via short range to the bats and simultaneously a synchronized reset signal to the ceiling sensors using a wired serial network. In response to the request packets sent by the controller, each bat broadcasts a pulse of ultrasonic to the grid of the ceiling sensors. Each ceiling sensor measures the time interval from reset to ultrasonic pulse arrival and computes its distance from the Bat. The local controller then forwards the distance measurements to a central controller, which performs the lateration computation. The location estimation of the Active Bat is more accurate than Active Badge [START_REF] Want | The Active Badge Location System[END_REF] and it can also provide orientation information. However, the performance of ultrasonic is influenced by the reflection and obstacles between tags and receivers, which degrades the system accuracy. Finally, the ceiling sensors need to be connected through cables which increases the deployment cost of the system.

The Cricket Location Support System [START_REF] Priyantha | The Cricket Location-Support system[END_REF] also uses ultrasound emitters as infrastructure and follows the ToA-based lateration. However, the computations are performed locally by the objects to be located for reducing the cost and ensuring more privacy. The emitters also transmit RF signals for synchronization of the ToA measurements and forwarding their location information in a decentralized fashion. Such location information is used for proximity based positioning in case of the failure of the lateration due to insufficient number of received ultrasonic beacons. affected by the complex behavior of signal propagation [START_REF] Ladd | Robotics-based location sensing using wireless ethernet[END_REF], and by various elements in indoor environments such as movement and orientation of human body, the overlapping of APs, the nearby tracked mobile devices, walls, doors, etc.

Cellular network

The Daedalus project [START_REF] Hodes | Composable ad-hoc mobile services for universal interaction[END_REF] developed a WLAN proximity-based system for coarsegrained user location. A mobile host estimates its location to be the same as the access point to which it is attached. Therefore, the accuracy of the system is limited by the access point density.

RADAR [START_REF] Bahl | RADAR: An In-Building RF-Based Location and Tracking System[END_REF] approach to determine the location from the radio map that best fits the collected signal strength information.

The Aura system proposed in [START_REF] Smailagic | Location sensing and privacy in a context aware computing environment[END_REF] uses two techniques: Pattern Matching (PM) and

Triangulation, Mapping and Interpolation (TMI). The PM approach is very similar to the RADAR approach. In the TMI technique, the physical position of all the access points in the area needs to be known and a function is also required to map signal strength onto distances. Based on this information, a set of training points at each trained position is generated. The interpolation of the training data allows the algorithm to use less training data than the PM approach. During the online phase, they use the approximate function they got from the training data to generate contours and they calculate the intersection between different contours yielding the signal space position of the user. The nearest set of mappings from the signal-space to the physical space is found by applying a weighted average, based on proximity, to the signal space position.

The Horus [START_REF] Youssef | The Horus WLAN location determination system[END_REF] is WLAN RSS fingerprinting localization system which defines the possible causes of variations in the received signal strength vector and devises techniques to overcome them, namely providing the correlation modeler, correlation handler, continuous space estimator, and small-space compensator modules. Moreover, it reduces the computational requirements of the location determination algorithm by applying location-clustering techniques.

The Nibble location system, from UCLA, is a WLAN-based scene analysis scheme which uses a Bayesian network to infer a user location [START_REF] Castro | A probabilistic room location service for wireless networked environments[END_REF]. Their Bayesian network model includes nodes to be localized, noise, and access points (sensors). The signal to noise ratio observed from an access point at a given location is taken as an indication of that location.

Ekahau [52] is a commercial real-time location system (RTLS) which also uses the WLAN infrastructure but combines site calibration and the RSS-based triangulation technique for determining the location of WiFi enabled devices.

Bluetooth

Bluetooth is a low-cost and low-power technology and many devices are already equipped with it. Thus, it can be used in location sensing. However, a disadvantage of Bluetoothbased positioning system is that the system can only provide accuracy from 2 to 3 m with the delay of about 20s. Furthermore, Bluetooth positioning systems suffer from the drawbacks of RF positioning technique in the complex and changing indoor situations. [START_REF] Forno | Design and implementation of a Bluetooth ad hoc network for indoor positioning[END_REF] is a bluetooth-based positioning system.

Radio Frequency Identification (RFID)

RFID is a means of storing and retrieving data through electromagnetic transmission to an RF compatible integrated circuit. Thus, it is not only for the indoor positioning applications, but also provides many potential services for the demands of users. The advantage of an RFID positioning system is that cheap, light and small tags can be taken by people to be tracked. The RFID system can uniquely identify equipments and persons tracked in the system. However, the proximity and absolute positioning techniques need numerous infrastructure components installed and maintained in the working area of an RFID positioning system.

SpotON [START_REF] Hightower | SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength[END_REF] is RFID positioning system which uses RSS measurements to estimate the distance between a target tag and at least three readers and then applies trilateration on the estimated distances.

LANDMARC [START_REF] Ni | LANDMARC: Indoor Location Sensing Using Active RFID[END_REF] employs also the RFID technology but follows a scene analysis approach by using readers with different power levels and reference tags placed at fixed, known locations as landmarks. Readers vary their read range to perform RSS measurements for all reference tags and for the target tag. The k nearest reference tags are then selected and their positions are averaged to estimate the location of the target tag.

Ultrawideband (UWB)

The UWB technology offers various advantages over other positioning technologies: no line-of-sight requirement, no multipath distortion, less interference, high penetration ability, etc. Thus, using UWB technology provides a higher accuracy. However, the high cost of UWB-enabled devices and infrastructure is an issue.

[57] is a proprietary UWB-based positioning solution.

Wireless Sensor Networks (WSN)

Wireless localization techniques have also been explored for localization in sensor networks. Sensor networks are ad hoc networks of many autonomous nodes deployed to perform a variety of distributed sensing tasks, by sensing physical or environmental condition including sound, pressure, temperature, light, etc., and generating proportional outputs.

Based on such measurements, a person or device can be located. Georgia Tech's Smart Floor [START_REF] Orr | The smart floor: A mechanism for natural user identification and tracking[END_REF] is a proximity based location system. Embedded pressure sensors on the floor of the area capture footfalls and Hidden Markov Models recognize the users according to their profiles. The system has the disadvantages of poor scalability and high incremental cost, because the floor of each building in which Smart

Floor is deployed must be physically altered to install the pressure sensor grids. [START_REF] Niculescu | Positioning in ad hoc sensor networks[END_REF] provides a survey for sensor-based positioning. ZigBee technology is a special case of such schemes and in [START_REF] Noh | Comparison of the mechanisms of the zigbee's indoor localization algorithm[END_REF] the authors enumerate and compare mechanisms based on this standard.

In general, sensor-based positioning is cost effective due to the decreasing of the price and the size of sensors. However, their limited processing capability and battery power reduce the accuracy for real-time tracking.

Chapter Summary

Undoubtedly, accurate and cost effective acquisition of a user or service location information, as part of the more general term of context, is one of the major envisions for the realization of the next generation user-oriented wireless networks.

In this chapter the principle location methods and the most popular wireless technologies for location sensing were reviewed focusing on their advantages and limitations. It is generally accepted that there is still no single technique or technology offering a general solution satisfying all requirements, impelling for new approaches and research directions.

The insufficiency of stand-alone solutions in combination with the heterogeneity of available technologies in the future pervasive environments drove the research over synergetic approaches for solving more efficiently the challenges of indoor localization [START_REF] Patterson | Challenges in location-aware computing[END_REF]. The SE-Lective Fusion LOCation estimation (SELFLOC) [START_REF] Gwon | Robust Indoor Location Estimation of Stationary and Mobile Users[END_REF] algorithm infers the user location by selectively fusing location information from multiple wireless technologies and/or multiple classical location algorithms in a theoretically optimal manner. The authors in [START_REF] Hossain | Indoor Localization using Multiple Wireless Technologies[END_REF] propose a fingerprinting method by combining the signal strength measurements from WiFi and bluetooth systems and report accuracy increase.

The promising research trend towards technology integration for tackling more effectively the problem of indoor location sensing is also adopted by us, as explained later in this thesis.

Chapter 3

WLAN Scene Analysis

Localization W ireless Local Area Networks (WLANs) are very common in most indoor environ- ments for providing wireless communication in an infrastructure or ad hoc mode.

Therefore, WLAN systems are considered as an attractive technology for performing indoor location sensing. For indoor positioning scene analysis, or fingerprinting, schemes are mostly preferred than triangulation schemes due to their independence on an indoor propagation radio model.

However, utilizing the WLAN technology for localization has a number of limitations.

Since the main target of a WLAN is the communication between its components, the deployment of the APs is such that minimum overlapping is achieved, undesirable for localization purposes. Moreover, the inherent characteristics of the wireless medium, the so called propagation losses, and uncontrollable environmental changes cause undesirable variations of the signal properties, deteriorating the positioning process. Finally, the orientation of the user has also a strong impact on the power level of the received signal causing uncertainty in fingerprinting positioning techniques.

In this chapter, we focus on WLAN-based indoor positioning and study both its probabilistic and deterministic fingerprinting variants. In general, probabilistic approaches are more accurate but more complex than the deterministic ones. We first identify how the inherent impairments of the wireless medium and indoor characteristics implicate the positioning process and suggest ways to mitigate them by employing simple techniques with low processing requirements. More specifically, we firstly propose training the positioning system by obtaining several samples of Received Signal Strength (RSS) measurements for different locations and orientations from the Access Points (APs) within the studied indoor environment. A simple processing of these RSS samples is then suggested. This is to evaluate their quality and reliability before storing them to the radio map. During the real-time positioning process, techniques to further increase the system accuracy and reduce its time response are explored. More precisely, using the current user orientation information for reducing the number of candidate training points and a hierarchical searching algorithm for selecting these candidates are examined.

The remainder of this chapter is organized as follows: section 3.1 gives some background regarding WLAN fingerprinting positioning and systems. Section 3.2 describes its main challenging issues which motivated this study. Section 3.3 describes our proposed localization approaches and section 3.4 discusses some system design parameters. In section 3.5 we evaluate the performance of the proposed schemes and compare them with two other popular location systems for the same experimental testbed. Finally, section 3.6

summarizes the main points and contributions of this chapter.

WLAN Localization

This section gives an overview of the WLAN technology from the localization perspective and reviews two popular WLAN fingerprinting positioning systems which initiated our study.

WLAN Technology Overview

Wireless LAN (WLAN) is the most widely adopted wireless networking technology due to its low infrastructure cost, ease of deployment and high data rates, by utilizing the unlicensed and free spectrum.

WLAN Positioning Systems

Although WLAN has not been designed for localization, the radio signal can be used for location estimation by exploiting the Received Signal Strength Indicator (RSSI) values.

The scanning mechanism is a part of the 802.11 specification, and thus this functionality is readily available in the hardware device driver. Therefore, the availability of WLAN infrastructure, the ease of obtaining such measurements, the simplicity of storing and comparing numerical parameters and mainly the strong correlation between a specific location and the RSS levels from the surrounding APs, RSSI information enables location estimation.

WLAN positioning systems can follow either RSS-based triangulation or fingerprinting approaches. Indoor propagation modeling is not accurate due to the implicating indoor characteristics, degrading the performance of triangulations schemes whose performance relies mainly on the accuracy of the radio model. However, in location fingerprinting, instead of determining the distance between the user and the transmitting AP, the characterization of the signal propagation is determined by actually measuring the RSSI pattern at certain locations. This provides localization even in very complex environments, because it is not based on the signal propagation model, but on a database of real measurements.

Therefore, we focus our attention on fingerprinting localization schemes.

Fingerprinting (or scene analysis) localization includes two main phases: offline and online. The offline is a training phase during which the area is calibrated by collecting RSSI measurements from the area APs at different locations. In this way, a database which correlates positions with RSSI fingerprints and referred as Radio Map is built. The online phase is the actual location determination phase during which the unknown location of a user holding a wireless device is inferred by testing the similarity in signal space between its current RSSI measurements and the stored Radio Map entries. Fingerprinting approaches can be either deterministic or probabilistic depending mainly on (i) the selected format for representing RSSI measurements as radio fingerprints during the offline Radio Map construction and (ii) the method for testing the similarity between the current radio characteristics and the stored radio map fingerprints during the online localization phase.

In deterministic schemes, a scalar value of several RSSI sample measurements from a specific AP is stored in the Radio Map as radio information from that AP and a non-random metric is used for radio similarity check. Probabilistic schemes use probability distributions to characterize the signal behavior at each calibrated location and follow probability-based similarity tests for inferring the unknown user location. Deterministic schemes are considered simpler with less processing and storing requirements, lacking however the accuracy of probabilistic frameworks which include more detailed information regarding signal behavior.

Reviewing the literature, a plethora of fingerprinting localization schemes, either deterministic, probabilistic or combination, can be found. Providing a survey is out of the scope of this thesis. Interested readers are directed to [START_REF] Honkavirta | Location fingerprinting methods in wireless local area networks[END_REF], [START_REF] Liu | Survey of Wireless Indoor Positioning Techniques and Systems[END_REF] for related studies. Our target is to develop a fingerprint-based WLAN localization scheme which achieves a good trade-off between accuracy and simplicity objectives. To that end, we focus our attention on two representative systems: RADAR [START_REF] Bahl | RADAR: An In-Building RF-Based Location and Tracking System[END_REF] and COMPASS [START_REF] King | COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses[END_REF]. RADAR is a reference deterministic fingerprinting system which uses RSSI measurements from the existing WLAN. During the offline phase, the average of multiple RSSI measurement samples from all visible APs at each training location forms the signal vector to be stored as a radio map entry for that location. During the online phase, a pattern matching technique based on the Euclidean distance in signal space between the current RSS vector and all radio map fingerprints is followed in order to determine the most similar ones. Finally, the coordinates of the k nearest entries are averaged in order to estimate the unknown user location based on the k Nearest Neighbor (k-NN) algorithm. COMPASS, is a probabilistic scheme which stores the histogram of the RSSI samples from each AP in order to build the radio map.

Moreover, RSSI samples are collected not only for different locations but also for different orientations. During the online phase, only the radio map entries with orientation similar to that of the user are considered for comparison. The similarity between the current signal vector and the offline fingerprints is based on the Bayes' theorem such that they maximize the current RSS probability density function. Finally, the unknown location is estimated as the average of location coordinates of the k most likely radio map entries.

Our Motivation: WLAN Scene-Analysis Positioning Challenges

For scene-analysis based positioning, the scene characteristics that are selected for training the system and compared during the real-time localization process should satisfy two main objectives. Firstly, they must uniquely describe different locations and secondly their storage and processing requirements should remain low. Obviously, an inherent trade off exists between them.

The stability of the RSSI values at a specific point and their divergence for different points are two desirable factors for inferring accurately a location based on RSS information. However, inherent characteristics of the wireless medium at this band and the indoor environment cause RSS variations, fusing the system. In this section, these hindrances and their impact in the positioning performance are addressed. In parallel, we propose simple methods to compensate them without increasing considerably the processing and hardware requirements.

Multi-path and Shadowing

Multipath and shadowing are the main limiting factors for accurate indoor positioning. Multipath is related to the signal propagation losses such as reflection, scattering, diffraction, due to the existence of walls, obstacles and the indoor layout. For dealing with multipath, several radio propagation models have been proposed all of which try to incorporate the indoor characteristics, however none of them has managed to accurately model and predict the radio behavior, vital for accurate location estimation. Therefore, we choose to obtain real (empirical) RSS measurements during the offline training phase instead of assuming a theoretical model.

Shadowing is related to signal variations due to environmental changes, movement of people and rearrangement of objects. The received signal varies with respect to time and especially with respect to the relative position of the receiver and the transmitter.

Shadowing makes indoor positioning even more complicated. The signal behavior at a specific location depends also on environmental characteristics that may change randomly in time. Thus, obtaining multiple samples of RSS measurements is essential, but still not sufficient. The consistency among these samples should also be considered. In [START_REF] Youssef | The Horus WLAN location determination system[END_REF], the authors observe high autocorrelation between successive samples and propose a first order autoregressive model in order to account for this high autocorrelation. In [START_REF] Chen | Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics[END_REF], sensors are used to sense environmental changes and the system is adapted accordingly.

We target at a more simplified solution, by not considering events of short duration. We believe that even though such short-time environmental changes are random, their impact in the sample space can be detected and alleviated. For instance, consider a set of samples (for a given point and a specific AP) most of which have absolute value higher than zero and some of them are almost zero valued. It is obvious, that since signal from this AP is received most of the time, this point is within the AP range and the almost zero-valued samples are most probably due to the instantaneous blocking effect of obstacles temporally placed during the sampling process. Thus, considering these incompatible samples may lead to RSS information distortion. Especially for the static schemes, where the mean value is used for summarizing the RSS, taking into account these samples reduces the correspondence with the real strength level. Moreover, the higher the strength of the signal the worse this distortion is, which is even more undesirable since high RSS values are more indicative and significant for the location estimation process. Based on this observation, in this work the incorporation of a sample filtering module in the positioning architecture is proposed and explained in section 3.3.

Orientation-dependency of the Radio Scene

It has already been identified that the power level of the signal received from an AP at a fixed location depends on the orientation of the user due to the blocking effect of the human body. For instance, consider the case when a user's orientation is such that his WiFi-receiver has direct line of sight with an AP but for the opposite orientation his body fully prevents a signal from being received from the same AP and the same physical position. Thus, the relation between a position and RSS level is not unique (1-1), but, in contrast, different RSS values correspond to the same position. In RADAR [START_REF] Bahl | RADAR: An In-Building RF-Based Location and Tracking System[END_REF] the authors try to deal with this by sampling RSS for four different orientations and taking the maximum value as representative for this position. In COMPASS [START_REF] King | COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses[END_REF] the authors assume the existence of digital compasses during training and and run-times phases. The orientation of the user is detected and only training points with similar orientation are selected for RSS comparison during the online phase.

We claim that the orientation information should certainly be considered by the positioning system, at least during the training phase and possibly during the real-time process as well. Training points should differ regarding not only their location coordinates but also their orientation. In our proposal RSS measurements for different orientations at the same position are considered as new training points, in contrast with COMPASS where they are merged. During the online phase, we examine two cases as shown in section 3.3. In the first case, the user is not equipped with a digital compass and therefore RSS comparison is performed for all training points. In the second case, we assume the user is also equipped with a digital compass and therefore, only points with similar orientation are considered.

Location Ambiguity

Location ambiguity is a term used to describe the phenomenon when two or more positions even physically far apart are very similar in signal space. This is mainly because of the complex indoor propagation environment. Apparently, since our system is based on the similarity in RSS to infer the location, such ambiguity must be eliminated. In [START_REF] Bahl | Enhancements to the radar user location and tracking system[END_REF] considering the location history of the tracked user is proposed. Thus, if a point close in signal space is far away from the previous location of the user, it is not considered as a candidate for inferring the current position. Hardware-based solutions can provide the location system with a location approximate and thus points not within this range are excluded.

For eliminating or mitigating this phenomenon we propose giving more priority to information more sensitive to RSS variations. Since the RSS level depends more on the distance from APs rather than the specific orientation, we propose a hierarchical pattern matching approach according to which the first level patterns are only location dependent and the second level patterns include orientation information.

Operational Processing Time

In general, scene analysis methods are regarded as fast response solutions with the deterministic cases even more advantageous. However, adding the orientation-dependent information increases the number of candidates and thus jeopardizes the time efficiency factor. For dealing with this, the hierarchical pattern matching approach compensates this issue.

Proposed Enhanced Localization Approaches

In this section the architecture, the general methodology and the mechanism of the basic system components of our enhanced localization approaches are described.

General Architecture and Methodology Overview

WLAN-based location determination systems can be categorized into two classes from architectural point of view: network-based and user-based. In the former, a specific positioning component is required which measures the RSS of the mobile user devices within its range and estimates their location based on a specific positioning algorithm. In the user-based approach, each user device is responsible for measuring the RSS from the visible APs and in the sequence uses this RSS information and a positioning algorithm to infer its location. We assume a user-based architecture so no special component is required and therefore they can easily work with the available WLAN system. However, the limited resources of user devices impose the requirement for a positioning process with low processing overhead. Radio Map (RM). Note that the offline phase is conducted only once and repeated only in the case of big environmental changes which may affect the RSS characteristics of this area.

The online phase is actually the main phase of the positioning process. Initially, as in the offline phase, run-time RSSI sampling and filtering of these samples are performed. The In the following the system components are described in detail and modeled.

Joint Location and Orientation (JLO) based Calibration

During the offline phase the area under study is calibrated in the sense that a set of specific points within this area are selected and multiple RSSI sample measurements from the surrounding APs are obtained. The selection of these points is not only locationbut also orientation-based, i.e. the selected points differ in their location and orientation.

Assume and r physically distinct points within the building are selected for training the system and i : i }1,...,r| one of these with known location coordinates (x i ,y i ). Instead of ignoring the orientation at this point and simply sampling RSSI measurements from all n APs, we repeat the RSSI sampling process for eight different orientations, i.e. Cω j with j =1...8. Let

S i (ω j )=[}SS i1 (ω j )| ,...,}SS in (ω j ),...| ] (3.1)
the RSS sample vector at position (x i ,y i ) and orientation ω j , where }SS in (ω j )| the set of RSS samples from AP n .

Sample Processing

Uncontrollable environmental changes cause distortion of the RSS characteristics postulating for a simple, not hardware-dependent solution. To that aim, the incorporation of a Sample Filtering module after the RSSI sample calibration step is proposed. This additional module actually removes RSSI samples whose value is incompatible when compared with the majority of the measurement samples. More complicated filtering approaches, taking into account the frequency of the samples, add complexity without an accuracy improvement, at least for our experimental data.

Radio Map Representation and Current Orientation

Including orientation information entails differentiating the representation format of the the RM entries. This is actually the point where our approach mainly differs from COMPASS and this difference is also reflected to the online location determination process.

Let (x i ,y i , S i ) a RM entry, where S i is called radio fingerprint of position (x i ,y i ), whose format should be such that the orientation factor is accounted. In COMPASS, the format of S i depends on the current user orientation, ω u , which implies that users need to be equipped with a compass. More precisely, the RSSI samples of the similar orientations of each calibrated point (x i ,y i ) are merged to finally describe the RSS fingerprint of the corresponding RM entry i, i.e. S i = j:|ω j -ωu|<Ω S i (ω j ), where the merging operation and Ω an orientation threshold. In contrast, in our case, we do not require from users to know their orientation and construct the RM according to two possible formats,

S A i = j=1,...,8 S i (ω j ), Ci }1,...,r| (3.2)
S B i =[S i (ω 1 ), S i (ω 2 ),...,S i (ω 8 )] T , Ci }1,...,r| , (3.3)
where S i (ω j ) is given by eq. (3.1). In the first case the radio fingerprint S A i actually merges the radio fingerprints from all 8 orientations, whereas in the second case, S B i ,w e differentiate among the radio fingerprints from the 8 different orientations and consider them as 8 individual RM entries, even though their physical location is identical.

Proposed Enhanced Localization Approaches 55

Keeping the hardware requirements of users as low as possible is more desirable. However, assuming the availability of compasses during the online is also studied. In this case, the Radio Map takes the following format

S C i =[S i (ω a ), S i (ω b ),...,S i (ω d )] T , Ci }1,...,r| . (3.4)
where ω a ,ω b ,...,ω d similar orientations with the current user orientation, i.e.

ω a ω u < Ω,ω b ω u < Ω,..., ω d ω u < Ω.

