Mohamad Yassin

Elias Rachid

Rony Nasrallah

Performance Comparison of Positioning Techniques in Wi-Fi Networks

Keywords: Wi-Fi positioning, RSSI fingerprinting, Location Based Services, geolocation

The wide deployment of Wi-Fi networks empowers the implementation of numerous applications such as Wi-Fi positioning, Location Based Services (LBS), wireless intrusion detection and real-time tracking. Many techniques are used to estimate Wi-Fi client position. Some of them are based on the Time or Angle of Arrival (ToA or AoA), while others use signal power measurements and fingerprinting. All these techniques require the reception of multiple wireless signals to provide enough data for solving the localization problem. In this paper, we describe the major techniques used for positioning in Wi-Fi networks. Real experiments are done to compare the accuracy of methods that use signal power measurement and Received Signal Strength Indication (RSSI) fingerprinting to estimate client position. Moreover, we investigate a fingerprinting method constrained by distance information to improve positioning accuracy. Localization techniques are more accurate when the estimated client positions are closer to the real geographical positions. Accuracy improvements increase user satisfaction, and make the localization services more robust and efficient.

I. INTRODUCTION

The recent development of wireless technology, the increasing need for mobile data and the large diversification of wireless devices have led to a wide deployment of Wireless Local Area Networks (WLAN). IEEE 802.11 [START_REF] Matthew | 11 Wireless Networks: The Definitive Guide[END_REF] (commonly known as Wi-Fi) is a widespread Radio Access Technology (RAT) used in wireless networks. It offers mobile users the ability to access the network while they are moving, and to use the various available services via a wireless connection.

The traditional services offered by a Wi-Fi network are: providing wireless internet access for users, allowing interconnectivity between multiple Wi-Fi enabled devices that are present in the same network and connecting distant wired networks via a wireless connection. However, Wi-Fi networks also allow the introduction of numerous Location Based Services [START_REF] Mohapatra | Survey of Location Based Wireless Services[END_REF]. These services are based on the knowledge of mobile user position. They have many innovative applications that are useful for the service providers and for the mobile clients.

Many techniques are used to estimate mobile user geographical position in wireless networks. Time of Arrival (ToA), Time Difference of Arrival (TDoA) [START_REF] Cong | Hybrid TDoA/AoA Mobile User Location for Wideband CDMA Cellular Systems[END_REF][START_REF] Shin | Comparisons of Error Characteristics Between ToA and TDoA Positioning[END_REF] and Received Signal Strength (RSSI) are techniques that offer range measurements to solve the positioning problem [START_REF] Ji | Robust Sensor Localization Algorithm in Wireless Ad-Hoc Sensor Networks[END_REF]. These measurements are converted into distance information that helps localizing the mobiles.

Positioning in cellular networks [START_REF] Kos | Mobile User Positioning in GSM/UMTS Cellular Networks[END_REF][START_REF] Adusei | Mobile Positioning Technologies in CellularNetworks: an Evaluation of their Performance Metrics[END_REF] has the advantage of the previous knowledge of base stations deployment and geographical positions. However, localization accuracy is restrained by the multipath and non-line-of-sight (NLOS) propagation. In Cell-ID [START_REF] Trevisani | Cell-ID Location Technique Limits and Benefits: an Experimental Study[END_REF] localization technique, each mobile estimates its actual position using the geographical coordinates of the serving base station. Other methods make use of the Received Signal Strength (RSS) which is the reception power of the beacon signals transmitted by the base stations [START_REF] Deblauwe | Cellular Positioning: from Received Signal Strength Measurements to the Algorithm[END_REF][START_REF] Huang | A Signal-Aware Fingerprinting-Based Positioning Technique in Cellular Networks[END_REF].

Criteria used for localization techniques performance evaluation are: accuracy and cost. They are more accurate when the estimated position is closer to the real geographical position. However, the cost of each technique depends on the number of network nodes (wireless access points) to be involved in the problem, the signaling traffic needed and the processing load of the localization algorithm. Hence, the performance of a localization technique is a compromise between its accuracy and its cost. Other performance metrics [START_REF] Adusei | Mobile Positioning Technologies in CellularNetworks: an Evaluation of their Performance Metrics[END_REF] are: reliability, latency and applicability.

Localization techniques in WLAN have many applications. These applications are known as Location Based Services (LBS), and they can be classified [START_REF] Mohapatra | Survey of Location Based Wireless Services[END_REF] as emergency services (i.e., security alerts, public safety), informational services (such as news, sports, stocks, etc.), tracking services (like asset/fleet/logistic monitoring or person tracking) and entertainment services (for example: locating a friend and gaming). Specific applications include [START_REF] Peng | A Survey of Location Based Service for Galileo System[END_REF] environment monitoring, support to people with disabilities, consumer protection, meteorology, science, timing, etc.

In this paper, we describe the main techniques used for locating mobile clients in wireless networks. Then we choose techniques based on signal power measurement and Received Signal Strength Indication fingerprinting used for positioning in Wi-Fi networks. In order to compare the performance of these positioning techniques, real experiments are done. We track the movements of a Wi-Fi client in a geographical area covered by three access points of the IEEE 802.11 technology. Localization techniques are more accurate when the estimated client positions are closer to the real geographical positions. Hence, localization accuracy is the main parameter used for performance comparison. An accurate positioning technique in Wi-Fi networks increases user satisfaction, and makes the localization services more robust and efficient.

The rest of the paper is organized as follows: we describe in section (II) the different positioning techniques used in wireless networks. We specify the Wi-Fi localization techniques to be compared in this paper in section (III). Section (IV) contains details about the experiments we have made. Experiments results and positioning techniques performance comparison are reported in section (V), and we conclude in section (VI).

II. RELATED WORK

Positioning techniques exist in satellite-based networks (e.g. Global Positioning System, GPS), in mobile networks (such as Global System for Mobile communications, GSM) and in Wireless Local Area Networks (like Wi-Fi and Bluetooth).

GPS satellites continuously broadcast their own position, speed and direction. Therefore, each GPS receiver can calculate its own position via trilateration [START_REF] Fang | Trilateration and Extension to Global Positioning System Navigation[END_REF]. Receivers position is the intersection of the spheres determined by the distances between the mobile device and each satellite. Localization accuracy depends on the number of visible satellites. Hence, satellite-based positioning does not work well for indoor localization, and even outdoor when facing obstacles such as high buildings.

Assisted-GPS (A-GPS) is introduced to overcome limitations of GPS. Each Base Transceiver Station (BTS) of the mobile network is equipped with GPS receivers. A-GPS exploits information about BTS geographical position as well as GPS localization information. However, it requires the upgrade of mobile stations and base transceiver stations.

Cell Identification (Cell-ID) [START_REF] Trevisani | Cell-ID Location Technique Limits and Benefits: an Experimental Study[END_REF] is one of the positioning techniques used in mobile networks where each base station broadcasts both the Location Area Identifier and the Cell-ID to the clients. Having the Cell-ID, each mobile can approximate its position via the geographical coordinates of the corresponding base station.

Triangulation [START_REF] Peneda | Trilateration for Indoors Positioning Within the Framework of Wireless Communications[END_REF] is a positioning technique that exploits the geometry of triangles to locate the mobile clients. It uses the Angle of Arrival (AoA) [START_REF] Pages-Zamora | Closed-Form Solution for Positioning Based on Angle of Arrival Measurements[END_REF] measurements of several radio links to estimate the communication angle between the base station and the mobile.

In the context of mobile networks, ToA is a time-based technique that evaluates the differences between bursts sent by a mobile. These measurements are converted into distance, and client position is estimated using trilateration. However, ToA requires synchronization between mobiles and base stations. It also depends on the propagation environment which is the main source of error [START_REF] Deng | An AoA Assisted ToA Positioning System[END_REF].

The position of a mobile is also estimated via trilateration in the Enhanced Observed Time Difference (E-OTD) localization technique [START_REF] Ciurana | WLAN Indoor Positioning Based on ToA with Two Reference Points[END_REF]. The mobile compares the relative times of arrival of bursts transmitted by nearby base stations to calculate its distance from each visible station. E-OTD requires synchronization between mobiles and base stations.

In the next section, we classify and describe the different localization techniques used in Wi-Fi networks.

III. WI-FI POSITIONING TECHNIQUES

A. Classification of Wi-Fi positioning techniques

We classify positioning techniques used in Wi-Fi networks into four main categories: cell identification (Cell-ID), time, RSSI and angle. Methods based on cell identification make use of the radio beacons transmitted by access points. Each user scans the received radio beacons to find out the closer access point from which it receives the greater signal strength.

ToA and TDoA measure the arrival times of signals transmitted by the access point [START_REF] Chai | Indoor Radio Propagation Models and Wireless Network Planning[END_REF]. However, timebased positioning techniques require time synchronization between access points and clients.

The most common positioning techniques in Wi-Fi networks are based on the received signal power measurements. Power measurements are converted into quantified levels called Received Signal Strength Indication that reflects the real signal power values. In this context, positioning can use either propagation models or empirical models. Propagation models [START_REF] Kumar | Performance and Analysis of Propagation Models for Predicting RSS for Efficient Handoff[END_REF] translate signal strength degradation when moving away from the access point. The signal loss is therefore converted into distance, and client position is estimated via trilateration. However, the empirical model creates a database containing RSSI values for the different points in the positioning map. Client position is then estimated as the point in the map having the closest RSSI values to those measured by the client.

We can measure the angle of arrival of signals transmitted by Wi-Fi clients using directional antennas or antenna arrays. This method is called Angle of Arrival, and it belongs to the category of angle-based positioning techniques.

Fig. 1 summarizes the main categories of Wi-Fi positioning techniques. They are based on Cell-ID, time, RSSI or angle.

B. RSSI-based trilateration

RSSI is converted into distance using propagation models. In fact, radio frequency signal is subject to degradation when propagating in free space. Propagation models formulate signal degradation as a function of the traveled distance and the transmission frequency. The Hata-Okumura model [START_REF] Hassan-Ali | A New Statistical Model for Site-Specific Indoor Radio Propagation Prediction Based on Geometric Optics and Geometric Probability[END_REF] for instance is used for Path Loss P L prediction in wireless environments. According to this propagation model, P L is given by:

P L = A + B.log(d) (1)
Given that d is the distance between the access point and the wireless client, B is a factor that depends on antenna characteristics, and A is the fixed loss depending on the transmit frequency.