Positioning Algorithm

During the online phase, the online RSS vector of a user u, denoted as S u is compared with the radio fingerprints, S i ,i }1,...,r|1 and the entries corresponding to the most similar ones are retrieved. The way for summarizing the RSS samples, }SS im (ω j )| ,a n d the similarity metric between radio fingerprints are defined by the positioning algorithm.

In general, the scene analysis positioning algorithms are categorized into two main types:

namely, deterministic and probabilistic for the static and differential cases, respectively.

Deterministic

In the deterministic approach, a single scalar, usually the mean value of the measured RSS samples is selected for summarizing them, i.e. SS im (ω j )=}SS im (ω j )| , Ci =1...,r, Cm =1 ,...,n and Cj =1 ,...8. Accordingly, the merging operation in eq. (3.2) is again the average. The metric for quantifying the similarity between them is their distance in signal space, i.e.

d ui = S u S i = l m=1 SS um SS im (3.5)
where l the number of considered APs2 , with l ≥ n and n the total number of APs in the area.

Probabilistic

In this case, the probability distribution of the sample values from each AP m is stored as the RSS information for each offline point i, i.e. SS im (ω j )=PDF}SS im (ω j )| and accordingly, the merging operation in eq. (3.2) is again the probability distribution of the

WLAN Scene Analysis Localization Chapter 3

RSSI samples for all orientations. The similarity metric is based on the Bayes' theorem, such that given S u we look for the offline points i which maximize the probability P (i/S u ),

i.e. we want

arg max i [P (i/S u )] = arg max i [P (S u /i)] (3.6)
where P (S u /i) can be calculated by combining the individual probabilities, P (SS um /i)f o r each AP m, by either multiplying (prod) or adding (sum) them, i.e.

P (S u /i):= ⎩ ⎝ ⎝ ⎝ ⎝ ⎪ ⎝ ⎝ ⎝ ⎝ ⎨ l m=1 P (SS um /i), if prod l m=1 P (SS um /i), if sum. (3.7)
Where l the number of the considered APs, with l ≥ n.

NNSS Algorithm for Location Estimation

The basic idea of the location estimation process is the Nearest Neighbor in Signal Space (NNSS) concept, since the closeness in signal information is considered. According to this, the RM entry i with minimum d ui or maximum probability P (S u /i) is selected as the NN of the user u. If more than one closest matches need to be determined we have the k-NNSS positioning algorithm, where k is a parameter defining the number of these NNs.

Two approaches for searching and finally selecting these k NNs from the available training RM entries are examined. The first one is hardware-dependent since user devices need to be equipped with a compass for determining their orientation. Based on this information the look up in the RM follows the representation (3.4). The second approach is a software-based solution. Actually, we propose a modified version of the k-NNSS positioning estimation which defines a different method for searching the NNs in the RM. Before giving the details of the proposed algorithm, we present the motivation behind it.

After the joint location-and-orientation-based calibration, an increased number of calibrated points is available, i.e. if r is the number of physically distinct points, a total number of 8 ± r radio fingerprints is available. Including them individually in the RM, see eq. (3.3), and considering all of them during the online searching phase would on the one hand increase the probability of finding a closest match in RSS but on the other hand, a large search space would degrade the time response and the resource utilization performance.

Furthermore, even though orientation is an important factor for RSS variations, the actual 2. In the second level, we consider eq. ( 3.3) as the RM representation but only for the selected m candidates, leading to a search space of 8 ± m points, and the k NNs are finally determined.

After having selected the k NNs of user u, their locations are utilized for estimating its unknown location ( x u , y u ). Since some of the selected NNs may correspond to the identical physical location, we calculate the weighted average of the unique coordinates with weight the frequency of appearance of each distinct location,

( x u , y u )= ⎞ ⎟ ⎟ ⎠ i∈Nu w i ×x i k , i∈Nu w i ×y i k ⎧ ⎜ (3.8)
where P u the set of the physically distinct NNs of user u.

System Design Considerations

In this section, the impact of two design parameters on the system performance is discussed.

Number of Access Points

An important parameter is the number of APs from which RSS is measured and considered in the final format of the radio fingerprints. In general, it holds that the more the available information, the more accurate the final decision. However, this statement is not always valid. For the deterministic approach, the similarity between fingerprints is a single scalar which contains RSS information from all APs without discriminating among them (see eq. (3.5)). Thus, if many APs are considered there is high probability that two different fingerprints will have the same distance from the user's current RSS vector, and consequently fusing the system. For the probabilistic approach, when an AP m is not visible from the current user, then training points i also non visible from this AP are biased in (eq.

3.7) due to the RSS histogram for this AP (PDF(}SS im | )=PDF(}0, 0,... =0| )=1 ) .

Finally, scanning signal entails power consumption which is undesirable. Therefore, the optimal number of APs that should be considered is a significant design parameter.

Number of Nearest Neighbors

In this section we explore the influence of the parameters m and k, of the mk-NNSS algorithm, on the system performance.

Parameter m

The parameter m defines how many closest matches are selected during the first level of the hierarchical RM look up and whose orientation-based fingerprints are considered during the second step of the search process. Obviously, higher values of m result in larger search space at the second level of the mk-NNSS and thus, the processing time and the consumed resources for performing more comparisons are increased. Its impact on the accuracy is not so obvious. A bigger search space increases the probability of finding a very similar point but also the location-based fingerprint lose their priority over the orientation-based fingerprints.

Parameter k

Parameter k defines how many NNs are finally selected for estimating the unknown location. Thus, the computational complexity is not affected by its value. However, it does affect the accuracy.

Intuitively, considering more points increases the accuracy but adding locations which are far from the real position may distort the final result. Thus, finding the optimal value is an issue. In most works which follow the k-NNSS method the value of k is fixed for all user-cases and is more related to the grid-geometry employed during the calibration phase.

However, the implicating factors of an indoor environment make each case different from the others. Therefore, making the value of k adaptive for each user, i.e. k u instead of a fixed k Cu, appears to be promising for increasing the accuracy.

Experimental Evaluation 59

For the deterministic case, the distance in RSS, d ui , would maybe give a hint for the optimal value of k u . Assuming a fixed value for k, we observed that for some users, k or more NNs had relatively small d ui but for some other users only a subset of these k NNs had relatively small d ui . Thus, adding or excluding NN s based on the relativity of their d ui value can lead to the optimal value of k u . Algorithm adaptiveN N (Du, rlt), gives the details for estimating the optimal value of k u for user u. The input D u is a vector of RSS distances between u and the RM entries, sorted in ascending order and rlt is a parameter which defines the number of these distances that should be considered for defining a relatively small distance in RSS. The operations mean and std give the mean value and the standard deviation of the first rlt smallest distances, respectively, and their purpose is to define the relatively small distance (RSdist) and the relatively small deviation (RSdev). The main idea of the algorithm is: if the absolute difference between the distance in SS of a NN i and the RSdist is smaller than the RSdev, then this NN should be considered as candidate for estimating the unknown location of user u.

Algorithm 1 [k u ]=adaptiveN N (D u ,rlt) RSdist = mean(D u (1 : rlt)) RSdev = std(D u (1 : rlt)) k u =1 for all i =1:length(D u ) do if d(u, i) RSdist < RSdev then k u = k u +1
end if end for

Experimental Evaluation

For evaluating the performance of our systems we chose to use a real experimental scenario instead of simulating one. Also, instead of performing a new experiment we preferred to utilize the already available measurement results from [START_REF] King | COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses[END_REF] in order to provide a fair comparison between our schemas and other systems. In this section, we first briefly describe the experimental testbed we utilized. After defining the performance metrics, we depict the performance improvement achieved by our proposed schemes and finally we compare them with two other positioning systems for the same experimental scenario.

Experimental Testbed and Data

The testbed we used for deploying our positioning system corresponds to an office environment of 15 ± 36 m 2 . During the offline phase, 110 RSS measurements from all 17 802.11b and 802.11g APs and responding peers were sampled for 166 distinct physically points and for each of the 8 possible orientations, leading to 1328 points in the radio map. During the online phase, 60 random points were selected and 110 RSS samples were measured from all APs. For more technical details, refer to [START_REF] King | COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses[END_REF].

Performance Metrics

A user-based positioning system is successful when it can estimate the user current location accurately, fast and without wasting the limited user-device resources. As accuracy metric we choose the Mean Location Error (MLE) between the real and estimated locations, denoted as (x u ,y u )a n d( x u , y u ), respectively, i.e.

MLE = 1 N N u=1 (x u x u ) 2 +(y u y u ) 2 (3.9)
where N the total number of users. For our data N=60.

For evaluating the time response of the system we use the Online Computation Time (OCT) metric, which is quantified by measuring the number of comparisons that need to be made during the online operational phase. It depends on the selected RM representation format and the NNSS approach. We adopt the following naming for the different tested scenarios, ≤ Merged RM is the case when orientation is not considered during the area calibration and thus (3.2) is used for the RM representation.

≤ Flat RM is the case when only eq. ( 3.3) is used, i.e. no priority given to the locationover the orientation-based fingerprints and the conventional k-NNSS is followed.

≤ Hierarchical RM is the case when both eq. (3.2) and eq. (3.3) are used and the mk-NNSS algorithm is followed.

≤ userOrient RM is the case when eq. (3.4) is adopted, i.e. a compass is assumed during the online phase also and γ is the number of RM entries with similar orientations.

Experimental Evaluation 61

Regarding their OCT we have,

OCT = ⎩ ⎝ ⎝ ⎝ ⎝ ⎝ ⎪ ⎝ ⎝ ⎝ ⎝ ⎝ ⎨ 166, if Merged RM 8 ± 166, if Flat RM 166 + μ ± 8, if Hierarchical RM γ ± 166, if userOrient RM.
(3.10)

Numerical Results

In this section, numerical results illustrate the impact of the proposed positioning enhancements and the various design parameters on the system performance. We study separately the probabilistic and deterministic cases and later a comparison among them is provided. We observe that even though for the metric prod the error reduction is high, its performance remains worse than the case of the sum metric, with or without considering the orientation during calibration. The intuition behind this is that metric sum is less dependent on the individual contribution of each AP and thus less bias from each one of them is introduced. For the rest, we consider only the case of this metric with JLO -based calibration and try to further improve it.

Probabilistic

In the sequence, we test if there is any benefit by filtering the incompatible samples. Finally, we assume the availability of digital compasses during the online phase as well and we examine if this additional information can further improve the accuracy of the system. Figure 3.2(c) shows that there is indeed improvement and the best accuracy (MLE = 1.93 meters) is achieved for threshold value Ω = 65 o , which corresponds to γ =2.

Deterministic

In Figure 3.

Experimental Evaluation

63

as well. However, regarding the Sample Filtering module, if applied, now we observe that the MLE can be further decreased for both RM representations. This is due to the limitation of the simple average to include information regarding the reliability of the samples. Overall, the best accuracy (MLE = 1.75 meters) is achieved when we perform both enhancements and the coordinates of k = 7 10 NNs are averaged.

In the sequence, we assume the availability of digital compasses during the online phase as well and we examine if this additional information can further improve the accuracy of the system. Comparing the performance of our optimum probabilistic and deterministic schemes, an interesting remark can be made. Even though deterministic approaches are considered as less accurate, we observe that the accuracy of our optimum deterministic approach is better than its probabilistic counterpart, keeping at the same time the advantage of low processing and storage requirements. For that reason, in the sequence we focus our attention on the deterministic case only and attempt to further improve its performance in terms of accuracy and time response as well.

In Figure 3.3(c) we examine the impact of the number of APs, l, that should be considered in eq. (3.5). The x axis corresponds to the number of APs. Note that it is important not only how many APs but also which APs are considered. However, considering all possible AP combinations was not trivial due to the large number of the available APs. Thus, we decided to first consider only the mode-1 APs (real APs) and then include the mode-3

APs (peers in adhoc mode), since the former were more frequently scanned. We observe that the MLE is improving as l increases until it exceeds a certain value. This justifies our claim that more information improves the accuracy but at the same time the phenomenon of aliasing becomes more possible. The minimum MLE is achieved when l = 10 APs, which is also the average number of the visible APs from each position. Thus, a general conclusion that could be made after these observations is that, for each online user case u an AP m should be included in eq. (3.5) if it is visible by this user u, i.e. SS um ∀ =0.

The main drawback of the Flat RM scheme is the large size of the resulting RM, which degrades the time response and resource utilization. The Hierarchical RM and the corresponding mk-NNSS algorithm were proposed for reducing the time for searching the candidate locations. In Figure 3.3(d) both the MLE and MLT are examined for the two cases of k, i.e. either equal to 7 or adaptive, as described in section 3.4.2. The main observation is that with the Hierarchical RM the achieved accuracy is better compared to Flat RM scheme and this justifies our motivation behind the mk-NNSS algorithm.

Additionally, comparing the two cases of the parameter k, we remark that the adaptive case decreases not only the MLE but also the time response of the system since the minimum possible MLE is achieved for lower value of the parameter m.

Comparison with other Systems

Finally, we compare the performance of the best deterministic and probabilistic schemes, with RADAR and COMPASS for the same experimental environment. For the deterministic case, optimal performance is achieved when JLO-based calibration and filtering are deployed and the hierarchical RM representation and pattern matching concepts are followed. We term this scheme as OPT-D. Additionally, we differentiate between the two cases regarding k. For the probabilistic case, the optimal performance is achieved with JLO-based calibration, the sum similarity metric and selection of RM points with similar to the user orientation. We term this scheme as OPT-P. Table 3.1 summarizes the main performance characteristics for all systems.

Regarding accuracy in terms of MLE, both OPT-D schemes are superior, with a slight increase when k value is adaptive. OPT-P is better compared to RADAR but less accurate than COMPASS. Regarding the Worst Error (WE), we achieved an impressive reduction for all of our schemes. Regarding the complexity and storage requirements for building the RM during the offline phase, deterministic approaches have less, since for each point only the mean values of the RSSI samples from all APs need to be measured and stored. On the contrary, for probabilistic schemes, the probability distributions of all RSSI sample values need to be calculated and stored.

Regarding the time response, for our deterministic schemes, we report an increase in the RM size. For the OPT-D and COMPASS the RM is less due to the selection only of γ = 2 RM entries with orientation similar to the user's.

Finally, regarding the hardware requirements, RADAR does not rely on a digital compass, our deterministic schemes require it only during the offline phase, whereas both our probabilistic proposal and COMPASS have this additional requirement during the entire positioning process. Though RFID offers promising benefits for accurate and fast tracking, there are some technology challenges that need to be addressed and overcome in order to fully exploit its potential. Admittedly, the interference problem among its components and from nonconductive materials is the main shortcoming of RFID [START_REF] Gp | Survey, Nomenclature and Comparison of Reader Anti-Collision Protocols in RFID[END_REF]. Actually, there are three main types of RFID interference. The first one is due to the concurrent responses of multiple tags to a single reader's query, the second is related to the queries of multiple readers to a single tag and finally, the third is due to the low signal power of weak tag responses compared to the stronger neighbor readers' transmissions. The first type affects the time response of the system, whereas the other two reduce the positioning accuracy. In addition, interference from non-conductive materials such as water or glass, imposes one more concern regarding the appropriateness of RFID for widespread deployment.

Chapter Summary

The main goal of this chapter is to explore the applicability of RFID for indoor location Chapter 4 sensing. To that end, we study the impact of several interference types on its performance.

Focusing on the case of determining the location of mobile terminals with reader extension by relying on a deployment of tags, we consider three RFID positioning schemes which are easily-implemented but differ in their memory and computation requirements. Mathematical models are derived for describing the main interference types and their influence on the accuracy and time response of these schemes. Finally, extensive simulation analysis is conducted for exploring the practicality and efficacy of RFID for the localization of single or multiple users under different levels of environmental harshness. Numerical results validate the potential of RFID in location sensing but also the requirement for careful design of RFID-based positioning systems.

The rest of this chapter is organized as follows: section 4.1 provides the essential background and related work for RFID localization while in section 4.2 we justify our motivation for conducting this study. In section 4.3 the conceptual framework of a RFID-based positioning system is described and section 4.4 provides its theoretical and simulation-based analysis. Finally, in section 4.5 we draw our main conclusions.

RFID Localization

This section gives an overview of the RFID technology from the localization perspective and reviews popular RFID positioning systems found in the literature.

RFID Technology Overview

RFID has a relatively long history of more than 50 years in the field of wireless communications, but only the last decade it has received a considerable attention for becoming a useful general purpose technology. Actually, RFID was initially developed as an automatic identification system consisting of three basic component types, readers, tags, and servers [START_REF] Want | An Introduction to RFID Technology[END_REF]. RFID tags are simple devices with main purposes storing their ID and transmitting it to a reader. Many types of RFID tags exist, but at the highest level, they can be divided into two classes: active and passive. Active tags require a power source such as an integrated battery. Passive tags do not need a battery to operate, they just backscatter the carrier signal received from a reader. This makes their lifetime large and cost negligible.

Readers are responsible for communicating with tags and an application. To that end, they have two interfaces. The first one is a RF interface enabling them to read the IDs of tags with their vicinity by running a simple link-layer protocol over the wireless channel. The In tag localization schemes, readers and possibly tags are deployed as reference points within the area of interest and a positioning technique is applied for estimating the location of a tag. SpotON [START_REF] Hightower | SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength[END_REF] uses RSS measurements to estimate the distance between a target tag and at least three readers and then applies trilateration on the estimated distances.

LANDMARC [START_REF] Ni | LANDMARC: Indoor Location Sensing Using Active RFID[END_REF] follows a scene analysis approach by using readers with different power levels and reference tags placed at fixed, known locations as landmarks. Readers vary their read range to perform RSS measurements for all reference tags and for the target tag.

The k nearest reference tags are then selected and their positions are averaged to estimate the location of the target tag. Wang et al. [START_REF] Wang | RFID-Based 3-D Positioning Schemes[END_REF] propose a 3-D positioning scheme which relies on a deployment of readers with different power levels on the floor and the ceiling of an indoor space and uses the Simplex optimization algorithm for estimating the location of multiple tags. LPM [START_REF] Stelzer | Concept and application of lpm -a novel 3-d local position measurement system[END_REF] uses reference tags to synchronize the readers. Then, TDoA principles and ToA measurements relative to the reference tags and the target tag are used to estimate the location of the target tag. In [START_REF] Bekkali | RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering[END_REF] RSS measurements from reference tags are collected to build a probabilistic radio map of the area and then, the Kalman filtering technique is iteratively applied to estimate the target's location.

If the target is a RFID reader, usually passive or active tags with known coordinates are deployed as reference points and their IDs are associated with their location information.

In [START_REF] Lee | Localization of mobile robot based on radio frequency identification devices[END_REF] passive tags are arranged on the floor at known locations in square pattern. The reader acquires all readable tag locations and estimates its location and orientation by Simplex active [START_REF] Wang | RFID-Based 3-D Positioning Schemes[END_REF] Reader Tags RSS proximity and optimization 0.2 -0.5 ft using weighted average method and Hough transform, respectively. Han et al. [START_REF] Han | An efficient localization scheme for a differential-driving mobile robot based on rfid system[END_REF] arrange tags in triangular pattern so that the distance in x-direction is reduced. They show that the maximum estimation error is reduced about 18% from the error in the square pattern.

Yanano et al. [START_REF] Yamano | Self-localization of mobile robots with rfid system by using support vector machine[END_REF] utilize the received signal strength to determine the reader position by using machine learning technique. In the training phase, the reader acquires the RSS from every tag in various locations in order to build a Support Vector Machine (SVM).

Since it is not possible to obtain the signal intensity from every location, they also propose a method to synthesize the RSS data from real RSS data acquired in the training phase.

When the reader enters the area, it will pass the received signal intensity vector to the SVM to determine its position. A Bayesian approach is also proposed to predict the position of a moving object [START_REF] Xu | Random sampling algorithm in rfid indoor location system[END_REF]. Having the posterior movement probability and the detected tags' locations, the reader location is determined by maximizing the posterior probability. Then, the reader position is calculated by averaging the inferred position from all tags. However, the accuracy of the algorithm depends on the movement probability model. Finally, [START_REF] Wang | RFID-Based 3-D Positioning Schemes[END_REF] proposes also a reader localization scheme by employing the Simplex optimization method. To that end, we have selected three positioning algorithms differing in their complexity level in order to investigate their behavior when multiple reader-enabled mobile nodes need to be localized simultaneously. We believe that examining this parameter is crucial for verifying the efficiency of employing RFID in general location sensing applications.

In the following we demonstrate and model the main RFID interference types and their impact on the localization performance. In addition, proposed mechanisms for dealing with each type are also outlined.

Multiple Tags-to-Reader Interference

When multiple tags are simultaneously energized by the same reader, they reflect simultaneously their respective signals back to the reader. Due to a mixture of scattered waves, the reader cannot differentiate individual IDs from the tags. This type of interference is known as multiple tags-to-reader interference or tag identification problem.

Anti-collision Algorithms

For resolving multiple tag responses an anti-collision mechanism is essential. Reviewing the literature, several anti-collision protocols have been proposed, such as time-division multiple or binary tree-based schemes [START_REF] Gp | Survey, Nomenclature and Comparison of Reader Anti-Collision Protocols in RFID[END_REF]. For instance, the EPCglobal [81], an organization that recognized the potential of RFID early, proposed bit-based Binary Tree algorithm Chapter 4

(deterministic) and Aloha-based algorithm (probabilistic). The International Standards Organization (ISO) as part of the ISO 18000 family proposed the Adaptive Protocol which is similar to the Aloha-based algorithm proposed by EPCglobal, and binary tree search algorithm. These protocols mainly differ in the number of tags that can be read per second, their power and processing requirements.

In this work, we selected the Pure and Slotted Aloha schemes [START_REF] Klair | On the energy consumption of pure and slotted aloha based rfid anti-collision protocols[END_REF] as basis for our analysis. Let G u the set of tags simultaneously energized by the reader r u . When reading starts, each tag transmits its ID irrespectively of the rest G u 1 tags. The communications from a tag to the reader is modeled as a Poisson process [START_REF] Schwartz | Telecommunication networks: protocols, modeling and analysis[END_REF]. Each tag responds on average λ times per second. The model requires independence among tag transmissions, which is supported by the lack of tag-to-tag communication capabilities. Since each tag's transmission is Poisson distributed, there is a mean delay of 1/λ between consecutive transmissions. This is referred to as the arrival delay [START_REF] Schwartz | Telecommunication networks: protocols, modeling and analysis[END_REF]. Thus, on average each tag takes 1 |Du|λ time to transmit its ID for the first time. This is referred as arrival delay [START_REF] Schwartz | Telecommunication networks: protocols, modeling and analysis[END_REF]. During collisions, colliding tags retransmits after a random time. In Aloha-based schemes, the retransmission time is divided into K time slots of equal duration s and each tag transmits its ID at random during one of the next time slots with probability 1/K. This means tags will retransmit within a period of K ± s after experiencing a collision. On average, a tag will retransmit after a duration of K+1 2 ± s = a slots. The number of collisions before a tag successfully responds is e xG A 1, where e xG A denotes the average number of retransmission attempts made before a successful identification, where G A = G u λs is the offered load and x = 1 for Pure Aloha (PA) and x = 2 for Slotted Aloha (SA). Since each collision is followed by a retransmission, the average delay before a successful response is (e xG A 1)a, followed by a single successful transmission of duration s. In total, the average delay a tag takes to transmit its ID successfully is t TR =(e xG A 1)as + s + 1 |Du|λ . For non-saturated case, i.e. tags to be detected are less than the maximum number of tags that can be read per inventory round, the total time needed for reading successfully G u tags follows the linear model

T TR = G u ± t TR = G u ± s 1+(e xG A 1)a + 1 G u λ . (4.1)

Multiple Readers-to-Tag Interference

Multiple readers-to-tag interference occurs when a tag is located at the intersection of two or more readers' interrogation range and the readers attempt to communicate with

R i + R j > d ij (4.2)
and r i and r j communicate at the same time, they will collide and the tags in the common area will not be detected.