The received power P r is a function of the transmit power, antenna gains and path loss. It is given by:

P r = P t + G t + G r -P L (2)
Given that P t is the transmitted power, G t is the transmit antenna gain, and G r is the receive antenna gain.

RSSI gives information about the received power P r . Using (1) and ( 2), we can find the distance d that separates the access point and the Wi-Fi client. Trilateration technique requires three distance measurements to find out client position. Hence, three access points are required to be present in the localization area. Client position is estimated as the intersection of three circles having the access points as centers and the obtained distances as radii.

C. RSSI fingerprinting

Received Signal Strength Indication fingerprinting is a positioning technique that uses empirical models. The idea is to perform preliminary RSSI measurements instead of using propagation models. It consists of two phases: an offline phase that generates a radio map for the positioning area, and an online phase that estimates client geographical position.

In the first phase, positioning area is separated into smaller parts representing a grid. We measure RSSI values for each point in the grid. Fig. 2 shows a Wi-Fi localization area where the map is divided into smaller areas. end if 13: end if compared to the fingerprint stored in the database to find the closest match. The final user position estimate is the entry in the database that best matches the actual measurements. We use the Euclidean distance to find the stored RSSI n-tuple that best matches the current measurements.

D. Constrained RSSI fingerprinting

RSSI fingerprinting is used for Wi-Fi client localization. However, its accuracy is restricted by signal fluctuations and by the dynamic characteristics the propagation environment. In order to improve positioning accuracy, we integrate a distance constraint within the position estimation algorithm. In fact, positioning results offer useful information for future position estimation in real-time tracking scenarios.

In this paper, we propose a constrained RSSI fingerprinting method for positioning in Wi-Fi networks. It calculates the distance between the estimated position and the old client position before making positioning decision. The idea is to eliminate position estimations if the distance between the last position and the estimated one is higher than the distance traveled by the Wi-Fi client. Client speed measurement is beyond the scope of this paper; however, we define d threshold as the maximum distance traveled during the time that separates two consecutive position estimations.

Algorithm 1 describes the constrained RSSI fingerprinting method. The estimated position is approved if it succeeds the distance check. For instance, the distance between the new and the old positions should be less than a predefined distance threshold for Wi-Fi clients moving at pedestrian speeds. This threshold depends on client characteristics (i.e., client speed) and on localization area geometry. However, we break the distance constraint after three negative successive checks, and we approve the estimated position for the current client. This algorithm allows us to eliminate positioning errors caused by wireless signal fluctuations. Experiments are performed to verify the performance of the proposed algorithm.

IV. EXPERIMENTS

A 12.73×18.18m 2 room at the faculty of engineering of Saint-Joseph University in Beirut is chosen as the indoor environment for our positioning tests. Our objective is to compare the performance of Wi-Fi positioning techniques described in this paper: RSSI-based trilateration, fingerprinting and constrained fingerprinting.

We deploy a Wi-Fi network consisting of three wireless access points as shown in Fig. 2. A Wi-Fi client present in the localization area gets information about the signals transmitted by the three access points deployed in the room.

The localization map is divided into smaller parts via a grid. The distance between two adjacent points in the grid is 3.00m. We perform RSSI measurements for each point in the map under different conditions: number of clients, network load, etc. Mean RSSI values are stored in the localization database that contains a triplet (RSSI 1 , RSSI 2 , RSSI 3 ) for each point in the grid. Database construction is the first phase of Wi-Fi positioning using fingerprinting technique. In the second phase, live RSSI measurements are compared to the values stored in the database using the Euclidean distance comparison criterion. User position is estimated as the entry in the database that best matches the actual measurements (i.e., that presents the minimum Euclidean distance). However, trilateration converts RSSI values into distance using the propagation models. For a better accuracy, we adapted the propagation models to our propagation environment performing preliminary signal measurements. Thus, the fixed loss parameter is adjusted according to measurement results.

The centralized approach is used for Wi-Fi client localization. For this reason, a positioning server is deployed in the network. It is connected to the three access points, and it collects all the necessary information about Wi-Fi clients present in the system. The server has also access to the localization database. We developed a graphical user interface that shows instantaneous user position on the localization map. Experiment scenarios and results are given in the next section.

V. EXPERIMENTS RESULTS AND DISCUSSION

We use three positioning techniques based on RSSI to estimate the position of a pedestrian Wi-Fi client that moves across the room (real-time tracking). Performance comparison parameters are: percentage of erroneous estimations and positioning accuracy.

A. RSSI-based trilateration

The Wi-Fi client moves along a predefined path that includes 16 points of the map. At each point, position estimation is done via the trilateration method. A centralized server collects the three RSSI measurements required to estimate client position. Fig. 3 shows the real path of the Wi-Fi client and the estimated path using RSSI-based trilateration method.