Reader Collision Probability

The probability P C ij of such collision type between readers r i and r j , if equation (4.2) is satisfied, depends on the probabilities r i and r j are simultaneously trying to communicate with their common tag. For characterizing the probability of simultaneous reader communication, we assume that each reader is in a scanning mode with probability p scan . Thus, P C ij depends on the probabilities r i and r j are in a scanning mode, p scan i and p scan j , respectively, i.e.

P C ij = p scan i ± p scan j . (4.3)
A mechanism coordinating reader transmissions as the one proposed in [START_REF] Papapostolou | Exploiting multi-modality and diversity for localization enhancement: WiFi & RFID usecase[END_REF] can compensate this type of interference.

Reader-to-Reader Interference

Reader-to-reader interference is induced when a signal from one reader reaches other readers. This can happen even if there is no intersection among reader interrogation ranges (R i + R j <d ij ) but because a neighbor reader's strong signal interferes with the weak reflected signal from a tag. Figure 4.1(b) demonstrates an example of collision from reader r 2 to reader r 1 when the latter tries to retrieve data from tag t 1 . Generally, signal strength of a reader is superior to that of a tag and therefore if the frequency channel occupied by r 2 is the same as that between t 1 and r 1 , r 1 is no longer able to listen to t 1 's response.

Read Range Reduction

Reader-to-reader interference affects the read range parameter. In equation (4.8) this factor had been neglected. However, when interfering readers exist, the actual interrogation range of the desired reader decreases to a circular region with radius R I max , which can be represented by

R I max = arg max d∈[0,Rmax] SIR(d) ≈ TH, (4.4)
where

SIR(d)= P s (d) i I i (4.5)
and I i the interference from reader r i .

The Class 1 Gen 2 Ultra High Frequency (UHF) standard ratified by EPCGlobal [81],

separates the readers' from tags' transmissions spectrally such that tags collide only with tags and readers collide only with readers.

Interference from Nonconductive Materials

Since RFID technology uses electromagnetic waves for communication, interference from specific materials such as glass or water is unavoidable. This prevents tags being detected from a reader even though they are located with its zone. For incorporating this characteristic in the model, each reference tag t is assigned a probability p t of not being detected.

Obviously high values of p t are assigned to tags which are mounted to such interfering materials.

Positioning Framework

In this section, we initially model a RFID system and the communication principles among its components and later we provide the architecture and processing details of the positioning schemes we consider.

RFID System and Communication Model

We model an indoor environment as a 2-D area with L and W denoting its length and width respectively. A set U , of passive RFID tags with known coordinates (x t ,y t ), Ct U are placed on the floor of this area such that a grid of reference tags is formed with intertag spacing δ. Within this area, we consider a set ⌈ of users with RFID reader-enabled terminals which are randomly located and an accurate and fast estimation of their position ( x u , y u ), Cu ⌈ should be obtained.

The communication between a reader and a passive tag is done using either magnetic or electromagnetic coupling. Coupling is the transfer of energy from one medium to another medium, and tags use it to obtain power from the reader to transfer data. There are two main types of coupling, inductive and backscatter, depending on whether the tags are operating in the near-field or far-field of the interrogator, respectively. A key difference between them is that far-field communication has a longer read range compared to near field communication. RFID systems operate in the Industry, Scientific and Medical (ISM) frequency band that ranges from 100 KHz to 5.8 GHz but they are further subdivided into four categories according to their operating frequency: Low Frequency (LF), High Frequency (HF), Ultra-High Frequency (UHF) and Microwave. Tags operating at UHF and microwave frequencies use far-field and couple with the interrogator using backscatter.

Recently, ultra-high frequency (UHF) passive RFID systems have received a great deal of attention and thus, we focus our research interest on these tag types.

The communication link between the main RFID components is half duplex, reader to tag and then tag to reader. In the forward link, the reader's transmitting antenna (transmitter) sends a modulated carrier to tags to power them up. In the return link, each tag receives the carrier for power supply and backscatters by changing the reflection coefficients of the antenna. In such a way, its ID is sent to the reader's receiving antenna (receiver). The path loss of this two way link may be expressed as

PL(d)=PL o +10N log d d o + X σ , (4.6)
where d the distance between the reader and a tag, PL o the path loss at reference distance

d o given by PL o = G t G r (g t Γg r ) λ 4πdo N
and G t , g t ,a n dG r , g r are the gains of the reader and tag transmit and receive antennas, respectively. Γ is a reflection coefficient of the tag and λ the wavelength. N =2 n, where n the path loss component of the one way link.

The path loss model defines the received power RSS(d) at the receiver given the transmit power P t of the transmitter, i.e.

RSS(d)=P t PL(d).

(4.7)

In the absence of interference, the maximum read range a reader receiver can decode the backscattered signal is such that

R max = arg max d≥0 RSS(d) ≈ TH, (4.8)
where TH represents a threshold value for successful decoding.

Ideally, it is assumed that the signal transmission from each reader forms a circle with radius R max if omnidirectional antennas are considered. However, in practice this is not real due to different signal gains at different directions. To quantify this problem a Degree of Irregularity (DoI) has been proposed in [START_REF] Wang | RFID-Based 3-D Positioning Schemes[END_REF], according to which if R u and R l the maximum and minimum values of a reader transmission range, then DoI is the maximum variation of the reader's transmission range per unit degree change. It is worthy mentioning that the proposed architecture may not always be the optimal choice. For example, if the wireless medium between users and the Location Server (LS) is not robust enough for exchanging messages successfully, a user-based approach would be more efficient. In this case, when a new user enters the indoor area it can receive information regarding the tag deployment automatically or after having subscribed to a relevant service.

Positioning System Architecture

Then, by following a positioning algorithm, it can estimate its own location. However, in such approach, greater attention should be given regarding the complexity of the positioning algorithm since mobile terminals have limited resources compared to servers.

Positioning Algorithms

A positioning algorithm defines the method of processing the available information in order to estimate the target's location. The main metrics for evaluating its performance are its accuracy, memory requirements and complexity. In this chapter, we study three positioning algorithms which can be easily implemented in the sense that they do not require any special hardware, but differ in their complexity and memory requirements.

Let G u denote the set of reference tags successfully detected from a user's reader r u and SS u a vector of the corresponding RSS measurements such that the entry RSS t is the RSS from the tag t G u to r u .

Simple Average (S-AVG)

This algorithm is based on the assumption that the reader radiation pattern forms a perfect circle. Thus, the user's location is estimated as the simple average of the coordinates Chapter 4

(x t ,y t )o fa l lt a g st G u , i.e. ( x u , y u )= t∈Du x t G u , t∈Du y t G u (4.9)
This scheme has the minimum memory requirements since only the ID information from the detected reference tags is used for estimating the unknown location. Regarding its processing requirements, it involves 2 ±G u additions of the coordinates of the detected tags and two divisions. Therefore, it has linear complexity O( G u ).

Weighted Average (W-AVG)

Since some of the detected tags may be closer than others, biasing the simple averaging method is proposed as an alternative approach. This can be achieved by assigning a weight w t to the coordinates of each tag t G u . These weights are based on their RRS from the reader. Thus, (4.9) becomes

( x u , y u )= t∈Du w t ×x t t∈Du w t , t∈Du w t ×y t t∈Du w t (4.10)
where w t =1/RSS t and RSS t the measured RSS value from tag t.

This scheme requires more memory than the S-AVG, since RSS information is used in addition to tags' IDs for estimating the unknown location. Regarding its processing requirements, it involves 4±G u addition, 2±G u multiplication and 2 division operations.

Thus, its complexity remains linear, i.e. O( G u ).

Multi-Lateration (ML)

Finally, we investigate a multi-lateration based approach which tries to take into account the imperfection of the readers' radiation pattern. The distances from all detected tags G u are first estimated and then (x u ,y u ) can be obtained by solving the following system of

G u equations (x 1 x u ) 2 +(y 1 y u ) 2 = d 2 1 . . . (x |Du| x u ) 2 +(y |Du| y u ) 2 = d 2 |Du| (4.11)

Simulation-based Performance Analysis 79

The above system of equations is not linear. According to [START_REF] Caffery | A new approach to the geometry of toa location[END_REF] it can be linearized by subtracting the last equation from the first G u 1 equations. The resulting system of linear equations is then given by the following matrix form

A[x u ,y u ] T = b, (4.12)
where

A := ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ 2(x t x 1 )2 ( y t y 1 ) .. .. .. 2(x t x |Du| )2 ( y t y |Du| ) ⎧ ⎜ , b := ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ x 2 1 x 2 |Du| + y 2 1 y 2 |Du| + d 2 1 d 2 |Du| . . . x 2 |Du|-1 x 2 |Du| + y 2 |Du|-1 y 2 |Du| + d 2 |Du|-1 d 2 |Du| ⎧ ⎜ . (4.13)
Since d t are not accurate, the above system of equations can be solved by a standard Least Squares (LS) approach [START_REF] Caffery | A new approach to the geometry of toa location[END_REF] as:

[ x u , y u ] T =(A T A) -1 A T b (4.14)
with the assumption that A T A is nonsingular and G u ≈ 3, i.e. at least three tags are detected. This scheme has similar memory requirements with the W-AVG. However, it has polynomial complexity O( G u 3 ) and it involves complex matrix operations such as creating an inverse matrix.

Simulation-based Performance Analysis

In this section we analyze and evaluate the performance of the studied localization schemes through simulations, using Matlab [85] as the simulation tool. Firstly, the simulation settings are specified, then the performance objectives are defined and finally, numerical results demonstrate the behavior of RFID in localization for different system design and environment characteristics. The simulation environment is a rectangular area 50 ± 50m 2 where reference tags are placed in a grid fashion with inter-tag spacing δ. Within this area a set of users ⌈ with reader-enabled terminals are randomly located. For the UHF RFID path loss model in eq.

Simulation Specifications

(4.6) operating frequency is 915 MHz, N =3.6a n dσ 2 [2,6].

Regarding the parameters of the Aloha anti-collision protocols we have set the rate of each tag's initial response λ = 30, the retransmission time is divided in K = 5 slots and each slot duration is s = 96 106 =0 .90ms which corresponds to the time needed for transmitting an ID of length 96 bits over a link with data rate 106 kbps. We assume that the probability p scan u a user u's reader r u queries for tag IDs follows uniform distribution U (β, 1), where β [0, 1] reflects the degree of multiple-readers-totag and reader-to-reader interference types. Indeed, when β = 1 all readers communicate simultaneously resulting in high level of interference, whereas when β = 0 the problems are less intense. Finally, the propagation delay T prop during message transmission is less than or equal to 1μs for the IEEE 802.11.

Performance Objectives

In general, the main performance objectives a positioning scheme should satisfy are high accuracy and fast time response. Thus, we define the Mean Location Error (MLE)

and Mean Localization Time (MLT) metrics for evaluating both objectives.

MLE is measured as the Euclidean distance between the actual and the estimated positions for all ⌈ users, i.e.

MLE = 1 ⌈ |U | u=1 (x u x u ) 2 +(y u y u ) 2 . ( 4
The collision delay depends on the anti-collision protocol. For instance, for the IEEE 802.11b Carrier Sense Multiple Access (CSMA) mechanism, the mean collision delay is given by

T col = CW max CW min 2 ± t s =10ms. ( 4
.17)

Numerical Results

Numerical results based on the average of 1000 independent simulation executions are presented in the following. We first focus on the single-user case and study the impact of several system design and environmental parameters on the positioning performance. In the sequence, we consider the case of multiple co-located users in order to manifest the accuracy degradation due to the several interference types. Finally, we show that this performance degradation can be compensated if the interference problem is alleviated or solved.

Single-User Case

The principal parameters related to the design of the proposed RFID-based positioning system are the inter-tag spacing δ of the reference tags, the maximum read range R max of the readers, the positioning algorithm (S-AVG, W-AVG or ML) and the anti-collision algorithm (PA or SA).

The environmental parameters characterize the severity of the indoor space. For our model, the main such tuning parameters are the shadowing variance σ in eq. (4.6) and the parameter α of the Uniform probability distribution function U (a, 1) followed by p t , Ct U . The main observation is that for all cases, increasing the inter-tag spacing δ reduces the positioning accuracy, which is quite rational since less tags are detected by each reader. Comparing the three positioning algorithms, we remark that considering the RSS information and increasing the processing complexity results in better accuracy, especially when R max =5 m. Regarding the two cases of the maximum read range, we observe that for δ ≥ 2m both of them achieve low MLE less than 0.5m. However, for δ ≈ 3m when R max =3m the accuracy reduction is much higher. This is because fewer tags are detected when the read range is reduced. On the other hand, when R max =5 m achieving high accuracy does not require a dense tag deployment (δ ≈ 4m), especially when the W-AVG or ML techniques are followed. whereas when R max is higher more tags are detected. The MLT versus the inter-tag spacing δ for both anti-collision algorithms when R max =3 m and R max =5 m is depicted in In general, the accuracy of indoor wireless positioning depends also on the characteristics of the environment. In Figure 4.6 we examine the impact of the shadowing variance σ and the interference level α from materials on the MLE for the three positioning algorithms.

Regarding the shadowing level in Figure 4.6(a), we observe that the S-AVG and W-AVG positioning algorithms exhibit tolerance regardless the increase of σ, whereas the performance of the ML technique is greatly degraded. This is because ML's accuracy depends highly on the accuracy of the path loss model which is used for estimating the distance from each detected tag. On the other hand, the location coordinates of the detected tags are the principal factors for the S-AVG and W-AVG algorithms.

Multi-User Case

So far, we were considering only one user being randomly located in the indoor space and we were exploring the performance of RFID positioning. In the following, we consider the case of multiple co-located users and we repeat similar performance tests in order to manifest the accuracy reduction caused due to their interference. However, compared to the single-user case, now there is a noticeable accuracy decay which demonstrates the impairing impact of the interference problem in RFID. Furthermore, while in the single-user case R max =5m was providing higher accuracy, in this case setting R max =3 m is more beneficent. This is obviously due to the higher probability of overlap among several read zones. Besides these observations, an interesting conclusion that can be made is that by adjusting the reader's range through a power control or another mechanism can alleviate the problem. However, this comes with higher complexity cost. In the following we focus on the main parameters which affect the level of interference, i.e. the number of users ⌈ , the read range R max and the scanning probability of their reader which is modelled by the parameter β.

In figures 4.9(a) and 4.9(b) we show the impact of increasing the number of users ⌈ when β = 1 and β = 0, respectively. Obviously, the MLE increases with the users' population expansion. The remarkable notice, however, is that for β = 0 the accuracy reduction is less. Therefore, if a mechanism for coordinating reader transmissions is designed, the accuracy degradation due to the RFID interference problem can be compensated. In figures 4.10(a) and 4.10(b) we show the impact of the reader range R max when β =1 and β = 0, respectively for ⌈ = 40 users. As expected, as R max grows the MLE increases due to the higher probability of overlap among readers' interrogation zones. However, the interference intensity can be greatly alleviated if readers' transmissions are coordinated.

Finally in Table 4.3 we summarize the main advantages and disadvantages of the system design parameters regarding their accuracy, time response, complexity and behavior under different environmental situations.

Chapter Summary

The growing popularity of the RFID technology and the increasing demand for intelligent location-aware services in indoor spaces motivated exploring its potential for providing accurate and time efficient localization with low deployment cost. However, despite the great benefits RFID can offer, the interference among its components and some materials are its main limiting factors. Therefore the impact of the RFID interference problem on the positioning performance should be extensively studied before the deployment of RFID-assisted location systems.

In this chapter, this issue was mainly addressed. After modeling the interference problem in RFID by considering its technology and communication specifications, we conducted extensive simulations for analyzing the performance of RFID in tracking single or multiple users, under different system configurations and environmental conditions. Numerical results encourage adopting RFID for localization but also indicate the essentiality of a careful system design in order to exploit its full potential, especially for highly populated environments.

Chapter 5

Integrating WLAN and RFID for Localization Enhancement I n the previous two chapters, we explored the applicability of WLAN and RFID technologies separately for indoor location sensing and identified their main strengths but also shortcomings. In summary, WLAN-based localizers are easily deployed but suffer from limited accuracy, whereas RFID-based solutions are very accurate for tracking a single user but are not efficient in populated environments due to the interference among users.

In this chapter, we consider a heterogeneous WiFi and RFID wireless network and propose utilizing both technologies for determining the location of multiple users with multi-modal terminals but with diverse capabilities. Multi-modality is related to the double interface of user devices, whereas diversity is related to the RFID component type, either reader or tag, each device supports. This scenario is entirely plausible in future heterogeneous wireless networks, whereby users will demand ubiquitous access to different applications from any available network [START_REF]Internet of things[END_REF]. Under this realistic scenario, we first propose taking advantage of the WLAN infrastructure for providing coordination among readers communication, in order to compensate the reader collision problem. Moreover, we propose exploiting the capability of reader-enabled users to sense neighbor tag-enabled users. Initially, reader-enabled users can use both the WLAN infrastructure and the RFID tag deployment for their localization, whereas the location of tag-enabled users can rely only on the WLAN system. Since RFID reader positioning schemes are more accurate than WLAN-based schemes, the proposed synergetic mechanism among users targets at increasing the WLAN positioning accuracy.

Integrating WLAN and RFID for Localization Enhancement Chapter 5

The remaining of this chapter is organized as follows. Section 5.1 explains our motivation for investigating the potential of a hybrid positioning system, which is described in section 5.2 and evaluated in section 5.3. Finally, section 5.4 summarizes this chapter.

Our Motivation: Need for Technology Integration for Localization Improvement

Our motivation for proposing a hybrid localization scheme stems from the need for synergy between the two technologies in order to take advantage of the benefits and overcome the limitations of the stand-alone solutions. In the following, we review the strengths and shortcomings of both WLAN and RFID positioning systems and explain the synergetic concepts we propose.

Review of Stand-alone Solutions

WLAN-based indoor location systems are attractive, mainly for their popularity in most indoor environments, the availability of RSS measurements during the scanning process of the handoff mechanism, their low cost and ease of deployment. However, there is a lower bound in their optimum achieved accuracy [START_REF] Ladd | Robotics-based location sensing using wireless ethernet[END_REF]. Since the main target of a WLAN is the communication between its components, the placement of the APs is such that minimum overlapping is achieved. Moreover, the inherent characteristics of the wireless medium, the so called propagation losses, the complicated in-building layout, the dependency on the receiver orientation, the Line Of Sight (LOS) requirement, and uncontrollable environmental changes cause undesirable signal variations, hence deteriorating the positioning process.

On the other hand, RFID is considered as a promising technology for indoor location sensing due to the low cost of passive tags, the non-LOS requirement, the fast reading of multiple tags and the reduced sensitivity regarding user orientation. However, when more than one reader need to be tracked, RFID technology suffers from the so-called reader collision problem [START_REF] Engels | WHITE PAPER -The Reader Collision Problem[END_REF]. The interference problem is more intense in RFID compared to other wireless technologies due to the inability of readers to communicate with each other, the lack of an infrastructure and the limited capabilities of the passive tags. Therefore, applying any of the general multiple access mechanisms, based on time, frequency, code division or carrier sensing, directly in RFID is not trivial [START_REF] Gp | Survey, Nomenclature and Comparison of Reader Anti-Collision Protocols in RFID[END_REF].

Positioning Framework 93

Synergetic Concepts

Since WiFi-enabled devices are very widespread nowadays and passive tags are cheaper than readers, we consider a realistic scenario whereby all user devices are WiFi-enabled, many of them have a passive tag and less an RFID reader. In the following, we explain our proposed synergetic concepts, termed as mutli-modality and diversity.

Multi-modality

Multi-modality is related to the double interface of user devices, so that communication with both the WLAN and RFID interfaces is enabled. Since readers and tags are attached to terminals which also support WiFi communication, we suggest taking advantage of the redundancy of communication channels offered by the multi-modality of user devices in order to compensate the RFID reader collision problem. More precisely, we study the potential benefits if the WLAN infrastructure is exploited for providing coordination among readers' communication. We first model a conceptual coordinating mechanism and then design a realistic scheduling mechanism in order to validate the performance advantages of this synergetic concept.

Diversity

Diversity is related to the RFID component type, either reader or tag, that is additionally available at each WiFi-enabled user device. We suggest benefitting from the diversity among the two types of terminals, by taking advantage of the capability a reader-enabled user to detect a nearby tag-enabled user, due to the build-in feature of its reader to detect tags. Since RFID reader localization schemes are more accurate than WLAN-based schemes, the position of the latter can be updated based on the proximity technique with reference point the former. In the following, we term reader-enabled users as active because they participate actively in the positioning process, whereas the tag-enabled users as passive.

Positioning Framework

In this section the overall framework of the positioning system is described.

Integrating WLAN and RFID for Localization Enhancement Chapter 5 Initially, the localization of both active and passive users can rely on the WLAN system by utilizing the RRS measurements. However, active users can also take advantage of the reference tag deployment.

System Architecture

The reader of each active user scans for tags within its coverage in order to retrieve their IDs. We assume that each reader's interrogations follow a transmission schedule provided from the WLAN infrastructure. In order to model this, we assume that an active user u's reader queries for tag IDs with some probability p scan u which follows uniform distribution U (β, 1), where β [0, 1] reflects the synchronization degree. Indeed, β = 1 corresponds to the case when all readers communicate simultaneously, whereas when β = 0 a conceptual access control mechanism is assumed.

The list of these retrieved IDs, called Tag List, is then forwarded to the location server.

Based on this received Tag List and the database which correlates the IDs of the reference tag with their location coordinates, the LS estimates the location of that user by employing an RFID-based positioning algorithm and finally returns this location estimate back to the corresponding user. The positioning algorithm can be any of the Simple Average (S-AVG),

Weighted Average (W-AVG) or Multi-Lateration (ML) algorithms that were described in section 4.3.3.

The retrieved IDs in the Tag List of an active user, may correspond not only to reference tag IDs, but also to passive users' IDs which are possibly located within the reader's range of that active user. Therefore, if the ID of a passive user is included in the Tag List from at least one active user, its initial estimated location can be improved. We selected as estimated position of the passive user to be the estimated location of the active user who has most recently detected him. More advanced positioning techniques can also be applied. However, we preferred to keep the complexity of this scheme low in order to focus on the advantages offered by the proposed synergetic concept even by employing a simple proximity algorithm. Finally, this location update is sent to the passive user.

Realistic Positioning Process

In the previous section, a readers' coordinated transmission mechanism was theoretically modeled. In the following, we design a scheduling mechanism for readers' transmissions that can be implemented with the aid of the WLAN infrastructure and we describe again the entire positioning process by employing it instead of assuming a theoretical one. for some users are improved with the aid of the steps (ii)-(v). In the following, we provide more details of our proposed scheme.

Initial location estimation

Initially, each user, passive or active utilizes its RSSI measurements from all visible APs to estimate its location based on the RSS-based triangulation technique, if there is coverage from at least three APs. Otherwise, proximity and non-proximity constraints are applied for roughly estimating its location. At the same time, the reader of each active user u interrogates tags within its coverage, whose retrieved IDs may correspond to both area reference tags and passive user tags. A list of these retrieved tag IDs, denoted as G u ,i s forwarded to the LS within a Tag List message through a WiFi communication channel.