Trilateration positioning results do not exactly match with the real positions of the Wi-Fi client. In fact, distance calculation is based on instantaneous RSSI measurements. Thus, errors are due to RSSI fluctuations along with time, interference and network load. However, the majority of the estimated positions are close to the real position of the client. The distance between the estimated position and the

B. RSSI fingerprinting

In the second experiment, we use RSSI fingerprinting to estimate user position. The Wi-Fi client moves along the same path under the same network conditions (i.e., number of clients and network load). Fig. 4 shows Wi-Fi client tracking results using RSSI fingerprinting method. 75% of the estimated positions using the fingerprinting method exactly match the real positions of the Wi-Fi client. Moreover, erroneous estimations are located in the neighboring of the real client positions. In fact, localization error is comparable to the minimum distance between two adjacent points in the grid (3m). In comparison with RSSI-based trilateration, the fingerprinting method shows greater immunity to RSSI fluctuations. Instead of converting instantaneous RSSI measurements into distance, the fingerprinting method compares the measured values with the mean RSSI values stored in the database. Positioning decision is made after finding the best match with mean RSSI values. Hence, the impact of RSSI fluctuations is mitigated. Localization results are more robust against interference and network conditions.

C. Constrained RSSI fingerpriniting

Positioning decisions are made according to the constrained RSSI fingerprinting method in the third experiment. The Wi-Fi client moves along the same path. Positioning results are shown in Fig. 5. We assume that the client cannot travel more than 3m at pedestrian speed between two consecutive position estimations. Note that the calculation of the distance threshold is beyond the scope of this paper. Around 81% of the estimated positions using constrained RSSI fingerprinting exactly match the real client positions. In addition, the distance between erroneous position estimations and real client positions is comparable to the minimum distance between two adjacent points in the grid. Constrained RSSI fingerprinting is also immune to RSSI fluctuations. Experiments results show that positioning accuracy is higher when using the constrained RSSI fingerprinting method. Moreover, the trilateration technique has never found the exact position of the Wi-Fi client. Nevertheless, the main advantage of trilateration is that it does not require a pre-positioning phase where we should perform RSSI measurements to be stored in a database.

Fig. 1 .

 1 Fig. 1. Classification of Wi-Fi positioning techniques

Fig. 2 .

 2 Fig. 2. Localization area divided using a gridAt each point of the grid, Wi-Fi signals are received from the nearby access points. We measure RSSI values under different conditions: time, interference, number of clients, network load. If n is the number of access points in the localization area, an n-tuple (RSSI 1 , RSSI 2 , ..., RSSI n ) containing mean RSSI values is created for each point (x, y) in the map. This information is stored in a database that will be used in the positioning process.After database creation, we proceed to the position estimation phase. Each Wi-Fi client performs live RSSI measurements. The obtained values are the main parameter when estimating client geographical position. They are

Fig. 3 .

 3 Fig. 3. Real-time tracking using RSSI-based trilateration

Fig. 4 .

 4 Fig. 4. Wi-Fi client tracking using RSSI fingerprinting

Fig. 5 .

 5 Fig. 5. Wi-Fi positioning using constrained RSSI fingerprinting

VI. CONCLUSION

In this paper, we described the major techniques used for positioning in wireless networks. They are mainly based on the Received Signal Strength Indication. RSSI fingerprinting methods are more accurate than trilateration; however, trilateration does not require the construction of a fingerprinting database. Accuracy improvements increase user satisfaction, and make the localization services more robust and efficient.

Future work may study localization techniques cost and performance when increasing the number of access points. We can also benefit from the diversity of wireless access networks deployed in the same geographical area to improve the accuracy of positioning techniques. For example, data from mobile networks (such as Global System for Mobile Communications) can be combined with information from Wireless Local Area Networks (such as Wi-Fi) in order to get a more accurate position estimation.

ACKNOWLEDGMENT

This work was jointly supported by the National Council for Scientific Research (CNRS) Lebanon and Saint Joseph University of Beirut.


Mohamad Yassin

Elias Rachid

A Survey of Positioning Techniques and Location Based Services in Wireless Networks

Keywords: Positioning techniques, GPS, GSM, Wi-Fi, Location Based Services, hybrid positioning systems

Positioning techniques are known in a wide variety of wireless radio access technologies. Traditionally, Global Positioning System (GPS) is the most popular outdoor positioning system. Localization also exists in mobile networks such as Global System for Mobile communications (GSM). Recently, Wireless Local Area Networks (WLAN) become widely deployed, and they are also used for localizing wireless-enabled clients. Many techniques are used to estimate client position in a wireless network. They are based on the characteristics of the received wireless signals: power, time or angle of arrival. In addition, hybrid positioning techniques make use of the collaboration between different wireless radio access technologies existing in the same geographical area. Client positioning allows the introduction of numerous services like real-time tracking, security alerts, informational services and entertainment applications. Such services are known as Location Based Services (LBS), and they are useful in both commerce and security sectors. In this paper, we explain the principles behind positioning techniques used in satellite networks, mobile networks and Wireless Local Area Networks. We also describe hybrid localization methods that exploit the coexistence of several radio access technologies in the same region, and we classify the location based services into several categories. When localization accuracy is improved, positiondependant services become more robust and efficient, and user satisfaction increases.