After the completion of the above initial location estimation task during the Initial Phase,aCollision Compensation Phase (CCP) is arranged to start in order to deal with the positioning performance degradation due to readers' collisions but also to improve the WLAN-based positioning accuracy.

Collision diagnosis

Firstly, the LS performs a Collision Diagnosis (CD) test for each active user u based on the received G u and a threshold TH CD . Algorithm 2 describes the pseudo-code of this test. N contains all sets G u from each user u and TH CD is a threshold whose value is discussed in Section 5.3. Initially, the tag IDs which correspond to reference tags (TU u ) are distinguished from these belonging to detected passive users (R⌈ u ). According to the collision test, an active user u is considered as suffering from collision if the number of its detected reference tags TU u is less than TH CD and classified into the group of colliding users L 1 . Otherwise, it is allocated in group L 2 . The idea behind this classification is to diagnose the severity degree of each collision and take action only if necessary with respect to localization performance (not enough number of detected reference tags for accurate location estimation). In the sequence, the LS estimates the location of all users within both L 1 and L 2 based on S-AVG, W-AVG or ML positioning algorithm. The locations of passive users are updated by the locations of the L 2 active users who have detected them.

For L 2 and their detected passive users, their calculated locations are considered as final location estimations and are forwarded to them. However, for L 1 active users, they are considered as coarse initial location estimations to be used by the next clustering step.

Algorithm 2 [L 1 , L 2 , X, Y]=CollisionDiagnoses(N, TH CD ) for all G u N do [TU u , R⌈ u ] ∈ assort(G u ) if TU u < TH CD then L 1 ∈L 1 + u else L 2 ∈L 2 + u end if ( x u , y u ) ∈ based on SA, WA or ML. ( x p , y p ) ∈ ( x u , y u ), Cp R⌈ u : u L 2 end for
Integrating WLAN and RFID for Localization Enhancement Chapter 5

Clustering

We propose clustering active users within group L 1 (whose readers suffer from collisions) into disjoint sets of inter-colliding nodes based on their proximity. The idea behind this is to group together neighbor nodes, since their readers are most probably colliding with each other. Subsequently, only readers' transmissions belonging to nodes within the same cluster need to be coordinated. For performing this clustering task, the LS uses the initial coarse location estimations of all users in L 1 to compute the Euclidean distance between all pairs of these nodes. The readers of two users u i and u j are expected to collide if their estimated distance d ij is smaller than a distance threshold TH dist . Therefore, the clusters should be created such that the distance between any pair of nodes which belong to disjoint clusters is certainly greater than the TH dist threshold. Thus, for two disjoint clusters C x and C y with x ∀ = y, it holds

d ij >TH dist , Ci, j : i C x ,j C y . (5.1)
The value of the threshold TH dist is also design parameter. Increasing its value creates less but more populated clusters.

Readers' transmission coordination

In the sequence, the WLAN is responsible for providing a coordination among readers' transmissions which belong to users within the same cluster such that their collisions are compensated. To that end, information regarding all produced clusters and the L 1 users is forwarded from the LS to their corresponding AP. The transmission schedule for each cluster is arranged in a Round Robin (RR) fashion, such that readers within the same cluster are scheduled to transmit one after the other1 . The process is as follows:

A BEACON message is first broadcasted to notify and prepare all users for the start up of this phase. Users within G 2 cease their readers interrogating tags, whereas a POLL message is sent to each user within each cluster in order to be notified for its allocated time slot, during which its reader is allowed to scan for tags. Note that, users within different clusters may be polled simultaneously since their readers are considered as non-colliding according to the clustering step. This actually explains better the idea behind clustering nodes, that is reducing the time duration of the scheduled transmissions.

Location refinement

Upon reception of the transmission schedule from its associated AP, the reader of each corresponding active user scans for tags according to its allocated time slot. The list of the detected tags is then send to the LS which then estimates the location of that user and of its detected passive users (if any) and transmits it back to them.

Performance Analysis

In this section we analyze and evaluate the performance of the proposed schemes through simulations, using Matlab [85] as simulation tool. Firstly, the simulation settings are specified, then the performance objectives are defined and finally, numerical results demonstrate the efficiency of both systems in providing accurate and time-efficient localization. This path loss model is used for calculating the RSS from each AP, based on its transmit power P t , i.e. RSS(d)=P t PL(d). For our case, the operating frequency is 2.4 GHz, n =3.5a n dσ 2 1 =3.5. In addition to the WLAN system, reference tags are placed in a grid fashion with intertag spacing δ. For the UHF RFID path loss model (see eq. assume that a tag is not detected with some probability which follows uniform distribution U (0, 1). Within this area N A active users and N P passive users are randomly located. R max denotes the maximum read range of each active user's reader which depends on the transmit power, antenna gains, propagation losses, interference and shadowing. To incorporate these factors, we assume that during simulations, the actual range is R max = R max • r, where r [0,r max ].

Simulation Setup

Performance Objectives

For system evaluation we focus on the achieved accuracy and response time since these are the main performance objectives for a positioning scheme.

Localization Accuracy

As accuracy metric we use the Mean Location Error (MLE), measured as the Euclidean distance between the actual and the estimated positions for all N A active or N P passive users, i.e.

MLE = 1 N N u=1 (x u x u ) 2 +(y u y u ) 2 , (5.3)
where N = N A or N P .

Performance Analysis 101

Response Time

As metric for the response time we define the Mean Localization Time (MLT) given by

MLT = T IP +T CCP , (5.4)
where T IP and T CCP the duration of the initial and collision compensation phases, respectively.

During the initial phase, both passive and active users estimate their location based on the WLAN and in parallel, the reader of each active user scans for tags. Therefore, the T IP is given by

T IP = max 1 N A +N P N A +N P u=1 T W LAN,u , 1 N A N A u=1 T TR,u ⎛ , (5.5)
where T W LAN,u is the duration of the WLAN-based positioning process of user u and T TR,u

is the tag reading time by active user u's reader, given by eq. ( 4.1).

The duration of the collision compensation phase T CCP should be actually evaluated, since this contributes to additional delay overhead over the normal positioning process. It is given by

T CCP =T CD +T Cl +T Sch +T LR (5.6)
where T CD , T Cl , T Sch and T LR the duration of the collision diagnosis test, clustering, scheduling and location refinement steps, respectively. From these factors, the prevailing one is T LR , which depends on the cardinality of the bigger collision cluster D max , and the tag reading time T TR,u of all nodes within D max , i.e.

T LR = u∈Cmax T TR,u .

(5.7)

Therefore, MLT is given by

MLT = max 1 N A + N P N A +N P u=1 T W LAN,u , 1 N A N A u=1 T TR,u ⎛ T IP + u∈Cmax T TR,u ≈T CCP .
(5.8)

Numerical Investigations

Numerical results based on the average of 1000 independent simulation executions are presented in the following. We first validate the accuracy improvement offered by the proposed hybrid scheme when a conceptual mechanism for synchronizing readers transmissions is assumed and later we evaluate the performance of the proposed scheduling mechanism, in terms of localization accuracy and response time. meters for all readers and N P = 50. We observe that when all readers query for tags simultaneously (No Synchronization), increasing their population results in performance degradation for active users, whereas the accuracy improves for passive users. This is reasonable since in highly populated environments the collision problem becomes more intense, but concurrently, more passive users are detected by active users for refining their initial location estimations. However, when the collision problem is mediated by coordinating readers' transmissions (Synchronization), both user types benefit. Indeed, the MLE for active users remains almost stable even for highly populated environments and the MLE reduction rate for passive users is higher when synchronization is employed. to be detected. However, when synchronization comes into place, increasing the read range does not affect considerably the MLE of active users since collisions are avoided. For passive users we observe that for almost all values of R max when synchronization among readers' queries is realized, the MLE is slightly worse. This happens because passive users may not be detected even if they are located close to a reader, if that reader is not in a scanning mode. However, for R max ≈ 4.5 meters the MLE is less when synchronization is on. This is due to the corresponding detrimental performance of active users. An interesting remark is that for R max = 5 meters and controlled reader transmissions, the MLE is less than 0.5 for active users and the optimum possible for passive users.

Conceptual Positioning System

The above results validate the accuracy improvement if the RFID reading process is coordinated and users benefit from their diversity by cooperating. In the following, we evaluate the performance of our proposed positioning system when the realistic CCP scheduling mechanism is employed, regarding both accuracy and accompanied time delays.

Realistic Positioning System

Without loss of generality, in the rest we assume the Simple Average as the principle positioning method, mainly for its simplicity and independence on the RFID path loss model. We first explore the impact of the TH CD and TH dist design parameters on the MLE TH max CD is its maximum value and TH opt CD is the one we selected as optimal for achieving a good trade off between accuracy and time objectives. The expected number of responding tags depends on the geometry of the tag deployment and the reader's range. Considering a grid tag deployment and that the reader's radiation pattern forms a circle, as depicted in Figure 5.8, the expected number of detected tags G is given by

G =4 R max δ 2 .
(5.9)

Setting TH max CD = G tags, a single tag miss is interpreted as indication of severe collision and thus, almost all active users are included in the scheduled transmission stage. Apparently, the achieved accuracy is optimum but with the cost of high increase in MLT.

For satisfying both objectives, a smaller value should be selected. Considering the grid geometry of the reference tags setting TH opt CD = 4 5 tags seems a rational choice. As TH dist increases, less but more populated clusters are generated. Apparently, overestimated TH dist results in time resource waste since active users whose readers are In Figure 5.9 we study the optimal value of the reader range R max with respect to both accuracy and time performance objectives. The three curves correspond to three positioning system cases:

1. CCP is not included, i.e. Initial Phase only.

CCP is included with TH

CD =TH max CD = G tags.
3. CCP is included with TH CD =TH opt CD = 5 tags.

Note than now the clustering threshold is given by: TH dist =2± (R max +2.5), based on our analysis in eq. (5.10). In Figure 5.9(a) the MLE increase as R max increases is due to the higher probability of overlap among interrogation zones since the reader ranges expand. However, employing the CCP mechanism, the accuracy reduction is compensated.

Comparing the two cases of TH CD , we observe that the accompanied time overhead in Integrating WLAN and RFID for Localization Enhancement Chapter 5 In the following, the advantage of exploiting users' diversity is validated. Figure 5.10

shows the impact of increasing the population of active users on the MLE for both user types. For all schemes, increasing readers' population results in accuracy degradation for active users (Figure 5.10(a)), but in accuracy improvement for passive users (Figure 5.10(b)), similarly with our observations in Figure 5.5. Moreover, the benefits by employing the CCP mechanism (for both TH CD cases) for both user types are in accordance with these when the coordination mechanism was conceptually modeled. more users are allocated to L 1 group after the CD test.

Chapter Summary

In this chapter we proposed a localization scheme based on the integration of the WLAN and RFID technologies in order to overcome the limitations of the stand-alone positioning solutions. The main advantage of multi-modal devices is the offered redundancy of communication channels. Thus, we suggested utilizing the WiFi channel for coordinating the communication in the RFID channel in order to subdue the restricting factor of reader collisions. Additionally, we considered a realistic scenario where users have devices with diverse capabilities. We proposed exploiting this diversity by benefitting from the inherent property of reader-enabled users to sense tag-enabled users in their vicinity, and hence refining their location estimation. The performance advantages of a conceptual coordination mechanism were first validated and later an easily implemented scheduling scheme was proposed for realizing these benefits. Based on extensive simulations, we tested the impact of various design parameters and we validated the achieved performance advantages of our proposals regarding both accuracy and time-efficiency objectives.

Part II technologies [START_REF] Akyildiz | A survey of mobility management in nextgeneration all-ip-based wireless systems[END_REF]. Mobility management issues concern both the link and network layers.

IP Mobility Management

At the link layer, access to the Internet via wireless networking entails the need for frequent changes of the serving Access Points (APs), either due to the small cell size of wireless networks or due to the desire of users for being always best-connected via any of the available wireless networks. However, frequent handoffs not only introduce time delays and packet loss which may be prohibitive for real-time applications but also lead to extensive power consumption which limits the lifetime of the energy-constrained mobile terminals.

At the network layer, mobility support is a requirement not appropriately addressed by the Internet Protocol (IP) of the TCP/IP protocol suite which was originally designed for static, wired networks. The most well known mechanism for mobility support in IP networks is Mobile IP (MIP) [START_REF] Bhagwat | Network layer mobility: an architecture and survey[END_REF], an Internet Engineering Task Force (IETF) standard communication protocol that is designed to let MNs move from one network to another while maintaining a permanent IP address. This is done through the interaction of a Home Agent (HA) and a Foreign Agent (FA) and the utilization of two IP addresses by the Mobile Node (MN): one for identification and the other for routing. However, the handoff process for updating the MN's routing address, leads to additional time delays and packet losses degrading the communication quality.

This chapter aims at providing essential background relevant to mobility management issues, standard protocols and literature review of research efforts for enabling seamless mobility over the Internet. The rest of the chapter is organized as follows: In section 6.1 we first describe the challenges for mobility management in next generation all-IPbased wireless systems. In section 6.2 the standard mechanisms currently proposed for dealing with this problem are presented. Finally, in section 6.3 we highlight the substantial shortcomings of the standard solutions and provide a literature review of works which target at amending them or proposing new ones. Finally, section 6.4 provides the chapter summary and our research directions for mobility management.

IP Mobility Problem

Mobility management (MM) as a general term is defined as the set of mechanisms aiming at providing seamless mobility [START_REF] Cerqueira | Mobility management for multi-user sessions in next generation wireless systems[END_REF]. Seamless mobility is commonly used to describe the requirement for continuation of an ongoing communication of a mobile node, regardless its mobility within the same network (link layer mobility) or among different subnetworks (network layer mobility). MM contains two components: location management and handoff management. The purpose of location management is to keep updated the system of the current location 1 of the mobile node and it is achieved by two types of tasks, location updates sent by the mobile node to the system or location deliveries (paging) whereby the system identifies the mobile node's location based on records of its latest communication.

Handoff management is the process of transferring an ongoing communication from the old Point of Attachment (PoA) to a new PoA, due to the change of the MN's location. If the new PoA belongs to the same subnetwork with the old PoA (link layer mobility), a link layer handoff is only required. However, if the new PoA belongs to a new subnetwork (network layer mobility), a handoff at the network layer is also necessary. Figure 6.1 illustrates the main processes included in the mobility management mechanism and provides a taxonomy of approaches for the execution of particular tasks on which we are focusing on in the rest of this thesis.

Enabling mobile and ubiquitous wireless access to the Internet has been widely recog-

1 In this case, location refers to the IP address of the M N that corresponds to its current subnetw ork, and not its physical coordinates. nized as one of the most challenging problems. This is mainly attributed to two factors:

(i) the wireless link layer characteristics and (ii) the operation of the TCP/IP protocol.

Limitations of the Wireless Link Layer

At the link layer, access to the Internet via wireless networking entails the need for frequent changes of the serving access points, either due to the small cell size of wireless networks or due to the desire of users for being always best-connected via any of the available wireless networks. However, frequent handoffs not only introduce time delays and packet loss which may be prohibitive for real-time applications but also lead to extensive power consumption which limits the lifetime of the energy-constrained mobile terminals.

In addition, the heterogeneity that will characterize future wireless systems instigates the development of an intelligent and global handoff mechanism that can provide seamless roaming capability regardless the actual underlying wireless access technology.

Limitations of the TCP/IP protocol

The TCP/IP protocol suite is a five-layer protocol architecture which defines the specifications for communication over the Internet. The Transmission Control Protocol (TCP) at the transport layer and the Internet Protocol (IP) at the network layer are its main elements which were originally designed for static computer networks, making it difficult for accommodating mobility support.

According to the IP, an IP address has two major functionalities: to uniquely identify a particular node in the entire network and for routing the traffic between two endpoints.

The IP address is indicative of the IP subnetwork the terminal resides. Apparently, the problem arises when the node changes subnetwork due to its mobility; its IP address has to be changed to represent its new point of attachment to the network such that packets are routed successfully to it. However, IP address changes cause interruption of any ongoing IP session.

The TCP provides a connection-oriented service that allows for reliability, fragmentation, flow control, and congestion control. Even if the device is able to obtain a new IP address dynamically, the transport connections established in the previous network will be broken after the change of the IP address. In addition, the congestion control of TCP [START_REF] Allman | TCP Congestion Control[END_REF] is based on the assumption that the end-to-end path of a connection is relatively stable after connection establishment and therefore it is not able to distinguish whether the packet loss is due to congestion or due to mobility and wireless link properties. Thus, the congestion window is reduced even if there is no congestion, resulting in an unreasonable throughput degradation [START_REF] Elaarag | Improving tcp performance over mobile networks[END_REF].

Standard Handoff Protocols

The Standard solutions for handoff management at both the link and network layers are described in the following. have been proposed and for different wireless access technologies, we focus on the IEEE 802.11 standard [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements -Part 11: Wireless LAN[END_REF], for its popularity, its inefficiency for mobility support due to small AP coverage, and the availability of numerical results regarding its latency analysis [START_REF] Mishra | An empirical analysis of the ieee 802.11 mac layer handoff process[END_REF].

According to its specifications, the handoff process includes three main steps: Discovery, Authentication and Association, as illustrated in Figure 6.2. During the Discovery phase, the MN searches for a AP with stronger RSS to associate with. This is accomplished through a medium access control (MAC) layer function, called scan. There are two modes of scanning: active and passive. In passive mode the MN listens for beacon messages (sent periodically by the APs), on assigned channels. In active mode, the MN sends in addition Probe Request broadcast packets on each channel and receives Probe Responses from APs. The standard defines two parameters to be set while scanning, namely MinChannel-Time and MaxChannelTime. The MinChannelTime is the minimum time the MN needs to spend on a channel. If the MN finds the channel to be busy before the MinChannelTime elapses, it concludes that there exists at least one AP operating on that channel. Therefore, the MN waits till the MaxChannelTime transpires to give to AP(s) operating on that channel enough time to send back their Probe Responses. After scanning all channels, the MN selects a target AP and enters the Authentication step, which includes the transmission of the MN's identity to the AP and the AP's Authentication Response. The L2 handoff terminates upon the reception of an Association Response message.

Handoffs are a major challenge in wireless networks since they occur frequently due to the small coverage area of the APs and the wireless link quality. This is because, during handoff the MN is unable to send or receive data, therefore its duration is critical for meeting the needs of real-time applications such as VoIP, 802.11 phones, mobile video conferencing and chat. For instance, Voice over IP (VoIP) requires a maximum end-to-end delay of 50 ms [START_REF] Mishra | Context Caching using Neighbor Graphs for Fast Handoffs in a Wireless Network[END_REF]. Furthermore, the scanning process is power consuming and should be avoided by the energy-constrained mobile terminals. Finally, the transmission of Probe Requests and Probe Responses leads to considerable bandwidth waste. According to the experimental results in [START_REF] Mishra | An empirical analysis of the ieee 802.11 mac layer handoff process[END_REF] the discovery phase delay is the dominating factor in L2 handoff latency, accounting for more than 90% of the overall cost. This is mainly because the MN has to wait for Probe Response messages even if no APs are operating on specific channels. issues occur and its session is not interrupted. However, if the APs belong to different IP subnetworks, the routing subnetwork prefix changes and thus a Network Layer (NL)

or layer 3 (L3) handoff follows the L2 handoff. Figure 6.3 illustrates the handoff process as described in MIP [START_REF] Bhagwat | Network layer mobility: an architecture and survey[END_REF]. It includes three stages: Movement Detection (MD), Address Configuration (AC) and Binding Update (BU). The movement detection stage is entered after a MN has attached itself to the new network (i.e. after the L2 handoff). In this stage a MN detects that it has moved to a new network, based on messages broadcasted by the ARs in either a passive or active mode. In passive mode, the ARs regularly send broadcast

Router Advertisements messages that contain their identity and their IP addresses.

In active mode, the MN is sending in addition Router Solicitation requests to ARs in order to discover new points of attachment to the network. The MN receives relevant information from the network that will allow it to configure its CoA and other network settings. Finally, it sends a Binding Update to its Home Agent.

The movement detection mechanism in MIP is designed to be suitable for mobility over heterogeneous networks and therefore it lacks information of the layer 2 handoffs.

When a MN moves to a new sub network, packets are not delivered to the MN at the new location until the Care-of-Address (CoA) registration to the HA is completed, due to the time difference between the completion of the link layer handoff and the registration of the new PoA to the Home Agent. In fact, during MD, the MN is physically connected to the new PoA, whereas at network layer it is still connected to the old PoA. Therefore, synchronizing the link and network layer handoffs is necessary, which can be achieved by minimizing the movement detection delay. MD duration is the main delay factor which depends on the frequency of the Router Advertisement or Router Solicitation messages. Statistically, the longer the time between two consecutive messages, the more it takes the movement detection to be completed. According to results found in [START_REF] Lee | Analysis of handoff delay for mobile ipv6[END_REF], MD latency is on average 36ms to 558ms when Router Advertisements are broadcasted every 0.05s to 1.5s.

State of the Art Handoff Schemes

The increasing demand for the support of real-time multimedia applications over the Internet via wireless and mobile networking, but also the failure of the standard solutions to fulfil this requirement drove the research towards the improvement of the handoff mechanism, resulting in a vast variety of proposals.

At the link layer most of proposals aim at improving the discovery phase, which is the dominating delay and energy consuming factor. The authors in [START_REF] Velayos | Techniques to reduce the ieee 802.11b handoff time[END_REF] claim that a slight increase (about 4%) of AP's capacity used for beacon transmission can shorten the beacon interval from 100 ms to 60 ms and adjusting MinChannelTime and MaxChannelTime in the active scanning can reduce the search phase by 20%. The authors in [START_REF] Mishra | Context Caching using Neighbor Graphs for Fast Handoffs in a Wireless Network[END_REF] target at decreasing the probe delay by reducing both the total number of channels to be probed and the waiting time on each channel. For doing this, they propose two novel L2 handoff schemes, called Neighbor Graph (NG) algorithms and NG-pruning algorithms, which make use of two data structures, namely the neighbor graph (NG) and the non-overlap graph (NOG), respectively. These structures abstract the handoff relationships between access points. In the SyncScan approach [START_REF]SyncScan: practical fast handoff for 802[END_REF], the MN monitors the proximity of APs in its neighborhood by scanning channels intermittently and recording the corresponding signal strengths. By synchronizing the APs and forcing the APs to transmit the beacon signals based on the channel they operate on, the MN knows when the beacon signals are transmitted on each channel. Therefore, the MN does not need to wait for the full beacon interval. Since

SyncScan is based on the regular monitoring of APs, time synchronization is a critical issue. Moreover, the accompanied channel switching delay incurred for each channel scan is not considered.

Rather than relying on RSS measurements, there are several schemes which follow alternative approaches. The authors of [START_REF] Lin | Performance analysis of a cross-layer handoff ordering scheme in wireless networks[END_REF] propose a cross layer handoff ordering scheme, which adopts the frame success rate (FSR) at MAC layer rather than the RSS at the PHY layer, as the link quality metric. They claim that FSR is more suitable to represent the link quality, because it can be easily mapped to the application layer QoS requirements of various applications, e.g. throughput, delay, and packet success rate. Another important class of handoff schemes make use of location information, based on the assumption that the closest AP provides better quality of communication. The authors of [START_REF] Kyriazakos | Optimization of the handover algorithm based on the position of the mobile terminals[END_REF] propose using the mobile node's position for increasing the stability of the network, preventing calldrops, traffic congestion and increasing the resource utilization. Furthermore, geolocation

State of the Art Handoff Schemes

119 information can also be used in heterogeneous networks. In [START_REF] Pesola | Location-aided handover in heterogeneous wireless networks[END_REF] the authors present a location assisted algorithm to manage handoffs between WLAN and GPRS networks.

A new network entity monitors the movement of MNs and detects when a MN moves inside the coverage area of a WLAN. According to various parameters, such as velocity, direction and ongoing traffic of MNs, it can estimate if a handoff is appropriate. [START_REF] Ylianttila | Analysis of handoff in a location-aware vertical multi-access network[END_REF] presents an architecture for the seamless location-aware integration of WLAN hotspots into cellular networks. Location-awareness, obtained by GPS receivers, can assist whether to join or leave WiFi hotspots. A location-based vertical handoff scheme in WLAN and UMTS networks is introduced in [START_REF] Inzerilli | A location-based vertical handover algorithm for limitation of the ping-pong effect[END_REF] for reducing the ping-pong effect, while in [START_REF] Tseng | Location tracking assisted handover algorithms for broadband wireless networks[END_REF] two location tracking algorithms are described for achieving reduced number of handoffs without additional connections between the mobile stations and the non-serving Base Station in a IEEE 802.16e network. Finally, the authors in [START_REF] Dutta | An experimental study of location assisted proactive handover[END_REF] demonstrate via experimental results the advantages of location-assisted handoffs over traditional RSS-based schemes regarding handoff latency and packet loss.

At the network layer, minimizing the duration of movement detection is mainly proposed. The authors in [START_REF] Fikouras | Performance comparison of hinted-and advertisementbased movement detection methods for mobile ip hand-offs[END_REF] discriminate two broad classes of movement detection approaches, advertisement-based and hint-based schemes.

The first rely on the periodic broadcasting of AR advertisements which include mobility related information. CARD (Candidate Access Router Discovery) [START_REF] Liebsch | Fast Router Discovery with RA Caching[END_REF] is an IETF proposal where an AR announces its capabilities in broadcast messages. In such schemes, there is an inherent trade-off between the bandwidth wasted by advertisements and the MD performance. The higher the rate that periodic advertisements are broadcast, the more bandwidth is wasted by these messages.

Hint-based mechanisms try to compromise the layer independence between Mobile IP and the link-layer by utilizing triggers or events from the link layer in order to detect the movement of the mobile node to a new network. The most popular of hint-based MD scheme is Fast Mobile IP (FMIPv6) [START_REF] Koodli | Fast Handovers for Mobile IPv6[END_REF]. It defines two modes of operation depending on the predictability of MN's mobility: the predictive mode and the reactive mode. In the predictive mode, the MN can predict its movement and the identifier of a prospective point of attachment, e.g. IEEE 802.11 Access Point (AP), and most of the handoff operations can be finished before the link layer handoff. On the contrary, in the reactive mode, the MN starts handoff operations after the link layer handoff. The performance of FMIPv6 suffers from problems such as predictive latency and reactive loss [START_REF] Lu | Improving the performance of fast handovers in mobile ipv6[END_REF]. Predictive latency is the time difference between the moment that packets for the MN are forwarded from the old AR to the new AR and the moment that the link between the MN and the old AR is really down. On the other hand, reactive loss is due to the fact that the old AR starts forwarding packets after the MN is attached to the new AR.

In the past few years, it has been proposed that geolocation information can be used for improving handoff management at the network layer as well. In [START_REF] Dutta | Gps assisted fast-handoff mechanism for real-time communication[END_REF] GPS coordinators are used to configure care-of-address, which provides faster IP address discovery. However, this strategy consumes too much address space and lacks flexibility, since it is mostly efficient for fast moving vehicles. Finally, few schemes rely on sensing techniques. [START_REF] Waharte | Sensor-based architecture for qos provisioning and fast handoff management in wlans[END_REF] proposes a sensor-augmented architecture for limiting the number of scanned channels and taking informed decision about the most appropriate AP to be associated with. In [START_REF] Bahety | Scalable qos provisioning for mobile networks using wireless sensors[END_REF] sensor networks are deployed at the network fringes in order to detect the L3 movement of a MN between two APs and prepare proactively its registration to the new network.

Chapter Summary

Mobility support is the main requirement for the accomplishment of the envisioned wireless and mobile Internet. This chapter provided essential background regarding the main mobility management problems and solutions focusing on the handoff process at both the link and network layers.

Solutions of the Standard protocols were firstly presented highlighting their main limitations. In the sequence, literature proposals trying to tackle them were explored and their advantages and disadvantages were discussed, in order to aid the comprehension of our motivation behind our proposed handoff schemes in the following chapters.

Undoubtedly, in future communication networks mobility management techniques should be designed in a way such that global roaming among heterogeneity access technologies will be possible. Moreover, considering the significance of location information in future context-aware communication makes location-aware mobility management schemes more attractive and viable solutions. These observations conducted our research efforts for designing efficient handoff schemes which are described in the following chapter.

Chapter 7

Location-aware Mobility Management H andoff management is one of the main research challenges for the realization of the envisioned mobile and wireless Internet. This is mainly due to the latency delay and energy consumption introduced during handoff, which are of major concern for realtime applications and battery-constrained mobile terminals. Moreover, the co-existence of heterogeneous networks which will be available to satisfy different needs of users impels for global handoff solutions, i.e. independent of any specific wireless access technology triggers.

In this chapter, we explore whether handoff management can benefit from the pervasiveness of future communication networks. The key idea is to follow the ambient intelligence paradigm for the purpose of context-aware handoff. Focusing again on the Radio Frequency Identification (RFID) but also on the Wireless Sensor and Actuator Networks (WSANs)

pervasive technologies, we propose two schemes for handoff prediction at the network layer or at both link and network layers. Analytical models for their time response and energy consumption are firstly derived and finally simulation-based results validate their performance superiority over the Standard solutions.

The rest of this chapter is organized as follows: section 7.1 explains our motivation for conducting this study. Sections 7.2 and 7.3 describe our two proposed handoff management schemes. Section 7.4 analyzes their performance theoretically while section 7.5 provides numerical results based on simulations. Finally, section 7.6 summarizes the main points of this chapter.

7.1 Motivation: Need for Seamless, Energy-aware and Global

Handoff

Our motivation stems from the necessity for the design of seamless but also energyefficient handoff mechanisms that will meet the requirements of real-time and QoS-demanding applications and can be easily adopted by the battery-constrained mobile terminals. Moreover, we target at schemes that do not rely on special triggers or characteristics of the underlying wireless access technology in order to be easily integrated in heterogeneous networks.

In the context of the upcoming pervasive communication era, several heterogeneous technologies will be available enabling ubiquitous access to different applications from a plethora of available interfaces at future multi-mode mobile terminals. Investigating potential synergies among these heterogeneous technologies appears indispensable in order to tackle more efficiently different functionalities in this network. We focus our attention on the possible interactions between wireless access technologies such as IEEE 802.11 with RFID (Radio Frequency Identification) technology and/or WSAN (Wireless Sensor/ Actuator Network) technology, in order to improve the handoff process from the latency and energy consumption points of view, both of which are of major interest in the generalized Internet mobility.

The main strengths of RFID include the low cost of passive tags, the fast and accurate reading of tags, the better resilience to harsh environmental factors, the ease and flexibility in associating tag IDs with handoff decision related information in a database, its independence from the principal wireless access technology and its anticipated widespread deployment and integration in future communication networks. Based on these observations, we first propose utilizing a RFID tag deployment for performing the movement detection step of the L3 handoff process, illustrated in Figure 6.3. In our proposed scheme, by associating area location with network topology information with the aid of the RFID technology, a MN can predict its next PoA and consequently pro-actively proceed with its registration to this PoA (if different from the current PoA). Thus, the IP handoff latency can be reduced to match the L2 handoff latency.

In the sequence, we try to take also into the factor of energy consumption and propose a second handoff scheme for both link and network layers which relies on the the deployment of a hybrid RFID and WSAN system. Even though RFID and WSAN are under parallel development, few integration schemes have been proposed [START_REF] Zhang | Integration of rfid into wireless sensor networks: Architectures, opportunities and challenging problems[END_REF]. The main strength 7.2 Scheme A: RFID-assisted Network Movement Detection 123 of WSANs is their wireless communication for performing distributed sensing and actuation tasks. However, sensors are power-limited and require strict time-synchronization for performing real-time computations. In contrast, RFID tags do not need battery and correlating their IDs with network information [START_REF] Papapostolou | Handoff management relying on rfid technology[END_REF] enables real-time information retrieval by reader-enabled terminals. However, direct communication among readers is not supported.

Thus, we argue that their integration is essential for enabling a complete pervasive solution.

In our system architecture, the WSAN is responsible for initiating/ceasing the handoff process, predicting the next point of attachment (PoA) and communicating through multi-hop all handoff related information. For predicting the next PoA, RFID passive tags are deployed at the outer part of APs' range in order to track the movement pattern of a MN with a reader-enabled terminal.

In the following sections, both schemes are described in detail.

Scheme A: RFID-assisted Network Movement Detection

Scheme A aims at reducing the movement detection latency for matching the handoffs at the link and network layers. Passive tags are deployed throughout the studied area in order to detect the movement of a MN with a reader -enabled terminal. The tags can be deployed in the area such that their IDs are associated with network topology information, i.e. each tag ID is matched to its best PoA. Then, during MN's mobility, information retrieved from the detected tags is used for detecting the user's movement and thus anticipating its next best PoA. Moreover, the selection of the best PoA is based on a decision function which can incorporate several parameters. The flexibility on its definition offers the opportunity for the provision of QoS support, by taking into account preferences of users or network providers.

System Architecture Design

We consider a wireless network divided into a set P of smaller subnetworks each one served by a single AP, acting as the Access Router (AR) of that subnetwork as well. Within the entire network, a MN m is roaming among these subnetworks while communicating.

When located within a subnetwork served by AP i ,M Nm has this AP as its Point of Attachment (PoA) for gaining access to the Internet, i.e. PoA m = AP i .

Apart from a wireless interface, the terminal of MN m is also equipped with an RFID reader r m , which retrieves information from a set U of passive RFID tags deployed in a grid fashion on the floor of the area. Each tag t U has certain ID ID t and location (x t ,y t ) and is called reference tag.

Finally, a dedicated server within the network domain, called RFID-Server (RFID-S), maintains a database to be utilized for the purpose of the movement detection procedure during the roaming of the MN.

Mechanism

The mechanism details are described in the following. Note that the L2 handoff process is not explicitly modified and can be assumed the one described in IEEE 802.11 standard [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements -Part 11: Wireless LAN[END_REF]. However the MD stage in the above proposal can be initiated in parallel with it or even trigger its initiation. In this case, our proposal helps L3 handoff to better synchronize with L2 handoff. After the reception of a successful Binding Acknowledgement message, the handoff is completed and the MN can continue its ongoing communication. In the case of movement between APs within the same subnetwork (same Access Router), no L3 registration is needed since the CoA has not changed. In this case, our proposal would trigger the L2 handoff to start pro-actively the scanning phase for discovering the best AP's RSS before loosing the signal from the current AP.

Message Exchange

Database Construction

The Point of Attachment Map (PAM) is built during an offline pre-phase and associates each reference tag ID with topology or connectivity information. As connectivity information, several characteristics can be considered as most appropriate to be stored depending on the requirements of the network and preferences of users or the network provider. We consider a simple scenario according to which each tag ID is associated with its best PoA.

Best P oA t for tag t is considered the AP j from which the RSS at that tag's position (x t , y t ) is stronger, similar to the RSS-based L2 handoff, i.e.

P oA t = AP arg max j∈N RSS W iF i (d tj ) , (7.1)
where d tj the distance between tag t and AP j . Table 7.1 shows the format of the PAM. Building the above PAM database requires manual effort for collecting RSS measurements from all APs at all reference tags' positions, which may be undesirable in some cases.

However, our proposed PoA prediction scheme is actually independent of this choice. For instance, the distance between APs and reference tags could have alternatively been used, such that best PoA t for tag t is the AP j which is closer to this tag, i.e.

PoA t = AP arg min

Network layer

According to Scheme A, the reader is querying for tag IDs even when there is no need for handoff, leading to considerable power waste. To deal with this limitation we propose employing WSAN in addition to the RFID deployment in order to control the MN's reader activity and minimize the energy consumption.

In the proposed system architecture, a deployment of RFID passive tags is used for capturing the mobility pattern of a MN with a reader-enabled terminal in order to predict its next PoA. In this way, there is no need for performing the power and time consuming RSS scanning process as defined in the 802.11 MAC protocol. In addition, if the predicted AP belongs to a different subnetwork, there is no need for waiting for the reception of Router Advertisement messages, hence minimizing the movement detection delay.

The main role of the WSAN is to serve as an overlay control plane on the top of the WLAN data plane, for monitoring and controlling the handoff process. In this way, the handoff-related overhead is shifted from the main data communication channel. Sensor nodes monitor the absence or presence of the MN within a specified region and route this information to actuator nodes which are then responsible for triggering the initiation or termination of the handoff prediction process, respectively. Thus, by selectively performing handoff prediction, further power consumption savings are achieved. The WLAN infrastructure consists of APs, deployed at known positions similar to the cellular concept and is responsible for providing data communication and wireless access to the Internet to MNs. R max denotes the maximum range of each AP and R saf e a safe region within which there is no need for handoff preparation. Defining these ranges can be done during the network configuration and may depend on parameters such as RSS level, obstruction, etc. In this study, we consider the distance from the AP. A database, called WLAN-Knowledge Table, is used to store all this information such that an entry associates an AP identifier with the position and the ranges R max and R saf e of that AP. deployed at strategic positions such that each pair of sensor nodes is responsible for routing information between a particular pair of neighboring actuators. In Figure 7.2, the pair s ij s ji is responsible for the communication between actuators a i and a j . Sensors are also pre-configured with safe region information (R saf e ) in order to monitor the MN within or without this region and inform the actuator only in the case of change of the MN's state.

System Architecture Design

Mechanism

In the following, a detailed mechanism description is provided. Reader command. We propose exchanging these messages on the WSAN for reducing the handover control overhead from the main data channel. However, this is not a strict protocol requirement.

Finally, during the handoff execution phase, the Standard steps are followed without the need for the L2 discovery and L3 movement detection steps.

Mobility Modeling

The movement pattern of the MN is modeled by three main mobility characteristics: current position ( X, Y ), velocity v and direction of movement φ. The estimation of these parameters relies on the RFID deployment and the reading capability of the MN's terminal.

Let U i the list of detected tag IDs at time t i . By looking-up at the RFID Knowledge For estimating the velocity, location estimations at two different time instances t i and t i+1 are required, such that

v i+1 =(v i+1,x ,v i+1,y )= X i+1 X i t i+1 t i , Y i+1 Y i t i+1 t i (7.5)
Finally, the direction of movement is estimated with reference to the serving AP's position (X 0 ,Y 0 ) by using vector analysis. Let

V i =(X i X 0 ,Y i Y 0 )andV i+1 =(X i+1 X i ,Y i+1
Y i ) the movement vectors at times t i and t i+1 , respectively. The angle φ [0, 2π] between them is given by

φ = cos -1 V i ×V i+1 V i V i+1 , (7.6)
where < ×> denotes the dot product between two vectors and V the norm operator for vector V.

Handoff Prediction Algorithm

The handoff prediction algorithm uses the MN's current mobility parameters and the APs position information (from the WLAN-knowledge table) for predicting whether the MN is moving towards a new AP and if so, for determining the most probable next PoA.

For identifying whether the MN is moving away from its serving AP, its movement direction is used. As shown in Figure 7.2, if φ<π/2 the MN is moving towards its current PoA and therefore there is no need for handoff. However, if φ ≈ π/2 the MN will most probably need handoff and therefore its next PoA should be predicted in advance. Assuming constant velocity v = v i+1 and direction of movement, the position of the MN a time t i+2 can be predicted as

X i+2 , Y i+2 = X i+1 + v x δt, Y i+1 + v y δt , (7.7)
where δt = t i+2 t i+1 = t i+1 t i the reading rate.

The next handoff decision is then based on the distance from the surrounding APs, such that the closest one is selected as the best AP i+2 at time t i+2 . However, for avoiding the ping pong phenomenon when the MN moves along the borders of two neighbor APs, a distance threshold TH condition is incorporated in the decision algorithm, such that AP i+2 =AP arg min min j d i+2 (AP j ) ,d i+2 AP i+1 TH (7.8) where d i (AP j ) the predicted distance from AP j at time t i and AP j ∀ = AP i+1 .

Recalling the handoff decision function of Schemes A, described in subsection 7.2.2.3, the selection of the next PoA was based on a simple maximum likelihood concept, instead of relying on accurate location and distance estimations. However, it is worthy mentioning that this is not the main difference between the two schemes. Either or even another decision function could be employed by any of them. We selected that decision function for Scheme A in order to emphasize the potential benefits of an available RFID deployment for the purpose of handoff prediction. Whereas the main focus of Scheme B is the accurate location estimation and energy savings that can be achieved if the key properties of many technologies are combined in a common architecture.

Theoretical Analysis

In this section, we analyze theoretically the performance of the Standard protocols and our proposed schemes with respect their time response and energy consumption.

Time Response

In general, the total handoff duration T HO includes the time needed for the link and possibly network layer handoffs, denoted as T L2 and T L3 , respectively.

Standard Protocols

According to the specifications of the Standard solutions, the L3 handoff starts after the completion of the L2 handoff. Therefore, the total handoff duration T S HO is given by adding these two factors, i.e. The duration of the discovery phase includes the channel switching and transmission (CS&T) delay and the probe delay T prob [START_REF] Mishra | Context Caching using Neighbor Graphs for Fast Handoffs in a Wireless Network[END_REF]. The CS&T delay is the time needed to switch and transmit on a channel and is about 40-150 usec [START_REF] Bahl | SSCH: Slotted Seeded Channel Hopping for Capacity Improvement in IEEE 802.11 Ad-Hoc Wireless Networks[END_REF]. The probe delay depends on the scanning mode, passive or active. In passive mode, the average probe delay is function of the number of scanned channels and the transmission rate of the beacon frames from the APs. For instance, in the IEEE 802.11b/g (11 channels) and with beacon interval 100 ms, the average probe delay is 1100 ms. On the other hand, in active mode the probe delay can be determined by the MinChannelTime and MaxChannelTime values, which are device-dependent. The current active scanning procedure requires for a MN to scan all available channels. Therefore, the probe delay is given by

T prob = C ± M axChannelT ime M inChannelT ime 2 , (7.9)
where C the number of channels (i.e., 11 channels for IEEE 802.11b and 32 channels for IEEE 802.11a).

Scheme A

In our proposed Scheme A, the L3 movement detection step is performed by the RFID system, independently of the WiFi channel. Thus, in contrast with MIP, the MD can start From the above components, the time factors T MN-S and T S-MN depend on the messages' size, supported data rate, the propagation delay and the time spent due to collisions before accessing the medium. Considering the high data rates (1 up to 54-Mbps) of the current IEEE 802.11 protocols, these time parameters are negligible. In fact, the prevailing one is the time needed for reading the tags by the MN's reader, i.e. T TR , which is analyzed in the following.

T TR depends on two factors: (i) the number of tags within a reader's range whose IDs need to be acquired with a single reading and (ii) the anti-collision protocol followed by the reader for resolving the collisions among multiple tags' responses. The number of responding tags depends on the geometry of the tags deployment and the reader's range.

Considering a grid tag deployment and that the reader's radiation pattern forms a circle, as depicted in Figure 7.4, the maximum number of detected tags N is given by N=4 r/δ{ 2 , (7.12)

where δ the inter-tag spacing and r the range radius.

For retrieving information from multiple tags, resolving the collisions among their transmissions is necessary. Reviewing the literature, several anti-collision protocols have been proposed, which mainly differ in the number of tags that can be read per second, their power and processing requirements [START_REF] Klair | On the energy consumption of pure and slotted aloha based rfid anti-collision protocols[END_REF]. In this work, we have selected as base of our analysis the Pure and Slotted Aloha, which are time-division multiple schemes.

For retrieving information from N tags, resolving the collisions among their transmissions is necessary. [START_REF] Klair | On the energy consumption of pure and slotted aloha based rfid anti-collision protocols[END_REF] provides a detailed analysis for several anti-collision protocols. In this When reading starts, each tag transmits its ID randomly irrespectively of the rest N 1 tags. The communications from a tag to the reader is modeled as a Poisson process [START_REF] Schwartz | Telecommunication networks: protocols, modeling and analysis[END_REF].

Each tag responds on average λ times per second. The model requires independence among tag transmissions, which is supported by the lack of tag-to-tag communication capabilities.

Since each tag's transmission is Poisson distributed, there is a mean delay of 1/λ between consecutive transmissions. This is referred to as the arrival delay [START_REF] Schwartz | Telecommunication networks: protocols, modeling and analysis[END_REF]. Thus, on average each tag takes 1/(Nλ) time to transmit its ID for the first time. During collisions, colliding tags retransmit after a random time. In Aloha-based schemes, the retransmission time is divided into K time slots of equal duration t s and each tag transmits its ID at random during one of the next time slots with probability 1/K. This means tags will retransmit within a period of K ± t s after experiencing a collision. On average, a tag will retransmit after a duration of ((K + 1)/2) ± t s = a slots. The number of collisions before a tag successfully responds is e xG A 1, where e xG A denotes the average number of retransmission attempts made before a successful identification, where G A = N λt s is the offered load and

x = 1 for Pure Aloha and x = 2 for Slotted Aloha. Since each collision is followed by a retransmission, the average delay before a successful response is (e xG A 1)a, followed by a single successful transmission of duration t s . In total, the average delay a tag takes to transmit its ID successfully is t T R = (e xG A 1)at s + t s + 1 Nλ . For non-saturated case, i.e. tags to be detected are less than the maximum number of tags that can be read per inventory round, the total time needed for reading successfully N tags follows the linear where T TR is the time needed to read all tags within range, T MN-AP the time needed to transmit the Tag List message from MN's sensor to its serving AP's actuator, T C the computational time for handoff prediction, T AP -AP the time for exchanging the HO Request and HO Reply messages between the current AP's and the new AP's actuators through their dedicated sensor pair and T AP -MN the time required for sending the HO Needed message from the serving AP's actuator to the MN.

T TR is given in eq. (7.13), but in this case the number of detected tags N is different, since tags are deployed in a uniform distribution instead of grid. Assuming their density is δ =N T /πR 2 , where N T the total number of tags in a surface πR 2 , and that the reader's radiation pattern forms a circle with radius r, the maximum number of detected tags N is given by

N= N T (πr/πR) 2 { = N T (r/R) 2 {. (7.16)
Finally, the time factors T MN-AP ,T AP -AP and T HO-ND depend on the messages' size, supported data rate, the propagation delay and the time spent due to collisions before accessing the medium. The parameters T MSG and T AP -MN have been neglected due to their order of magnitude (μs) compared to the rest.

Energy Consumption

Energy consumption is another critical performance aspect since mobile terminals are energy constrained.