I. INTRODUCTION

Numerous geolocation technologies are used to estimate client (person or object) geographical position. The large diversification of existing wireless Radio Access Technologies (RAT) and the increasing number of wirelessenabled devices are promoting the extensive application of Location Based Services (LBS) [START_REF] Zhang | The Location Privacy Protection Research in Location-Based Service[END_REF]. Position-dependant services include: emergency services such as rescue response [START_REF] Casper | Human-Robot Interactions During the Robot Assisted Urban Search and Rescue Response at the World Trade Center[END_REF] and security alerts, entertainment services like mobile gaming [START_REF] Ferreira | A Middleware Architecture for Mobile and Prevasive Large Scale Augmented Reality Games[END_REF], medical applications [START_REF] Hong | Observability of Error States in GPS/INS Integration[END_REF] and a wide variety of other applications.

Global Positioning System (GPS) is the most common technology which supports outdoor locating services [START_REF] Yeh | A Study on Outdoor Positioning Technology Using GPS and WiFi Networks[END_REF]. Satellites orbiting around the Earth continuously broadcast their own position and direction. Broadcasted signals are used by the receivers to estimate satellites positions as well as the distance between satellite and receiver. Having these distance measurements, trilateration [START_REF] Fang | Trilateration and Extension to Global Positioning System Navigation[END_REF] is usually used to estimate receivers position. Accuracy of the estimated positions depends on the number of visible satellites. Hence, it does not work well for indoor positioning, and it depends on weather conditions.

Base stations of the mobile terrestrial radio access networks, such as Global System for Mobile communi-cations (GSM), are the reference points for mobile client localization. Cell-Identification (Cell-ID) estimates client position using the geographical coordinates of its serving base station in cellular networks [START_REF] Kos | Mobile User Positioning in GSM/UMTS Cellular Networks[END_REF][START_REF] Adusei | Mobile Positioning Technologies in CellularNetworks: an Evaluation of their Performance Metrics[END_REF][START_REF] Trevisani | Cell-ID Location Technique Limits and Benefits: an Experimental Study[END_REF]. Other positioning methods are based on the fingerprinting database, and they make use of Received Signal Strength (RSS) measurements [START_REF] Laitinen | Database Correlation Method for GSM Location[END_REF][START_REF] Talvitie | Modeling Received Signal Strength Measurements for Cellular Network Based Positioning[END_REF][START_REF] Deblauwe | Cellular Positioning: from Received Signal Strength Measurements to the Algorithm[END_REF][START_REF] Huang | A Signal-Aware Fingerprinting-Based Positioning Technique in Cellular Networks[END_REF]. However, positioning accuracy is restrained by interference, multipath and non line-of-sight (NLOS) propagation.

A Wireless Local Area Network (WLAN) offers connectivity and internet access for wireless enabled clients within its coverage area. For example, IEEE 802.11 [START_REF] Matthew | 11 Wireless Networks: The Definitive Guide[END_REF] (commonly known as Wi-Fi) is widely deployed, and it is also used for localizing Wi-Fi enabled devices. The fingerprinting approach is often used in Wi-Fi positioning systems [START_REF] Vaupel | Wi-Fi Positioning: System Considerations and Device Calibration[END_REF][START_REF] Shin | Enhanced Weighted K-Nearest Neighbor Algorithm for Indoor Wi-Fi Positioning Systems[END_REF]. It is based on Received Signal Strength Indication (RSSI) measurements in the localization area. Positioning systems using Wi-Fi are considered cost effective and practical solutions for indoor location tracking and estimation [START_REF] Chang | Robust Indoor Positioning Using Differential Wi-Fi Access Points[END_REF].

Positioning techniques performance comparison is done using several metrics [START_REF] Adusei | Mobile Positioning Technologies in CellularNetworks: an Evaluation of their Performance Metrics[END_REF] such as applicability, latency, reliability, accuracy and cost. A technique is more accurate when the estimated position of a client is closer to the real geographical position. Positioning accuracy is getting more important with the increasing use of position dependant applications. Indeed, it is crucial for emergency location services. Hence, hybrid positioning systems, such as [START_REF] De Angelis | GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios[END_REF][START_REF] Rooney | Accurate Vehicular Positioning Using a DAB-GSM Hybrid System[END_REF], are introduced to improve accuracy and reliability of the existing localization technologies.

In this paper, we describe the main positioning techniques used in satellite networks like GPS, in mobile networks, such as GSM, and in wireless local area networks such as Wi-Fi. The coexistence of several wireless radio access technologies in the same area allows the introduction of hybrid positioning systems, and promotes the diversification of position dependant services. We explain some of the hybrid localization techniques that coordinate information received from different radio access technologies in order to improve positioning accuracy. Such improvements increase user satisfaction, and make LBS more robust and efficient. We also classify these services into several categories. The rest of the paper is organized as follows: in (II) we explain the principles behind positioning techniques used in satellite and mobile networks. Wi-Fi localization methods are reported in (III). We describe hybrid positioning systems in (IV). Section (V) contains a classification of LBS. Concluding remarks are given in section (VI).

II. POSITIONING IN GPS AND MOBILE NETWORKS

GPS consists of a network of 24 satellites in six different 12-hour orbital paths spaced so that at least five are in view from every point on the globe [START_REF] Bajaj | GPS: Location-Tracking Technology[END_REF]. Satellites serve as reference points when estimating client position. They continuously broadcast signals containing information about their own position and direction. Distance between satellite and receiver is determined by precisely measuring the time it takes a signal to travel from the satellite to the receivers antenna.

Once the distances between visible satellites and the GPS receiver are measured, client position is estimated via the trilateration method, commonly known as triangulation [START_REF] Yeh | A Study on Outdoor Positioning Technology Using GPS and WiFi Networks[END_REF]. Three distance measurements are required to perform position estimation. In fact, the estimated position is the intersection of three spheres having the satellites as centers and the calculated distances as radii.

GPS accuracy is largely reduced by several factors such as signal delays, satellite clock errors, multipath distortion, receiver noise and various environment noises [START_REF] Huang | Improve GPS Positioning Accuracy with Context Awareness[END_REF]. To overcome visibility problems between satellites and receivers, assisted-GPS is proposed. It benefits from the coexistence of satellite networks along with terrestrial wireless access networks (i.e. mobile networks or Wireless Local Area Networks) in the same area. Therefore, superior accuracy, availability and coverage are offered for indoor use or in urban areas. Refer to [START_REF] Djuknic | Geolocation and Assisted GPS[END_REF] for more information about Assisted-Global Positioning System.

In mobile networks, such as GSM, many techniques are used to estimate client position. Contrarily to the satellites that are continuously moving around the globe, base stations (BS) of the mobile networks have fixed geographical positions. In addition, each BS broadcasts its Cell-ID and Location Area Identifier (LAI) to the mobiles within its coverage area. Therefore, each mobile can approximate its own position using the geographical coordinates of its serving base station in Cell-ID method [START_REF] Trevisani | Cell-ID Location Technique Limits and Benefits: an Experimental Study[END_REF].

Angle of Arrival (AoA) measurements [START_REF] Pages-Zamora | Closed-Form Solution for Positioning Based on Angle of Arrival Measurements[END_REF][START_REF] Niculescu | Ad-Hoc Positioning System (APS) Using AoA[END_REF] of several radio links between the base stations and the mobile are also used to estimate client position. Hence, user position is approximated according to these angle measurements and using information about base stations geographical coordinates. Time of Arrival (ToA) [START_REF] Simsim | Time of Arrival Statistics in Cellular Environments[END_REF][START_REF] Deng | An AoA Assisted ToA Positioning System[END_REF] requires synchronization between the different network elements (i.e., base stations and mobile stations). The time difference between bursts sent by the mobile are converted into distance. Hence, trilateration is used to estimate client position. Other methods use received signal strength measurements to localize mobile stations. For example, received signal power is converted into distance via propagation models or empirical models. In addition, the fingerprinting method [START_REF] Laitinen | Database Correlation Method for GSM Location[END_REF] compares RSS measurements with the values stored in a database for specific points in the localization map in order to approximate client position.

Time Difference of Arrival (TDoA) [START_REF] Shin | Comparisons of Error Characteristics Between ToA and TDoA Positioning[END_REF][START_REF] Gustafsson | Positioning Using Time-Difference of Arrival Measurements[END_REF] technique is inspired by ToA. Indeed, in ToA, the positioning entity measures signal propagation time from the emitter to the receiver. Time measurement is converted into distance that is used to estimate client position. However, TDoA technique requires the simultaneous transmission (for each base station) of two signals having different frequencies. These signals will reach the receiver at different times. Therefore, the time difference is measured and converted into distance. Once we have three distance measurements, trilateration is used to estimate client position. Enhanced Observed Time Difference (E-OTD) [START_REF] Charitanetra | Mobile Positioning Location Using E-OTD Method for GSM Network[END_REF] requires synchronization between network entities (base stations and mobiles). Each base station broadcasts messages within its coverage area. A mobile station compares the relative times of arrival of these messages to estimate its distance from each visible base station.

Localization techniques in mobile networks can be classified in two categories: network-based and clientbased. In network-based positioning techniques, the network collects necessary information to estimate client position. Time, angle or distance measurements performed by the base stations are usually forwarded to a positioning server deployed in the network. The required information when estimating user position is stored in the positioning database. Thus, positioning server has information about the positions of all the users in the system. However, client-based localization techniques are characterized by the absence of a centralized positioning entity. In fact, each client performs time, angle, power or distance measurements locally. Thus, it approximates its own position using local measurements and information broadcasted from the base stations.

Fig. 1 shows a qualitative comparison of the positioning techniques described in this section. Performance criteria used for localization techniques comparison are accuracy and coverage. A positioning technique is better when it has lower accuracy error (distance between the estimated position and the real geographical position) and greater coverage. Traditionally, a Wi-Fi network provides Internet access to wireless-enabled clients located within its coverage area. In addition, it allows interconnectivity between wireless devices existing in the same network. Recently, Wi-Fi networks are having additional applications. For example, we can benefit of the coexistence of several radio access technologies in the same geographical area. Heterogeneous networks offer the possibility to steer user sessions preferentially to a given Radio Access Technology, such as Wi-Fi or Universal Mobile Terrestrial radio access System (UMTS), according to service type [START_REF] Venes | Performance of a Heterogeneous Network with UMTS, Wi-Fi and WiMAX[END_REF] and network load. Moreover, the wide deployment of Wi-Fi networks allows the introduction of numerous location-based services.