IEEE 802.11 Scanning

According to IEEE 802.11 specifications, when the RSS or SNR from the serving AP drops below a certain threshold value TH HO , the handoff process is initiated. The main power-demanding process of standard handoff is the RSS scanning, during which the MN's wireless NIC needs to periodically probe all legitimate channels in order to obtain RSS measurements from all visible APs. For a given trajectory of a MN, the energy consumed for this MAC operation depends on the frequency of RSS scans. Let D T the total duration of a MN's trajectory and D S the average WiFi scanning period1 , i.e. the time between two consecutive scan operations. The total energy consumed is given by

E S = D T D S T prob P WiFi S , (7.17)
where T prob given in eq. ( 7.9) and P WiFi S the power consumption during WiFi scanning.

Scheme A

At Scheme A the main energy consuming processes are the scanning operations at both the WiFi and RFID channels. During a MN's trajectory, its reader emits periodically RF signals to energize reference tags within its vicinity in order to retrieve their IDs. Assuming the ALOHA variants as the anti-collision algorithms, the energy consumed during a single inventory round is given by T TR ± P S , where T TR is the duration of the inventory round given in eq. (7.13) and P RF ID S the power consumption during scanning. Let D T the total duration of a MN trajectory and D R > T TR the reading period, i.e. time between two consecutive inventory rounds. The energy consumed is given by

E A RF ID = D T D R T TR P RF ID S +(D R T TR )P RF ID I , (7.18)
where P RF ID I the power consumed during idle listening.

Therefore, the total energy consumption for Scheme A is

E A =E S +E A RF ID . (7.19)

Scheme B

At Scheme B the prediction phase involves the main energy consuming tasks by the MN, which are (i) RFID scanning and (ii) sensor communication.

While the MN is monitored within the safe region, there is no need for tag scanning, i.e. its reader is in idle listening mode. However, as soon as it exceeds this region, a handoff prediction phase is entered during which its reader periodical ly retrieves ID information from the RFID tag deployment. The frequency and duration of the prediction and sensing sessions depend on the system configuration regarding the selection of the R saf e parameter.

Apparently, larger values of R saf e lead to power savings (no scanning operation) but with the risk of wrong or missed handoff predictions. Let f pred and D pred the frequency and average duration of the prediction phase, respectively, for a given MN's trajectory. If D R is the reading period within each prediction phase, the energy consumed during a single handoff prediction phase by the reader is given by For the entire trajectory duration D T , the total energy consumed is computed as

E pred,R = D pred D R T TR P RF ID S +(D R T TR )P RF ID I . ( 7
E B =(D T f pred D pred )P RF ID I + f pred (E pred,R +E pred,S ) (7.22)

Performance Analysis

In this section, we evaluate the performance of our scheme based on simulations, using MATLAB [85] as simulation tool.

Simulation Setup

Our simulation environment which corresponds to a rectangular indoor area 200 ± Within this region, a MN whose terminal supports an interface to the WLAN and an RFID reader roams among the 11 available subnetworks. Regarding its mobility, we have assumed the Random WayPoint (RWP) mobility model [START_REF] Camp | A survey of mobility models for ad hoc network research[END_REF]. Briefly, in the RWP model

Accuracy Analysis

For evaluating the performance of our handoff approaches their accuracy in predicting the next PoA is of major concern. In order to quantify this, we define a new performance metric named Point of Attachment Prediction Error Ratio (PAPER) and given by PAPER = # correct P oA decisions # all P oA decisions (7.24)

Correct PoA decision is considered the case when the predicted PoA is identical with the AP from with the strongest RSS. PoA decision is taken by the RFID-S every time it receives a Tag List update by the MN which depends on the reading period.

In Figure 7.5 the prediction accuracy of schemes A and B is evaluated as the reading period D R increases, for two different V max values. For all cases, decreasing the frequency of Tag List updates (by increasing the reading period) degrades the accuracy performance.

For slow-moving cases however, the performance degradation is less intense. Comparing the two schemes, Scheme A performs better even for higher speed. This is because at this scheme the MN's movement is detected over the entire AP range, whereas at Scheme B it is tracked only outside the safe region. Adjusting the frequency of the reader reports or the design parameter R saf e depending on the MN speed of movement could be possible techniques for alleviating this accuracy degradation. for both of our schemes and the IEEE 802.11 Standard. First of all, we observe that there is a trade off between accuracy and energy performance objectives regarding the value of the scanning frequency. Comparing the three systems, we observe that the WiFi scanning is much more power demanding than RFID tag scanning. Therefore, at Scheme A the overall consumed energy is slightly increased due to the additional tag reading by the MN's reader. Scheme B gives the best performance, leading to considerable power savings, even in the case of very frequent Tag List reports. This is apparently due to the elimination of the WiFi scanning process and the controlled RFID reading.

Energy Consumption

Time Latency

In Figure 7.7 the main prediction delay factors are depicted for both mechanisms as the tag density increases. As analyzed in Section 7.4, the time required for retrieving reference tag IDs contributes the most in the overall handoff latency for both schemes.

Both Pure and Slotted Aloha variants are considered. Another time factor that should be also considered is the sensor communication in Scheme B, since the supported data rates are much lower compared to the WiFi channel. We observe that for a dense tag deployment (small inter-tag spacing), the reading time T T R and the time needed to send the Tag List messages T M N -AP are very high due to the big number of responding tags. As density decreases, however, they both improve due to the smaller number of detected tags and the size reduction of the Tag List messages (fewer bits), respectively. Comparing the Pure and Slotted Aloha we observe that Slotted Aloha has better performance, due to the reduction of the vulnerability period 2t [START_REF] Burdet | Rfid multiple access methods[END_REF]. Finally, we compare the time response of the prediction processes of both of our schemes with their equivalents of the Standard protocols. According to experimental results in [START_REF] Mishra | An empirical analysis of the ieee 802.11 mac layer handoff process[END_REF] the L2 discovery latency is between 58.74ms and 396.76ms and the movement detection delay is on average 36ms to 558ms when router Advertisements are broadcasted every 0.05s to 1.5s, according to [START_REF] Lee | Analysis of handoff delay for mobile ipv6[END_REF]. In our schemes for δ = 3, the total handoff delay is just around 60ms, which validates their performance superiority.

Conclusions and Future Directions

In the emerging pervasive communication era, several smart objects such as sensors and RFID tags will be deployed all around the user enabling coupling the physical environment with the computing applications. In this chapter, we extended the functionality of the sensor and RFID technologies by exploiting their properties for purposes other than simply sensing and item identification or tracking. More precisely, we presented how these technologies can also assist in improving network functionalities such as handoff management.

Two such schemes were proposed. The first one relies on a deployment of RFID passive tags for detecting the IP movement of a MN. The proposed mechanism predicts the MN's target PoA, so that it can pro-actively associate with it, i.e. before its physical disconnection from its current PoA. The main benefit of this solution is that it does not rely on the broadcast of Router Advertisement messages, hence achieving considerable waiting time and bandwidth savings. Moreover, being independent of the underlying wireless access technology (such as IEEE 802.11), it can offer mobility support over heterogeneous networks. The second scheme tries to reduce the energy consumption as well by controlling the RFID scanning operation and eliminating the need for sensing the WiFi channel during link layer handoff. For achieving this, a smart space with sensors, actuators and RFID tags is deployed for predicting the mobility of mobile devices with sensor and RFID reader capabilities.

The main consideration for both schemes is their feasibility due to their deployment requirements. However, in the context of the envisioned ambient intelligent environments where large numbers of everyday objects scattered all over will become smart, such solutions are entirely plausible. Moreover, our system design and configuration choices such as grid tag deployment, placement of sensors, etc. are not the core concepts but serve for the purpose of convenience in performance analysis and elaboration of the achieved benefits.

This was a preliminary study for validating the potential of pervasive intelligence in mobility management. Further performance analysis, such as packet loss for different types of traffic, or experimental validation need to be done. Despite the intensive research efforts over the last few decades towards successful indoor location systems, indoor localization still remains a challenging task. This is mainly attributed to the harshness of the indoor environments on signal propagation, caused by obstacles and frequent environmental changes. Moreover, most applications require solutions with minimum hardware and deployment costs or human intervention. Finally, compared with outdoor scenarios, scalability issues occur more frequently inside a building where there is higher tendency for coexistence of many users.

Handoff management has also received considerable attention but still there is no handoff scheme that satisfies all objectives for seamless and global roaming in future communication networks. With the growing popularity of the Internet and the high speed evolution of wireless access networks, novel applications emerge and become even more demanding in their QoS requirements. Some of the most common approaches rely on special characteristics of specific wireless access technologies which apparently limits their applicability and easy integration in heterogeneous networks.

In the following sections, we first summarize our main contributions towards the thesis objectives and later we discuss some possible future directions for extending our research study.

Contributions

Our approach for solving both problems of indoor localization and handoff management was motivated by the general context of the envisioned pervasive computing systems. It is generally accepted that in the near future an increasingly large numbers of everyday objects scattered throughout the surrounding environment will become smart by having some kind of simple computation and communication technology embedded into them, which will allow them to be connected to each other within local networks and, ultimately, connected to the Internet. Exploring whether and how several popular technologies, such as WLAN, RFID and WSANs, either alone or in combination, can aid tackling efficiently our two problems was actually the main core of this thesis.

The main contributions of this thesis include: ≤ Focusing on the case of WLAN fingerprinting location systems, we studied both deterministic and probabilistic cases and proposed the incorporation of simple additional modules in the positioning mechanism for mitigating the main shortcomings. Our main contributions include: considering orientation information and filtering incompatible RSSI samples during the training or the entire operation and a hierarchical pattern matching algorithm for selecting candidate locations from the training set during the run-time process. Additionally, the impact of two design parameters was discussed. More precisely, we suggested including RSS information from less APs and adapting the number of candidate locations depending on each user RSS characteristics. Numerical results based on real experimental evaluation of our proposals showed accuracy enhancement, especially for the deterministic case, without sacrificing considerably the time-efficiency of the localization process.

≤ The growing popularity of RFID for indoor localization but also the lack in the ≤ Considering the benefits but also the limitations of the standalone positioning solutions, we proposed an integration localization scheme that combines both WLAN and RFID technologies. The main advantage of multi-modal devices is the offered redundancy of communication channels. Thus, we suggested utilizing the WiFi channel for coordinating the communication in the RFID channel in order to subdue the restricting factor of reader collisions. Additionally, we considered a realistic scenario where users have devices with diverse capabilities. We proposed exploiting this diversity by benefitting from the inherent property of reader-enabled users to sense tag-enabled users in their vicinity, and hence refining their location estimation. The performance advantages of a conceptual coordination mechanism were first validated and later a feasible scheduling scheme was proposed for realizing these benefits. Based on extensive simulations, we tested the impact of various design parameters and we validated the achieved performance advantages of our proposals regarding both accuracy and time-efficiency objectives.

≤ For handoff management, we first focused on the movement detection step of the MIP network layer handoff mechanism and proposed a scheme that relies on a deployment of RFID passive tags for detecting the IP movement of a MN. The proposed mechanism predicts the MN's target PoA, so that it can pro-actively associate with it, i.e. before its physical disconnection from its current PoA. The main benefit of this solution is that it does not rely on the broadcast of Router Advertisement messages, hence achieving considerable waiting time and bandwidth savings. Moreover, being independent of the underlying wireless access technology (such as IEEE 802.11), it can offer mobility support over heterogeneous networks.

≤ Finally, considering also the problem of extensive energy consumption during the scanning process of the Standard link layer handoff mechanism, we proposed a second scheme for handoff management at both link and network layers. For achieving this, a smart space with sensors, actuators and RFID tags is deployed for predicting the mobility of mobile devices with sensor and RFID reader capabilities. The proposed scheme proved beneficial in terms of handoff latency, energy consumption, accuracy and bandwidth utilization.

Future Directions

Even though most of the objectives were well satisfied by our proposed systems, we believe that our research can go beyond for achieving further improvements. Some possible future directions may include:

≤ The design of novel localization algorithms which will take into account the specific characteristics of each technology or exploit their integration. ≤ The development of cooperative schemes which take advantage of possible interactions among users in a more sophisticated way appears promising and should certainly be studied more extensively.

≤ Studying different deployment and configuration schemes for both localization and handoff management is also interesting direction in order to decrease the cost of the systems.

≤ The handoff decision functions of our proposed schemes can easily incorporate more factors, such as user preferences, profile etc. However, we limited our study in simple decision functions in order to focus more on the achieved accuracy and not the potential flexibility. However, exploring more sophisticated decision functions should be studied especially for vertical handoff in heterogenous networks.

≤ Last but not least the experimental testing of our systems is an essential step for cross validating the simulation based performance evaluation results. Pour réaliser un tel environnement ubiquitaire, la connaissance de l'emplacement et de la gestion de la mobilité sont deux notions de base, tandis qu'une forte corrélation existe entre eux. Le besoin continu pour déterminer l'emplacement d'une entité inconnue découle de la capacité de la mobilité de cette entité tandis que les questions soulevées en raison de la gestion de la mobilité peuvent en bénéficier si les informations de localisation relative sont disponibles. Cette thèse a pour objectifs à améliorer les procédures de la localisation ≤ Ambient Assisted Living: les informations de positionnement précis est essentiel pour le succès de l'Ambient Assisted Living (AAL) du projet [START_REF]Ambinet Assisted Living project (AAL)[END_REF] qui vise à renforcer la vie quotidienne des personnes âgées et des personnes souffrant de handicaps, grâce à l'utilisation de l'information et des technologies de la communication (TIC).

≤ Person et Asset Tracking: le suivi des personnes à l'intérieur des bâtiments est essentiel dans les situations d'urgence comme les incendies, les tremblements de terre ou autres catastrophes. En outre, les systèmes de localisation en intérieur sont utiles dans les hôpitaux pour dépister des membres du personnel à tout moment, sans leur intervention, dans les musées ou les écoles pour garder la trace de la localisation des enfants [2].

Le suivi des objets est utile pour trouver la localisation d'équipements hospitaliers dans un hôpital, trouver des livres dans une bibliothèque ou des produits dans un entrepôt. L'emplacement de différentes ressources physiques telles que les imprimantes, les projets et les photocopieurs permet également aux applications de découvrir de ressources [3].

≤ Navigation: les informations de localisation à l'intérieur peuvent être utilisés pour 9.1 Les objectifs et Les défis 149 construire des outils de navigation dans les bâtiments peu familier [4], comme les aéroports, les gares, les musées, les campus, les grands magasins ou les bâtiments de grands bureaux.

≤ Location-Based Advertising and Social Networking:l e sm éthodes de localisation peuvent être utilisées pour les divertissements sélectives et orienté s [ 5 ]e td e fournir des informations sur les produits à l'intérieur de magasins [6]. De point de vu de l'application ou l'utilisateur, la provision de services peuvent mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de handoff, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit.

Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision de handoff qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation réussi à l'intérieur est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles liées à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de handoff dans des réseaux hétérogènes sans fil de futur est beaucoup plus difficile que dans les réseaux traditionnels homogènes. La procédure de handoff doit être transparente pour satisfaire la qualité de service requise par les applications de futur et leurs fonctionnalités, cela ne doit pas dépendre de la caractéristique de l'opération des technologies différentes. En outre, les décisions de handoff devraient être suffisamment souples pour tenir compte aux préférences des utilisateurs d'un large éventail de critères proposés par toutes les technologies.

L'objectif principal de cette thèse est de mettre au point précis, le temps et l'emplacement de puissance efficaces et la gestion de handoff afin de mieux satisfaire les applications sensible des utilisateurs en dépendent au contexte dans lequel les utilisateur se trouvent. Pour obtenir une localisation à l'intérieur, le potentiel de réseau sans fil local (WLAN) et Radio Frequency Identification (RFID) comme une technologie autonome pour détection de location sont d'abord ont été étudiés par des expérimentations de plusieurs algorithmes et paramètres dans des plateformes réels ou par de nombreuses simulations, alors que leurs lacunes ont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie a été validée par des analyses profondes sur leur performance si elles fonctionnent d'une manière automne (sans intégration). En ce qui concerne la tâche de gestion de handoff, nous identifions que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux types de systèmes qui utilisent l'information obtenue à partir des systèmes de localisation one été proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. La deuxième méthode repose sur une approche intégrée RFID et réseaux de capteurs / actionneur Network (WSAN) de déploiement pour la localisation physique des utilisateurs et par la suite pour prédire leur prochaine point de handoff aux niveaux des couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil sous-jacent, les deux régimes peuvent être facilement mises en oeuvre dans des réseaux hétérogènes.

L'évaluation de la performance démontre les avantages de nos méthodes proposées par rapport aux protocoles standards concernant l'exactitude de prévision, le temps de latence et l'économie d' énergie. Les mots clés : mobilité, localisation, gestion de handoff, communication des réseaux sans fil, architecture des réseaux hétérogènes, analyse de performance, WLAN, RFID, WSAN.

Introduction Chapter 1

 1 to improve the localization accuracy without increasing considerably the complexity and hardware requirements. Reference papers 1 include: -C1 A. Papapostolou and H. Chaouchi, WIFE: Wireless Indoor positioning based on Fingerprint Evaluation, in Proceeding of the 8th IFIP NETWORKING conference, Aachen, Germany, March 2009 [15]. -C2 A. Papapostolou and H. Chaouchi, Orientation -Based Radio Map Extensions for Improving Positioning System Accuracy, in Proceeding of the 6th ACM International Wireless Communications and Mobile Computing Conference (ACM IWCMC), Leipzig, Germany, June 2009 [16].

  . Papapostolou and H. Chaouchi, Considerations for RFID-based Indoor Simultaneous Tracking, in Proceedings of the 2nd Joint IFIP Wireless and Mobile

Chapter 1 -

 1 J3 A. Papapostolou and H. Chaouchi, Integrating RFID and WLAN for Indoor Positioning and IP Movement Detection journal of Wireless Networks (Springer), submitted in November 2009. ≤ Finally, in order to complete the viability of our proposed schemes, we tried to take into account their accompanied energy consumption. Motivated by this, we proposed a scheme combining the benefits of RFID and WSAN technologies for handoff improvement with respect to latency and energy consumption. Reference papers include: -C8 A. Papapostolou and H. Chaouchi, Deploying Wireless Sensor/Actuator Networks and RFID for Handoff Enhancement in Proceeding of the International Conference on Ambient Systems, Networks and Technologies (ACM ANT), Paris, France, November 2010 [25]. -J4 A. Papapostolou and H. Chaouchi, Handoff Management Schemes in Future Pervasive Environments, submitted to the journal of Mobile Networks (Springer), SI on Future Internet for Green and Pervasive Media of the journal of Mobile Networks and Applications (Springer), submitted in December 2010.

Figure 2 . 1 .

 21 The key concept is to utilize properties of Received Radio Signal (RRS) measurements from several Fixed Reference Points (FRPs) in order to infer the unknown location of the receiver. Initially, radio signals transmitted by the FRPs (such as Access Points or Base Stations) are sensed/measured by the RRS-sensing devices of the receiver and then converted to location-related signal metrics. The reported signal metrics are then processed by the positioning algorithm for estimating the unknown location of the receiver, which is finally utilized by the application. The accuracy of the signal metrics and the complexity of the positioning algorithm define the accuracy of the estimated location.

2. 1 29 Figure 2 . 1 :

 12921 Figure 2.1: Wireless Positioning System.

Figure 2 . 2 :

 22 Figure 2.2: Principle Positioning Techniques.

Figure 2 .

 2 Figure 2.2(c), the estimated location is the intersection point of the cycles with centers the reference points and radius the corresponding estimated distances from each one of them.

Figure 2 .

 2 2(b) illustrates this concept.

Figure 2 .

 2 Figure 2.3 depicts the general mechanism of scene analysis localization. Such methods require an offline phase for learning the radio characteristics in a specific area under study.Such radio characteristics may correspond to any radio signal metrics, either range-based or range-free; RSS is though the commonly selected metric. This signal information is then stored in a database called Radio Map. During the online localization phase, the receiver's unknown location is inferred based on the similarity between the Radio Map entries and the real-time RSS measurements. The similarity in signal space can be based either on pattern matching techniques (deterministic schemes) or on probability distributions (probabilistic schemes). The type of these features and the way they are represented define the accuracy and complexity of this positioning method. In general, deterministic scene analysis compared to the probabilistic case is simpler but less accurate way for discriminating among different area positions.

Figure 2 . 3 :

 23 Figure 2.3: Scene Analysis Positioning Technique.

Figure 3 .

 3 Figure 3.1 illustrates an overview of our proposed positioning methodology. It includes two main stages, namely the offline training stage and online location determination stage. During the offline phase, Joint Location and Orientation (JLO) -based calibration of the area under study is conducted, i.e. multiple RSSI samples measurements are obtained for different locations and orientations. These RSSI samples are processed by the RSS sample filter module, transformed to an appropriate format and finally stored in a database, called

Figure 3 . 1 :

 31 Figure 3.1: WLAN Fingerprinting Positioning Process Overview.

3. 4

 4 System Design Considerations 57 location still remains the main indicative one and thus, it should be prioritized during the RSS similarity testing. Finally, the phenomenon of location ambiguity, as addressed in section 3.2.3, would become more possible since the probability of two physically far located points having the same RSS characteristics would increase. In other words, both complexity and accuracy related reasons triggered us in proposing a two-level algorithm, called mk-NNSS, 1. Initially, we consider eq. (3.2) as the RM representation and the m closest matches are determined.

Figure 3 .

 3 Figure 3.2(a) illustrates the performance advantage in terms of MLE if JLO -based calibration is employed. Both metrics, prod and sum, (see eq. (3.7)), are examined for different numbers of NNs. We observe that even though for the metric prod the error

Figure 3 .

 3 Figure 3.2(b) shows that the performance is worse. This is because probabilistic schemes are based on the statistics of the samples which give the appropriate information concerning the reliability of the samples, something not possible in the deterministic schemes.

  schemes.

Figure 3 . 2 :

 32 Figure 3.2: Performance Analysis of Probabilistic Fingerprinting.

  Figure 3.3(b) shows the MLE for different values of the orientation threshold, Ω. The minimum MLE is achieved for high values of Ω, therefore, there is no need for additional hardware equipment during the actual runtime of the system, in contrast with the probabilistic case.

3. 5

 5 Experimental Evaluation 65 (a) Impact on the M L E after JL O -calibration and sample filtering for the deterministic scheme. (b) M L E versus orientation threshold for the deterministic scheme. O ptimal case w hen Ω > 300 o . (c) Impact on the M L E of the number of considered A P s for the deterministic scheme. (d) Impact on the M L E and O CT after hierarchical pattern matching for the tw o deterministic schemes.

Figure 3 . 3 :

 33 Figure 3.3: Performance Analysis of Deterministic Fingerprinting.

  This chapter focused on the case of indoor location determination based on the received signal intensity from the existing 802.11 wireless data network. Following the concept of fingerprinting technique, we studied both deterministic and probabilistic cases and proposed the incorporation of simple additional modules in the positioning mechanism for mitigating the shortcomings of WiFi-based localization. Our main contributions include: considering orientation information and filtering incompatible RSSI samples during the training or the entire operation and an hierarchical pattern matching algorithm for selecting candidate locations from the training set during the run-time process. Additionally, the impact of two design parameters was discussed. More precisely, we suggested including RSS information from less APs and adapting the number of candidate locations depending on each user RSS characteristics. Numerical results based on real experimental evaluation of our proposals showed accuracy enhancement, especially for the deterministic case, without sacrificing considerably the time-efficiency of the localization process. Chapter 4RFID Reader Localization R adio Frequency Identification (RFID) is a rapidly developing wireless technology with key features which anticipate its outstanding position in the upcoming era of pervasive computing. Even though object identification is its primary objective, it is generally accepted that RFID systems can revolutionize various commercial applications. A RFID system consists of three basic component types, readers, tags (passive or active), and servers[START_REF] Want | An Introduction to RFID Technology[END_REF]. Key properties of RFID, such as low cost and indefinite lifetime of passive tags, non line of sight requirement, simultaneous and fast reading of multiple tag IDs, resilience to environmental changes, reduced sensitivity regarding user orientation motivated the research over RFID-based positioning schemes. Correlating tag IDs with their location coordinates is the principle concept for their realization.

4. 1

 1 RFID Localization 69 second one is a communication interface, such as IEEE 802.11, for enabling communication with servers. Servers are back-end entities responsible for receiving and processing the information sent from the readers.Key benefits of RFID, such as low cost and indefinite lifetime of passive tags, non line of sight requirement, simultaneous and fast reading of multiple tag IDs, resilience to environmental changes, reduced sensitivity regarding user orientation, inspired the academia and industry for exploring its potentials in more intelligent applications, such as supply chain management, object or people tracking, real-time inventory, retail, anti-counterfeiting, baggage handling, and health-care[START_REF] Baudin | RFID applications in manufacturing[END_REF] and more recently for indoor localization.

4. 1

 1 .2 RFID Positioning and Systems RFID positioning systems can be broadly divided into two classes: tag and reader localization, depending on the RFID component type of the target.

4. 2

 2 Our Motivation: The Interference Problem in RFID 73 this tag simultaneously. Let R i and R j denote the read ranges of readers r i and r j and d ij their distance. Apparently, if

Figure 4 .

 4 Figure 4.1(a) depicts two readers r 1 and r 2 which transmit simultaneously query messages to a tag t 1 situated within their overlapping region. t 1 might not be able to read the query messages from neither r 1 nor r 2 due to interference.

  (a) M any R eaders-to-T ag Interference. (b) R eader-to-R eader Interference.

Figure 4 . 1 :

 41 Figure 4.1: Two types of interference in RFID.

Figure 4 .

 4 Figure 4.2 depicts the positioning system architecture. The reader embedded at each user device queries for reference tags within its coverage in order to retrieve their IDs. Then, the list of the retrieved tag IDs with the corresponding RSS levels is forwarded to the Location Server within a Tag List message. Based on the received Tag List messages and a repository which correlates the IDs of the reference tag with their location coordinates, the Location Server estimates the location for all users by employing a RFID-based positioning (see subsection 4.3.3) algorithm and finally returns the estimated locations back to the corresponding users in Location Estimate messages.The communication between the reader and the tags is done through the RF interface of the reader, whereas the communication between the reader and the server is possible through the communication interface of the reader, such as IEEE 802.11. Alternatively, assuming multi-mode devices, the Tag List and location estimation messages can be exchanged by the wireless interface of the user device.

Figure 4 . 2 :

 42 Figure 4.2: RFID-based Positioning System Architecture.

4. 4

 4 Simulation-based Performance Analysis 81 R max denotes the maximum read range of each reader which depends on the transmit power, the decoding threshold TH, antenna gains, propagation losses, interference and shadowing, as shown in eq. (4.4).The probability p t at a gt is not detected follows uniform distribution U (0,α), where a [0, 1] characterizes the interference degree from the material of the objects the reference tags are mounted on.Finally, for the communication between a reader (or wireless interface) and the Location Server, the IEEE 802.11b WLAN Standard[START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements -Part 11[END_REF] has been assumed with supported data rate R=2 Mbps and slot time t s =2 0 μs. We have assumed an ideal transmission channel in terms of noise and interference and that the only cause of packet loss is due to their collisions. Collision happens when multiple nodes try to access a shared link at the same time. For wireless links, the multiple access procedure follows the IEEE 802.11 CSMA (Carrier Sense Multiple Access) mechanism. Each node senses the carrier before its transmission. If the link is busy, the node waits for a random back-off period before trying to transmit again. This back-off time follows the equation: back-off time = CW ±t s , where CW represents the size of contention window in each node whose value is between CW min and CW max . CW min and CW max represent the minimum and maximum size of the contention window. After each collision, the contention window size is doubled and the back-off time is doubled accordingly. For the 802.11b: CW max = 1023 and CW min = 31.

. 15 ) 96 ±G u 2 ×10 6

 159626 For measuring the MLT the following time factors should be added:1. the time T TR needed for retrieving successfully all G u tags' IDs within range, given by eq. (4.1), 2. the time T R-S needed for sending successfully the Tag List message from the reader (or user terminal) to the server, 3. the processing time T pr of the positioning algorithm, which depends on its complexity, 4. the time T S-R needed for sending successfully the location estimation from the server to the reader (or user terminal).The times T R-S and T S-R include the transmission delay T tr , the collision delay T col for accessing the wireless medium and the propagation delay T prop . The transmission delay T tr depends on the message size in bits and the link rate R. For instance, the Tag List message includes mainly G u ± 96 bits. The timestamp and some additional control bits are ignored. Thus, T R-S tr → =48±G u μs.

Figure 4 .

 4 Figure 4.3 illustrates the dependency of the positioning accuracy on the inter-tag spacing δ and the three positioning algorithms when R max =3m in Figure 4.3(a) and R max =5m inFigure 4.3(b). The main observation is that for all cases, increasing the inter-tag spacing δ

  MLE vs δ when Rmax =3m. δ σ α (b) MLE vs δ when Rmax =5m.

Figure 4 . 3 :

 43 Figure 4.3: Impact of system design parameters on Accuracy for a single user.

Figure 4 .

 4 Figure 4.4(a) and Figure 4.4(b), respectively.First of all, we observe that Slotted Aloha has better performance than Pure Aloha, due to the reduction of the vulnerability period 2s[START_REF] Burdet | Rfid multiple access methods[END_REF]. In both figures, when the grid deployment is dense, the tag reading time is very high due to the big number of responding tags. Comparing the two cases of R max values, when R max =3 m less tags are within a reader's interrogation zone and thus, less reading time is required. Finally, recalling Figure4.3, we conclude that there is a trade-off between the accuracy and time response objectives, regarding the optimal value of δ. More tags provide more information for the location determination process but on the other hand more time is required for detecting them.

Figure 4 . 4 :

 44 Figure 4.4: Impact of system design parameters on Time Response.

Figure 4 .

 4 Figure 4.5 depicts the processing time T pr (specified in flops 1 ) of each positioning algorithm as the inter-tag spacing increases, for R max =3 m and R max =5 m in figures 4.5(a) and 4.5(b), respectively. The main observation is the high processing time of the Multi-Lateration approach for dense tag deployments. The most interesting remarks, however, can be made if Figure 4.3(b) is taken into account. The W-AVG approach has the best 1 The execution time of a program depends on the number of floating-point operations (FLOPs) involved.Every computer has a processor speed which can be defined in flops/sec. Knowing the processor speed and how many flops are needed to run a program gives us the computational time required: Time required (sec) = Number of FLOPs/Processor Speed (FLOP/sec)[START_REF] Canale | Numerical Methods for Engineers[END_REF].

4. 4

 4 Simulation-based Performance Analysis 85 performance if both objectives are considered. Moreover, for R max =5 m and δ =5 m, the accuracy of the ML technique is high without considerable processing cost. Therefore, more sophisticated techniques can alleviate the need for carefully designed systems. Processing time vs δ when Rmax =3m. Processing time vs δ when Rmax =5m.

Figure 4 . 5 :

 45 Figure 4.5: Impact of positioning algorithm on Time Response.

Figure 4 .

 4 6(b) depicts the MLE increase due to the interference rise from nonconductive materials as α increases. This factor is especially detrimental for the S-AVG and W-AVG algorithms, while the ML exhibits great tolerance. This is because in ML, detecting three tags is enough for accurate location estimation. Chapter 4 σ δ α (a) MLE vs shadowing variance σ. α δ σ (b) MLE vs interference level from materials α.

Figure 4 . 6 :

 46 Figure 4.6: Impact of environmental parameters on Accuracy for a single user.

Figure 4 .

 4 Figure 4.7 is the corresponding of Figure 4.3 but for ⌈ = 20 users whose positions need to be determined simultaneously, i.e. β = 1. Our remarks regarding the impact of the tag density δ and the positioning algorithms are validated for this case as well.

Figure 4 .

 4 Figure 4.8 is the corresponding of Figure 4.6 but for ⌈ = 20 users, δ =1 m instead of δ =2 m and R max =3 m instead of R max =5 m. The main observation is that the interference problem makes the deteriorating impact of both environmental factors on the accuracy even more harmful. The most interesting remark, however, concerns the behavior

  MLE vs δ when Rmax =5m

Figure 4 . 7 :

 47 Figure 4.7: Impact of system design parameters on Accuracy for multiple users.

  MLE vs interference level from materials α.

Figure 4 . 8 :

 48 Figure 4.8: Impact of environmental parameters on Accuracy for multiple users.

  MLE vs Number of users |U| when β =1.

  MLE vs Number of users |U| when β =0.

Figure 4 . 9 :

 49 Figure 4.9: Accuracy reduction due to users' increase and its potential alleviation.

  MLE vs read range Rmax when β =0.

Figure 4 . 10 :

 410 Figure 4.10: Accuracy reduction due to read range increase and its potential alleviation.

Figure 5 .

 5 Figure 5.1 illustrates the architecture of the proposed hybrid positioning system. It includes the WLAN infrastructure, the RFID reference tag deployment, the two user types, namely active and passive, and a central Location Server (LS) within the network which maintains a database correlating tag IDs with location coordinates of reference tags or with passive users' IDs.

Figure 5 . 1 :

 51 Figure 5.1: Hybrid Positioning Architecture

Figure 5 .

 5 Figure 5.2 depicts the general concept of our proposed positioning process.

Figure 5 . 2 :

 52 Figure 5.2: Conceptual Positioning Process.

Figure 5 .Figure 5 . 3 :

 553 Figure 5.3 illustrates the process of the proposed positioning approach. It includes two main phases, namely the Initial Phase (IP) and the Collision Compensation Phase (CCP), and five main tasks: (i) initial location estimation, (ii) collision diagnosis, (iii) clustering,

Figure 5 .

 5 Figure 5.4 illustrates our simulation environment, a rectangular area 50 ± 50 square meters where 9 WiFi APs provide data communication with maximum range 15 meters.The indoor log-distance path loss model, described in[START_REF] Rappaport | Wireless Communications: Principles and Practice[END_REF], has been selected to model the communication at the 802.11b (WiFi) channel, i.e.PL(d)=PL(do )+10n log d d o + X σ ,(5.2)where d the distance between transmitter (AP) and receiver (MN), PL(d o ) the free space path loss at reference distance d o , n the path loss exponent whose value depends on the frequency used, the surroundings and building type, and X σ is a zero-mean Gaussian random variable in dB having a standard deviation of σ dB . The variable X σ is called the shadow fading and is used to model the random nature of indoor signal propagation due to the effect of various environmental factors such as multipath, obstruction, orientation, etc.

2 [ 2

 22 (4.6)) operating frequency is 915 MHz, N =3 .6a n dσ 2 , 6]. To consider the factor of interference from the objects we Integrating WLAN and RFID for Localization Enhancement Chapter 5

Figure 5 . 4 :

 54 Figure 5.4: Simulation environment.

Figure 5 . 5 :

 55 Figure 5.5: Impact of reader population (N A ) increase on localization accuracy with and without synchronization.

Figures 5 .

 5 Figures 5.5(a) and 5.5(b) show the impact of increasing the readers' population on the MLE for active and passive users, respectively. We have assumed δ = 1 meter, R max = 3

Figures 5 .

 5 Figures 5.6(a) and 5.6(b) depict the impact of increasing the maximum read range on the accuracy for both user types. We have assumed δ = 1, N A = 40 and N P = 50. When all readers scan simultaneously for tag IDs, their coverage range is a compelling design parameter between the two user types, since for active users large values of R max increase the frequency of overlapped interrogation zones, whereas passive users have more chances

Figure 5 . 6 :

 56 Figure 5.6: Impact of read range (R max ) on localization accuracy with and without synchronization.

(Figure 5

 5 .7(a)) and MLT 2 (Figure 5.7(a)). TH CD is the threshold used by the collision 104 Integrating WLAN and RFID for Localization Enhancement Chapter 5 diagnosis test (Algorithm 2) to decide whether an active user should participate in the scheduled transmission phase or not. TH dist is the threshold used during the clustering step: if the estimated distance between two users is less than TH dist , then they are clustered into the same cluster of most probably inter-colliding nodes. δ (a) Mean Location Error. δ (b) Mean Localization Time.

Figure 5 . 7 :

 57 Figure 5.7: Impact of thresholds TH CD and TH dist on positioning performance.

Figure 5 . 8 :

 58 Figure 5.8: Grid tag deployment and Reader radiation pattern.

Figure 5 .

 5 Figure 5.9(b) is not high. The increased MLT for R max < 3m is because less users pass the collision diagnosis test since few tags are detected. Overall, R max =3 m or R max =3 .5mappear the optimal choices when all objectives are concerned.

Figure 5 . 9 :

 59 Figure 5.9: Impact of reader range (R max ) on positioning performance.

Figures 5 .Figure 5 . 10 :

 5510 Figures 5.11(a) and 5.11(b) demonstrate the impact of increasing the maximum read range on the MLE for active and passive users, respectively. The observations here are in accordance with our remarks in Figure 5.6. The reason of the bad MLE performance for passive users when R max =5m is because the high MLE for active users is propagated to their detected passive users. However, when CCP is employed with TH dist =2±(R max +2) and TH CD = 5 or TH CD = G , the MLE decreases for both user types. Finally, we illustrate the time overhead due to the round robbin (RR) readers' readings during the CCP. The MLT in terms of RR cycles ( D max ) is depicted as reader population (Figure 5.12(a)) or read range (Figure 5.12(b)) increases. Comparing them in conjunction with Figure 5.10 and Figure 5.11, respectively, we conclude that employing the collision

Figure 5 . 11 :Figure 5 . 12 :

 511512 Figure 5.11: Impact of read range (R max ) on accuracy (MLE)

  rapid growth of wireless access networks, the great advances in mobile computing and the overwhelming success of the Internet, a new communication paradigm has emerged, whereby mobile users require ubiquitous access to their services while roaming, preferably without interruption or degradation of their communication quality. One of the research challenges for next generation (NG) all-IP-based wireless and mobile systems is the design of intelligent mobility management techniques that take advantage of IP-based technologies to achieve global roaming among heterogeneity access

6. 1 IP Mobility Problem 113 Figure 6 . 1 :

 111361 Figure 6.1: Mobility Management Mechanism.

6. 2

 2 .0.1 Link Layer Handoff A Link Layer (LL) or layer 2 (L2) handoff (HO) occurs because the MN must establish a new physical connection to a new Access Point (AP). This is because, due to mobility, the received signal strength (RSS) or Signal to Noise Ratio (SNR) from the MN's current AP may decrease, causing degradation of their communication. Even though several protocols 6.2 Standard Handoff Protocols 115

Figure 6 . 2 :

 62 Figure 6.2: IEEE 802.11 handoff mechanism.

Figure 6 . 3 :

 63 Figure 6.3: Mobile IP handoff mechanism.

Figure 7 .

 7 Figure 7.1 illustrates the process and message exchange diagram of the proposed mechanism, during the real-time movement of a MN. Initially, the RFID reader r m of each MN m's device queries periodically (or on demand) for tags within its coverage in order to retrieve their IDs. The list of the retrieved IDs, denoted as G m , is then forwarded to the RFID-S in a Tag List message. The reading period, i.e. time interval between consecutive tag readings or equivalently the frequency of the Tag List updates, are system design parameters.

Figure 7 . 1 :

 71 Figure 7.1: Scheme A handoff mechanism.

3 7 . 3

 373 Handoff Decision function Similar to the information selected for constructing the PAM during the system training phase, defining the decision function for selecting the next PoA of MNs during the real-time phase can also be flexible and based on special preferences of the network designer. We design a simple decision function in order to focus our attention on the precision achieved by the RFID technology in predicting the next PoA. Thus, given the set G m of detected tag IDs of a MN m (information contained in the Tag List message) and the set of their best PoAs }ID t ,PoA t | , Ct G m (information obtained by looking up the database), each unique AP j is assigned a frequency f j equal to the number of tags in G m which have assigned this AP as their best PoA. Then, the AP j which appears most frequently (f j is maximum) is selected as the next PoA m of the MN m, i.e. PoA m = AP arg max j∈N Scheme B: RFID and WSAN for Handoff at Link and

Figure 7 .

 7 Figure 7.2 illustrates the system architecture, which consists of the WLAN infrastructure and the deployment of the RFID and WSANs at strategic points. Mobile nodes are multi-mode terminals equipped with RF transceiver, RFID reader and sensor.

Figure 7 . 2 :

 72 Figure 7.2: Scheme B system architecture.

7. 3

 3 Figure 7.3 depicts the message exchange time-diagram. It includes three phases: sensing, handoff prediction and handoff execution.

Figure 7 . 3 :

 73 Figure 7.3: Scheme B handoff mechanism.

  T S HO = T S L2 + T S L3 = (T D + T AU + T AS ) + (T M D + T AC + T BU ), where T D , T AU and T AS are the delays during the discovery, authentication and association steps of the IEEE 802.11 handoff process, while T M D , T AC and T BU count for the movement detection, address configuration and binding update steps of the MIP handoff process.

7. 4

 4 Theoretical Analysis 133 before the completion of the L2 handoff and even complete before it. Therefore, its latencyT AHO is given by TA HO = max}T L 2 , T A MD +T AC +T BU | (7.10)In our case, T A MD is given byT A MD =T TR +T MN-S + T dec +T S-MN (7.11)which includes the time needed for the MN's reader to scan all reference tags within its vicinity (T TR ), the time needed for transmitting the Tag List message from the MN to the RFID-S (T MN-S ), the processing time needed for choosing the next best PoA (T dec ) and the time needed for sending the Handoff Needed message from the RFID-S back to the MN (T S-MN ).

Figure 7 . 4 :

 74 Figure 7.4: Grid tag deployment and reader radiation pattern.

. 20 )

 20 The sensor communication includes the transmission of two Tag List messages and the reception of the Start Reader and HO Needed or Stop Reader messages at the beginning and end of the HO prediction phase, respectively. Assuming P WSAN Tx and P WSAN Rx the power consumed during data transmission and reception over the WSAN, respectively, the energy consumed during a single handoff prediction phase due to sensor communication is given by E pred,S = D pred D R 2T MN-AP P WSAN Tx +P WSAN Rx (T Start +T AP -MN ) , (7.21) where T Start the time spent to receive the Start Reader.

200m 2 .

 2 The WLAN consists of 11 APs deployed according to the cellular concept with R max =3 0 m, R saf e =2 0 m (for Scheme B) and distance between two adjacent APs 50m.

Figure 7 . 5 :

 75 Scheme B -V max = 0.5 m/s Scheme A -V max = 2 m/s Scheme B -V max = 2 m/s

Figure 7 . 6 :

 76 Scheme A -V max = 0.5 m/s

Figure 7 .

 7 Figure 7.6 depicts the accompanied energy consumption (in a base 10 logarithmic scale)

7. 6 Figure 7 . 7 :

 677 Figure 7.7: Time response of tag reading and sensor communication versus average inter-tag spacing.

Chapter 8 Conclusions

 8 T his thesis was motivated by the ultimate goal of next generation communication systems to enhance the experience users perceive from their network. Common approaches for achieving this goal include the development of context-aware applications and the provision of mobility support. The location of the user is indispensable part of the general term context, while mobility support mainly refers to the handoff process that is required for retaining an ongoing communication of the user during its movement among different networks.Within this context, we focused on the indoor localization and handoff management problems and we tried to tackle them more efficiently by taking advantage of the most prominent technologies in the current and future communication networks, that is Wireless Local Area Networks (WLANs), Radio Frequency Identification (RFID) and Wireless Sensor and Actuator Networks (WSANs).

8. 1

 1 Contributions 145 literature of studies regarding the effect of its interference problem on the localization performance motivated us in addressing this issue. After modeling the interference problem in RFID by considering its technology and communication specifications, we conducted extensive simulations for analyzing the performance of RFID in tracking single or multiple users, under different system configurations and environmental conditions. Numerical results encourage adopting RFID for localization but also indicate the essentiality of a careful system design in order to exploit its full potential, especially in highly populated environments.

  rapide des communications sans fil et des technologies de réseaux, les grandes avancées de l'informatique mobile et les appareils portables, et l'immense succès de l'Internet, une ère de communication révolutionnaire omniprésente et mobile est en train de devenir le successeur naturel des systèmes actuels de communication mobile. L'objectif de cette vision envahissante ou omniprésente est de créer une intelligence ambiante avec le concept de base de l'interaction entre l'homme avec son environnement et le but ultime amélioration de l'expérience utilisateur du réseau. Dans ce but, un nombre de plus en plus grande des objets du quotidien disséminés dans l'environnement va devenir à intelligentes en faisant une sorte de calcul simple et de la technologie de communication intégré en eux, qui leur permettra d'être reliés les uns aux autres au sein des réseaux locaux et, finalement, connecté à Internet. Les utilisateurs deviennent encore beaucoup plus exigeants par rapport à la connectivité et l'accès universel aux différentes applications n'importe où, n'importe quand, en utilisant la meilleure technologie d'une pléthore d'interfaces disponibles à l'avenir des terminaux mobiles mult-interfaces, et sans la nécessité d'une prise d'une conscience explicite de la communication sous-jacente et de la technologie de l'informatique.