Wi-Fi positioning techniques are similar to those used in mobile networks. However, the most common technique used to localize a client in Wi-Fi networks is based on RSSI measurements. In the remainder of this section, we classify Wi-Fi positioning techniques into several categories, and we describe the basics of RSSI-based localization methods.

ToA [START_REF] Ciurana | WLAN Indoor Positioning Based on ToA with Two Reference Points[END_REF] and TDoA perform time measurements to calculate the distance between Wi-Fi client and access points. Hence, three distance measurements are required to estimate user position via trilateration [START_REF] Peneda | Trilateration for Indoors Positioning Within the Framework of Wireless Communications[END_REF]. Such methods belong to the category of time-based positioning techniques, and they require time synchronization between network entities. In Cell-ID category, users scan the received radio beacons to estimate the closer access point. They use either predefined radio propagation models or experimental fingerprinting data to estimate user position.

AoA method uses directional antennas to measure the angle of arrival of signals transmitted by the clients. Hence, client position is estimated via the geometry of triangles in angle-based positioning techniques. However, the most common positioning techniques in Wi-Fi networks are based on RSSI measurements [START_REF] Yassin | Performance Comparison of Positioning Techniques in Wi-Fi Networks[END_REF]. Some of them are based on propagation models [START_REF] Chai | Indoor Radio Propagation Models and Wireless Network Planning[END_REF] to translate signal power into distance. Other methods use empirical models, and they store RSSI measurements in a positioning database. Therefore, localization methods in Wi-Fi are classified into four main categories: Cell-ID, time, RSSI and angle. Fig. 3 illustrates the classification of Wi-Fi positioning techniques.

Fig. 3. Categories of Wi-Fi positioning techniques

Received signal strength indication measurements are quantified levels that reflect the real power of a received wireless signal. When propagating in free space, the transmitted radio frequency signal is subject to degradation due to attenuation, reflection, diffraction and scattering. Indeed, several propagation models formulate signal strength degradation as a function of the traveled distance and the transmission frequency. For instance, Hata-Okumura [START_REF] Kumar | Performance and Analysis of Propagation Models for Predicting RSS for Efficient Handoff[END_REF] model approximates Path Loss (PL) according to the distance between emitter and receiver, antenna characteristics and transmission frequency. Hence, RSSI measurements are compared with theoretical values of the received power (calculated using propagation models) in order to find the distance traveled by the signal. Three distance measurements are required to estimate Wi-Fi-enabled client position via trilateration. In fact, it is the intersection of three circles having the access points as centers and the calculated distances as radii.

Other positioning techniques based on RSSI measurements use empirical models to estimate user position. Instead of approximating the distance between Wi-Fi clients and access points, the localization area is divided into smaller parts using a grid. Each point of the grid receives several Wi-Fi signals from the neighboring access points. RSSI measurements are performed under different conditions (e.g. time, interference, network load) in order to increase positioning accuracy. If n is the number of Wi-Fi access points, an n-tuple (RSSI 1 , RSSI 2 , ..., RSSI n ) containing mean RSSI values is created for each point (x, y) in the map. Such positioning technique is called RSSI fingerprinting, and it occurs in two phases: an offline fingerprinting phase and an online positioning phase.

In the first phase, RSSI measurements are done for each point in the positioning map (under different network conditions). At the end of this phase, positioning database is created. It contains mean RSSI values for every point in the grid [START_REF] Hassan-Ali | A New Statistical Model for Site-Specific Indoor Radio Propagation Prediction Based on Geometric Optics and Geometric Probability[END_REF]. However, the second phase performs online RSSI measurements for signals received from the neighboring access points. The positioning entity compares live RSSI measurements with the values stored in the database. Therefore, client position is estimated as the entry (x, y) in the database that best matches the actual measurements [START_REF] Rodrigues | Fingerprinting-Based Radio Localization in Indoor Environments Using Multiple Wireless Technologies[END_REF]. Accuracy of RSSI-based positioning techniques in Wi-Fi networks depends on the number of access points involved in the localization problem.

IV. HYBRID POSITIONING SYSTEM

The wide usage of position dependant services increases the need for more accurate position estimation techniques. Due to the limitations of positioning methods that use data from one single RAT, hybrid positioning techniques are proposed to increase accuracy. They make use of the collaboration between different wireless access networks existing in the same geographical area, such as GPS and GSM, to exchange additional position related data.

The main factors that reduce accuracy of GPS are multipath distortion and visibility problem between satellites and receivers [START_REF] Bajaj | GPS: Location-Tracking Technology[END_REF]. Therefore, a hybrid positioning technique called Assisted-Global Positioning System (A-GPS) [START_REF] Djuknic | Geolocation and Assisted GPS[END_REF] is introduced to overcome these limitations. In fact, GSM Base Transceiver Stations (BTS) are involved in the positioning problem along with the satellites. Additional data about BTS geographical position and proximity to the mobile is used together with GPS localization information in order to estimate client position. Moreover, authors of [START_REF] Zirari | Wi-Fi GPS Based Combined Positioning Algorithm[END_REF] propose a Wi-Fi GPS based combined positioning algorithm. Indeed, localization data from the Wi-Fi network is used when the number of visible satellites is less than four.