D

  'autre part, l'emplacement orientér éseau social peut améliorer encore les services de l'Internet orienér éseau social tels que Facebook, Friendsters, MySpace, etc en permettant aux utilisateurs de former des groupes en fonction de leur préférence sociale et d'intérêt.≤ l'amélioration des performances du réseau: de les informations de localisation des utilisateurs peuvent également être exploitées pour améliorer la fonctionnalitée t la QoS dans les réseaux sans fil. Ces méthodes ont été proposées pour le contrôle d'accès basés sur la localisation[7], handoff basés sur la localisation et dans les réseaux ad hoc afin d'optimiser les algorithmes de routage et d' auto-configuration du réseau[START_REF] Bocquet | Using enhanced-tdoa measurement for indoor positioning[END_REF]. Un pas de plus, en combinant des données de positionnement avec les profils des utilisateurs pourrait considérablement faciliter la planification du réseau, l'équilibrage de charge, la mise en cache des informations proches de l'utilisateur, la gestion des ressources radio et de la conception d'autres méthodes d'amélioration des performances[START_REF] Prasithsangaree | On indoor position location with wireless lans[END_REF].Pour la réussite de ces applications, la conception d'une détermination d'un système de localisation précise et fiable est essentielle. La localisation sans fil, c.-à-d. l'estimation de la position en se basant sur des signaux radio (RS), a attiré une attention considérable dans le domaine des télécommunications et de la navigation. Le système de positionnement le plus connu est le système de positionnement global (GPS)[START_REF] Wellenhoff | Global Positions System: Theory and Practice[END_REF], qui se base sur la satellite et qui est une réussite pour le suivi des utilisateurs en environnements plein air.Toutefois, l'incapacité de signaux de satellite pour pénétrer dans les bâtiments de la cause échec complet du GPS dans les environnements intérieurs. Pour la dé t e c t i o nd el al o c a l isation intérieur, certain nombre d'autres technologies sans fil ont été proposées, telles que l'échographie infrarouge (IR), réseaux sans fil local (WLAN), la bande ultralarge (UWB), l'identification par radiofréquence (RFID), Bluetooth, réseaux de capteurs sans fil (WSN)[START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF]. Cependant, les canaux intcanauxa de la propagation radio souffrent d'une atténuation Thesis' French Version Chapitre 9sévère et une propagation de vu direct pour la propagation de signale entre l'émetteur et le récepteur[START_REF] Pahlavan | Wireless Information Networks[END_REF], ce qui rend le positionnement précis à l'intérieur très difficile.En outre, par rapport aux systèmes extérieurs, déterminer l'emplacement d'un utilisateur ou un dispositif intérieur d'un bâtiment est beaucoup plus difficile non seulement en raison de sa nature rude, mais aussi en raison de l'exigence de services à l'intérieur pour précision de plus en plus précis. La gestion de handoff est unea procédure pour main-tenirconnexion de l'utilisateur mobile active tout en changeant son point d'attachement de son réseau en raison de la mobilité. Dans l'avenir des réseaux omniprésents, plusieurs technologies sans fil hétérogènes seront disponibles et les utilisateurs ont un accès universels à la demande de meilleur connectivité parmi un large éventail d'applications lors de leurs déplacements. Pour l'intégration harmonisée de ces différentes technologies dans un cadre commun, la conception de systèmes intelligents de gestion de la mobilité est requise afin de permettre aux utilisateurs mobiles d'avoir une continuité de service sans interruption n'importe où, à tout moment, quelle que soit leur technologie d'accès. En outre, les méthodes de gestion de la mobilité devraient être en mesure de satisfaire les besoins des applications émergentes qui devient de plus en plus exigeants quant à leurs contraintes de QoS. Cependant, le temps de latence au cours de la procédure de handoff conduit à une dégradation des performances. Pour le cas de réseaux sans fil WLAN basé sur la norme d' IEEE 802.11, la procédure de handoff nécessite que le noeud mobile cherche régulièrement des points d'accès pour mieux associer, en balayant tous les canaux WLAN. Toutefois, ce processus fait perdre l'énergie et introduit la perte de paquets, vu que lors du balayage, le noeud mobile n'est pas en mesure d'être desservi par son actuel AP. Mobile IP [13] est un protocole de couche réseau pour la gestion de mobilité pour les réseaux IP. Il transmet les paquets aux utilisateurs mobiles qui sont loin de leurs réseaux à la maison en utilisant des tunnels IP dans IP. Le handoff en Mobile IP est composé d'une séquence d'étapes, dont l'un porte sur la détection d'un noeud mobile en mouvement vers le nouveau réseau. Toutefois, lorsque le noeud mobile subit le mouvement de détection, il est incapable de recevoir des paquets IP, ce qui entraîne une dégradation des performances supplémentaires.

9. 2 ≤

 2 Un Aperçu de la thèseLa connaissance de l'emplacement des utilisateurs et des objets dans un environnement intérieur et leur gestion de la mobilité à travers des réseaux hétérogènes sont considérés 9.2 Un Aperçu de la thèse 151 comme des jalons clés vers la réalisation des futurs réseaux de communication mobile. En outre, la forte corrélation entre ces mandats tâches d'instruction de leurs aspects en parallèle, au lieu de les considérer comme deux processus indépendants. Cette thè s ev i s el ed éveloppement de la localisationt et les systèmes de gestion de la mobilité avec objectifs de conception principaux: ≤ Précision. Sachant exactement où quelqu'un ou quelque chose se déplace vers peut améliorer l'expérience utilisateur par la prestation de services personnalisés et d'améliorer le réseau fonctionnalité. ≤ Temps de réponse rapide. Les nouvelles applications seront plus exigeantes en termes de exigences de qualité de service, poussant pour la localisation rapide et systèmes de transfert. ≤ Extensibilité.L a p r ésence de nombreux utilisateurs ne doivent pas dégrader les performances du système. ≤ Handoff Générique. La coexistence de réseaux hétérogènes dans lequel les utilisateurs peuvent se déplacer, faire des handoffs intercellulaires indépendamment de la technologie des solutions les plus viables. ≤ L'orientation énergie. Comme les appareils mobiles sont contraints de consommation d'énergie de la batterie, cette question devraient être prise en compte. Les systèmes de communication sont envisagées à l'avenir pour offrir une connectivité hétérogènes et omniprésente , permettant aux utilisateurs mobiles qui seront entourés par des technologies différentes mais complémentaires, de capturer leurs différents besoins et exigences. Motivé par cette observation, explorer les synergies possibles et les interactions entre plusieurs technologies sont notre principale approche afin de faire face plus efficacement nos objectifs. Pour connaître la détection de la localisation à l'intérieur, nous avons concentré notre attention sur deux technologies sans fil; WLAN et RFID. WLAN, tels que IEEE 802.11, est considéré comme prometteur car il offre un faible coût et une solution fiable en raison de sa disponibilité dans la plupart des environnements intérieurs et la capacité de coordination de la communication en mode infrastructure. Toutefois, la précision est très affectée en présence de trajets multiples et de graves changements environnementaux. Plus récemment, la RFID a émergé comme une technologie intéressante pour une localisation précise deThesis' French Version Chapitre 9 détection en raison du faible coût des étiquettes passives, la lecture rapide des étiquettes multiples, la vue directe (LOS), le rend moins sensible à l'orientation des utilisateurs. Cependant, la principale lacune de la RFID est considérée comme le problème d'interférence entre ses composantes, principalement en raison de les capacités limitées des étiquettes passives et l'incapacité de communication directe entre lecteurs [14]. Afin de surmonter les limites des deux technologies, nous avons proposé une architecture d'intégration pour améliorer la performance de localisation. En ce qui concerne le problème de gestion de la mobilité, nous nous sommes concentrés sur le volet handoff et nous avons exploré le potentiel de deux technologies populaires omniprésentes: RFID et WSAN, pour fournir une solution rapide de handoff dans le cas de la mobilitéà Internet via un accès Internet / WiFi réseau. Toutefois, nos méthodes proposées peuvent être appliqués pour la liaison de différents et niveau du réseau pour les scénarios de mobilité, ce qui les rend des solutions viables dans les réseaux hétérogènes. 9.3 Le cheminement de travail et les contributions Pour atteindre les buts et objectifs de cette thèse, les étapes successives ont été suivies dans un ordre chronologique: Au départ, nous nous sommes concentrés sur l'approche de positionnement dans les réseaux WLAN qui se base sur les empreintes digitales. Cette méthode est considéré comme le plus populaire pour la localisation en intérieur à faible coût. Considérant à la fois son variantes déterministe et probabiliste, nous avons proposé quelques techniques simples pour améliorer la précision de localisation sans augmenter considérablement la complexité les besoins en matériel et. Les references suivantes comprennet nos travaux dans cette direction: -C1 A. Papapostolou and H. Chaouchi, WIFE: Wireless Indoor positioning based on Fingerprint Evaluation, in Proceeding of the 8th IFIP NETWORKING conference, Aachen, Germany, March 2009 [15]. -C2 A. Papapostolou and H. Chaouchi, Orientation -Based Radio Map Extensions for Improving Positioning System Accuracy, in Proceeding of the 6th ACM International Wireless Communications and Mobile Computing Conference (ACM IWCMC), Leipzig, Germany, June 2009 [16].

9. 3 ≤≤ 9 IEEE≤

 39 Le cheminement de travail et les contributions 153 -J1 A. Papapostolou and H. Chaouchi, Scene Analysis Indoor Positioning Enhancements, Annals of Telecommunications journal, October 2010 [17]. Le positionnement RFID est considéré comme une autre solution intéressante pour la localitéd ed étection avec une plus grande précision que celle d systèmes WLAN. Cependant, peu d'attention a étéd o n n ée au problème de collision, dans la mesure où le positionnement est concerné, qui est le talon d'Achille de la technologie RFID. Par conséquent, nous avons étudié longuement la performance de la plupart des algorithmes populaires de positionnement de RFID en présence de plusieurs utilisateurs. Les références suivantes montre nos contributions dans ce domaine: -C3 A. Papapostolou and H. Chaouchi, Considerations for RFID-based Indoor Simultaneous Tracking, in Proceedings of the 2nd Joint IFIP Wireless and Mobile Networking Conference, Gdansk, Polland, September 2009 [18]. -J2 A. Papapostolou and H. Chaouchi, RFID-assisted Indoor Localization and the Impact of Interference on its Performance, in the SI on RFID Technology, Systems, and Applications of the Journal of Network and Computer Applications (Elsevier), April 2010 [19]. Motivé par les avantages mais aussi les limites des solutions autonomes, comme identifiés dans les étapes précédentes, une architecture d'intégration combinant à la fois WLAN et les technologies RFID a alors été proposée. L'idée principale est de profiter de la précision de localisation offerts par le déploiement de la RFID et la capacitéd e coordination de l'infrastructure WLAN pour minimiser des problèmes liés à collision sur la canal RFID avec l'objectif ultime d'améliorer la précision de la localisation d'une manière efficace le temps. No travaux dans ce domaines sont représentés par les références suivantes: -C4 A. Papapostolou and H. Chaouchi, Simulation-based Analysis for a Heterogeneous Indoor Localization Scheme, in Proceedings of the 7th IEEE Consumer Communication and Networking Conference (IEEE CCNC), Las Vegas, Nevada, January 2010 [20]. -C5 A. Papapostolou and H. Chaouchi,Exploiting Multi-modality and Diversity for Localization Enhancement: WiFi and RFID usecase, in Proceedings of the 20th Thesis' French Version Chapitre International symposium on Personal Indoor and Mobile Radio Communications (IEEE PIMRC), Tokyo, Japan, September 2009 [21]. ≤ L'étape suivantes a été motivé par le constat que une architecture qui combine WLAN et RFID pourrait être également utilisé pour la gestion de la mobilité. Ayant comme but l'amélioration de handoff au niveau de couche réseau, nous avons proposé d'utiliser le déploiement de la RFID afin de minimiser le délai dans la procédure de gestion en utilisant IP mobile dans la phase de détection de mouvement dans le canal WiFi. Toutefois, il est digne de mentionner que l'architecture proposée est également valable dans d'autres réseaux de mobilité. Nos références comprennent: -C6 A. Papapostolou and H. Chaouchi, RFID-assisted Movement Detection Improvement in IP Mobility, in Proceedings of the 3rd IFIP International Conference on New Technologies, Mobility and Security (IFIP NTMS), Cairo, Egypt, December, 2009 [22]. -C7 A. Papapostolou and H. Chaouchi, Handoff Management relying on RFID Technology, in Proceedings of the IEEE Wireless Communication and Networking Conference (IEEE WCNC), Sydney, Australia, April 2010 [23]. Dans la séquence de l'architecture du système unifié pour la localisation et la mobilité la gestion a été conçu et analysé dans: -B1 A. Papapostolou and H. Chaouchi, RFID Deployment for Location and Mobility Management on the Internet, in H. Chaouchi (ed), The Internet of Things: Connecting Objects, Wiley, John & Sons, May 2010 [24]. -J3 A. Papapostolou and H. Chaouchi, Integrating RFID and WLAN for Indoor Positioning and IP Movement Detection journal of Wireless Networks (Springer), submitted in November 2009. ≤ Enfin, pour compléter la viabilité de nos systèmes proposés, nous avons essayéd e prendre en compte leur consommation d'énergie accompagnée. Motivé par cette idée, nous avons proposé une méthode qui combine les avantages de la RFID et les technologies de handoff dans le WSAN pour l'amélioration par rapport àlar éduction de latence et de l'énergie. Nos références comprennent: 9.4 L'Organisation de la thèse 155 -C8 A. Papapostolou and H. Chaouchi, Deploying Wireless Sensor/Actuator Networks and RFID for Handoff Enhancement in Proceeding of the International Conference on Ambient Systems, Networks and Technologies (ACM ANT), Paris, France, November 2010 [25]. -J4 A. Papapostolou and H. Chaouchi, Handoff Management Schemes in Future Pervasive Environments, submitted to the journal of Mobile Networks (Springer), SI on Future Internet for Green and Pervasive Media of the journal of Mobile Networks and Applications (Springer), submitted in December 2010. 9.4 L'Organisation de la thèse Le reste de cette thèse est organisé comme suit. Afin de faciliter sa présentation, nous l'avons divisé en deux parties: la première partie est notée à la localisation, alors que la deuxième partie se concentre sur les aspects gestion de la mobilité. Le premier chapitre de deux parties, (chapitres 2 et 6), comprendra des renseignements généraux et les travaux connexes essentielles à la compréhension et la mise en évidence des nos contributions. Le chapitre 3 décrit nos méthodes proposées pour améliorer la précision de la localisation des empreintes digitales dans le WLAN, le chapitre 4 étudie les performances de la RFID lorsque le problème des collisions entre en lieu et le chapitre 5 termine la première partie en décrivant un système hétérogène qui combine les avantages des deux technologies. Le chapitre 7 décrit et compare les deux méthodes que nous proposons pour la gestion de la mobilité. Enfin le chapitre 8 résume nos conclusions principales, les réalisations et les questions en suspens pour la recherche future.

Table of contents

 of 2.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 WLAN Scene Analysis Localization 3.1 WLAN Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 WLAN Technology Overview . . . . . . . . . . . . . . . . . . . . . . 3.1.2 WLAN Positioning Systems . . . . . . . . . . . . . . . . . . . . . . . 3.2 Our Motivation: WLAN Scene-Analysis Positioning Challenges . . . . . . . 3.2.1 Multi-path and Shadowing . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Orientation-dependency of the Radio Scene . . . . . . . . . . . . . . 3.2.3 Location Ambiguity . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Operational Processing Time . . . . . . . . . . . . . . . . . . . . . . 3.3 Proposed Enhanced Localization Approaches . . . . . . . . . . . . . . . . . 3.3.1 General Architecture and Methodology Overview . . . . . . . . . . . 3.3.2 Joint Location and Orientation (JLO) based Calibration . . . . . . . 3.3.3 Sample Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Radio Map Representation and Current Orientation . . . . . . . . . .3.6 NNSS Algorithm for Location Estimation . . . . . . . . . . . . . . . 3.4 System Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison with other Systems . . . . . . . . . . . . . . . . . . . . . 3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

	Chapter 1

3.3.5 Positioning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5.1 Deterministic . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5.2 Probabilistic . . . . . . . . . . . . . . . . . . . . . . . . . . 33.4.1 Number of Access Points . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Number of Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . 3.4.2.1 Parameter m . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2.2 Parameter k . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 3.5.1 Experimental Testbed and Data . . . . . . . . . . . . . . . . . . . . 3.5.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3.1 Probabilistic . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3.2 Deterministic . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4

Table 3 .

 3

		1: Comparison with other systems
	System	MLE WE OCT Type	Compass
						offline online
	OPT-D, k = 7	1.41	4.10 486	Det.
	OPT-D, k : ad. 1.37	3.83 446	Det.
	OPT-P, sum	1.94	3.93 332	Prob.
	RADAR	2.26	15	166	Det.
	COMPASS	1.65	11	332	Prob.

Table 4 .

 4 1: RFID Localization systems.

	System	Target Deployment	Approach	Accuracy
	SpotOn [54]	Tag	Readers	RSS trilateration	3 m
	Landmarc [55]	Tag	Readers and Tags RSS Scene Analysis	1 -2 m
	Simplex passive[74] Tag	Readers and Tags RSS proximity and optimization	0.3 -3 ft
	LPM [75]	Tag	Readers and Tags TDoA weighted mean squares	-
	Kalman [76]	Tag	Readers and Tags RSS mean squares and Kalman filtering 0.5 -5 m
	Lee [77]	Reader Tags (dense)	RSS Proximity	0.026 m
	Han [78]	Reader Tags (dense)	Training and RSS Proximity	0.016 m
	SVM [79]	Reader Tags	RSS Scene Analysis	80%
	Bayesian [80]	Reader Tags	Proximity and Bayesian Inference	1.5 m

Table 4 .

 4 1 summarizes the main characteristics of the above systems. In this chapter, we focus on the second type of positioning schemes, i.e. reader localization systems, because they are easier to implement as low cost passive tags can be deployed in a large extent in most indoor environments. Additionally, it is anticipated that future mobile terminals will have a reader extension capability for gaining access at a wide range of innovative applications and services supported by RFID systems.

	4.2 Our Motivation: The Interference Problem in RFID	71
	4.2 Our Motivation: The Interference Problem in RFID
	Selecting a best scheme is apparently not trivial since this depends on several factors
	such as deployment cost, processing requirements, time and power constraints, scalability
	issues etc.

The goal of this chapter is definitely not proposing a novel positioning algorithm. Actually, our motivation stems from the lack in the literature of a research study regarding the impact of the interference problem, which is persistent in RFID, on the localization performance. Even though RFID is a promising technology for localization, the interference problem should be extensively studied before the development of RFID-based localizers.

Table 4 .

 4 2 summarizes the simulation parameters with their default and varied values during the performed simulations.

	Table 4.2: Simulation Parameters
	Parameter	Symbol Default	Varied Range
	Area size [m 2 ]	L ± W	50 ± 50	-
	Inter-tag spacing [m]	δ	1, 2	[1, 5]
	# of users	⌈	1, 20, 40 [1, 50]
	RFID frequency [MHz]	f	915	-
	PL shadowing variance [dB] σ	3.3[ 2 , 6]
	Path loss exponent	N	3.6-
	Read range [m]	R max	3, 5	[3, 5]
	Degree of Irregularity	doi	0.3	-
	Tag response rate	λ	30	-
	# slots/transmission time	K	5-
	Slot duration [ms]	s	0.90	-
	Reader scan prob.	p scan	1	U (β, 1) : β [0, 1]
	Tag t non-detection prob.	p t	0.1	U (0,α):α [0, 1]
	WLAN link rate (Mbps)	R	2
	Propagation delay (μs)T prop	1

Table 4 .

 4 3: System Design Guide.

	4.5 Chapter Summary

(a) MLE vs read range Rmax when β =1.

  Based on the received Tag List messages, a database called PAM (see section 7.2.2.2) and a well defined decision function (see section 7.2.2.3), the RFID-S predicts the most suitable PoA with which the MN m most probably will associate, i.e. P oA m , after the L2 handoff. If the selected next PoA is different from the current PoA of the MN, the RFID-S sends a Handoff Needed message to that MN, which contains information required for the new CoA acquisition. Hence, the Movement Detection step in our proposal does not rely on Router Advertisements or Router Solicitations messages which add to the handoff delay and consume valuable bandwidth. Upon successful association to the target PoA (if different from the current PoA), the MN can configure a new CoA using the IP prefix included in the Handoff Needed message and immediately send a Binding Update message to its Home Agent (HA).

	7.2 Scheme A: RFID-assisted Network Movement Detection	125

Table 7 .

 7

		1: PAM Database format.
	# Tag ID Location Best PoA
	1	0000...	(x 1 ,y 1 )	AP 1
	...	...	...	...
	t	0101...	(x t ,y t )	AP j
	...	...	...	...
	U	1111... (x |T | ,y |T | )	AP |N |

Table ,

 , the MN's position can be estimated as the weighted average of the location coordinates (x t ,y t )Ct U i , with weighting factor w t depending on the signal strength of tag t's

	response, i.e.
	X i , Y i =	t∈Ti w t ×x t t∈Ti w t	, t∈Ti w t ×y t t∈Ti w t	.	(7.4)

  In our second proposal, the L2 discovery and L3 movement detection phases are replaced by the handoff prediction phase performed by the RFID and WSAN deployment. Therefore, the actual handoff latency T B HO is given by T B HO =T PRED + max}T AU +T AS , T AC +T BU | , (7.14) where T PRED is the time taken from the latest MN's Tag List report until the handoff initiation. With the aid of Figure 7.3, T PRED can be calculated by adding the following factors T PRED =T TR +T MN-AP +T C +T AP -AP +T AP -MN , (7.15)

	7.4 Theoretical Analysis			135
	model	T TR =N± t TR =N± t s 1+(e xG A 1)a +	1 Nλ	.	(7.13)
	7.4.1.3 Scheme B

  PL(d o) the free space path loss at reference distance d o , n the path loss exponent whose value depends on the frequency used, the surroundings and building type, and X σ is a zero-mean Gaussian random variable in dB having a standard deviation of σ dB . The variable X σ is called the shadow fading and is used to model the random nature of indoor signal propagation due to the effect of various environmental factors such as multipath, obstruction, orientation, etc.

	Location-aware Mobility Management	Chapter 7
	All APs are identical and follow the 802.11b (WiFi) standard with operating frequency at
	2.4GHz. Heterogeneous and alternative radio technologies could have been assumed since
	the proposed mechanisms do not rely on triggers from lower layers. The indoor log-distance
	path loss model, described in [33], has been selected to model the communication at the
	802.11b channel
	PL(d)=PL(d o )+10n log	d d o	+ X σ ,	(7.23)
	where d the distance between transmitter (AP) and receiver (MN),

This path loss model is used for calculating the RSS from each AP, based on its transmit power P t , i.e. RSS(d)=P t PL(d).

  Localisation en Intérieur et Gestion de la Mobilité dans les Réseaux Sans Fils Hétérogènes ÉmergentsRésumé Au cours de ces dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseaux sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau.

	IP Internet Protocol RR Round Robin
	ISO International Standards Organization RRS Received Radio Signal
	LAN Local Area Network RS Radio Signals
	LBS Location Based Services RSS Received Signal Strength
	LF Low Frequency RSSI Received Signal Strength Indicator
	LL Link Layer RWP Random Way Point
	LOS Line Of Sight SNR Signal to Noise Ratio
	LS Least Squares SA Slotted Aloha
	MAC Medium Access Control S-AVG Simple Average
	MANET Mobile Ad Hoc Networks SELFLOC SELective Fusion LOCation
	MD Movement Detection STA mobile STAtion
	MF Matched Filter SVM Support Vector Machine
	MIP Mobile IP TCP Transmission Control Protocol
	ML Multi-Lateration TDoA Time Difference of Arrival
	MLE Mean Location Error TMI Triangulation, Mapping and Interpolation
	MLT Mean Localization Time ToA Time of Arrival
	MM Mobility Management UHF Ultra High Frequency
	MN Mobile Node UWB UltraWideBand
	NIC Network Interface Card WE Worst Error
	NL Network Layer WiFi Wireless Fidelity
	NLOS Non Line Of Sight W-AVG Weighted Average
	NN Nearest Neighbor WMN Wireless Mesh Network
	NNSS Nearest Neighbor in Signal Space WSAN Wireless Sensor and Actuator Network
	PA Pure Aloha WSN Wireless Sensor Network
	PAM Point of Attachment Map WLAN Wireless Local Area Networks
	PDA Personal Digital Assistan
	PHY PHYsical
	PM Pattern Matching
	PoA Point of Attachment
	QoS Quality of Service
	RF Radio Frequency
	RFC Request For Comments
	RFID Radio Frequency IDentification
	RM Radio Map

Si can be any of the S A i , S B i or S C i RM representations

A design parameter to be discussed in section

3.4.1

In IEEE 802.11 only one AP is in charge of a MN. We have assumed that MNs within the same cluster are served by the same AP and that time synchronization among WiFi-enabled devices is possible when they operate in infrastructure mode through normal beacon messages broadcasted periodically by the APs.

W e focus only on the T CCP w hich is the additional time overhead.

The average scanning period depends on the threshold THHO; the higher its value the smaller the scanning period.

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