In cellular networks, positioning accuracy is restrained by the non-line-of-sight propagation and by interference mitigation techniques. Thus, inconsistent information is provided as entry when resolving the localization problem. Authors of [START_REF] De Angelis | GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios[END_REF] describe a hybrid positioning scheme that combines TDoA technique measurements obtained from the cellular network with GPS range information to improve the accuracy of mobile client position estimation. The obtained accuracy should be within the limits required by emergency location services.

ToA technique is very frequently used for positioning in mobile networks. However, it requires synchronization between mobiles and base stations. In addition, ToA is restricted by multipath and non-line-of-sight propagation problems. Hence, this positioning technique is assisted by additional information from AoA technique. Angle measurements are performed by the serving base station using antenna arrays. The usage of ToA assisted AoA technique improves positioning accuracy, especially in bad propagation environments [START_REF] Deng | An AoA Assisted ToA Positioning System[END_REF].

In wireless local area networks, such as Wi-Fi, RSSI fingerprinting technique depends on the number of access points involved in the localization process. The validity of mean RSSI measurements stored in the database also affects positioning accuracy. We exploit the coexistence of Personal Area Networks (PAN) along with Wi-Fi in the same geographical area to improve positioning accuracy. In fact, RSSI measurements are done for all the existing wireless technologies, and results are stored in the positioning database. Therefore, Wi-Fi fingerprinting uses additional RSSI information from wireless PANs existing in the same area such as Bluetooth and ZigBee networks.

More information about this hybrid technique is found in [START_REF] Rodrigues | Fingerprinting-Based Radio Localization in Indoor Environments Using Multiple Wireless Technologies[END_REF].

V. LOCATION BASED SERVICES

In wireless networks, the knowledge of user geographical position allows the introduction of numerous position dependant applications. These applications are known as location-based services, and they are useful for service providers as well as for mobile clients.

The wide deployment of radio access technologies and the increasing development of wireless-enabled devices are promoting the extensive use of LBS. As mentioned in the previous sections, numerous positioning techniques are used to estimate client position. However, the main parameter for LBS efficiency is accuracy of the positioning technique. Indeed, users are more satisfied when the estimated position is closer to the real geographical position, and when the probability of erroneous estimations is reduced.

Location-based services are related to the position of the user making the request. They are classified [START_REF] Mohapatra | Survey of Location Based Wireless Services[END_REF] as emergency services (e.g. security alerts, public safety and query of the nearest hospital), informational services (i.e., news, sports, stocks and query of the nearest hotel or cinema), tracking services (like asset/fleet/logistic monitoring or person tracking), entertainment services (for example: locating a friend and gaming) and advertising services (such as announcements or invitation messages broadcasted by the shops to the nearby mobile clients). Moreover, future applications of LBS include support to the studies on climate change, seismology and oceanography.

Position dependant services are useful for mobile mapping, deformation monitoring and many civil engineering applications [START_REF] Peng | A Survey of Location Based Service for Galileo System[END_REF]. They have revolutionized navigation (on land, in the air and at sea) and intelligent transportation system by increasing their safety and efficiency. However, user location privacy security poses a potentially grave threat [START_REF] Zhang | The Location Privacy Protection Research in Location-Based Service[END_REF]. In fact, it is possible to access user location information anytime and anywhere. Therefore, many privacy protection methods are introduced to deal with the contradiction between location privacy protection and quality of service in LBS. Some of them protect user ID information by hiding the true ID when requesting the service. Other methods do not submit the exact location to the server, but they send a region containing user exact position.

VI. CONCLUSION

In this paper, we surveyed the major positioning techniques used in wireless networks. Global positioning system is the most common technology for outdoor locating services. Localization techniques in mobile networks, such as global system for mobile communications, perform time, angle or signal power measurements to estimate user position. In Wi-Fi networks, received signal strength indication fingerprinting approximates user position within the coverage area. Accuracy depends on the number of access points involved in the positioning process.

Hybrid positioning techniques are proposed to satisfy the increasing need for positioning accuracy. They make use of the collaboration between multiple radio access technologies existing in the same geographical area. For instance, Assisted-GPS uses information from the mobile network to improve positioning accuracy in GPS. Location-based services are based on the knowledge of user position. They include emergency, informational, tracking, entertainment and advertising services. LBS are useful for service providers and for the clients. Accuracy improvements increase user satisfaction, and make the localization services more robust and efficient.

Fig. 1 .

 1 Fig. 1. Positioning techniques comparison [9]

Fig. 2 .

 2 Fig. 2. Wi-Fi network having several clients


Pierre Gehl

Miao Wang

Khaled Taalab

Dina D'ayala

Francesca Medda

Tao Cheng

Use of multi-hazard fragility functions for the multi-risk assessment of road networks

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Use of multi-hazard fragility functions for the multi-risk assessment of road networks Authors: Gehl P., Wang M., Taalab K., D'Ayala D., Medda F., Cheng T.

Infrastructure systems such as road networks appear to be particularlyexposed to multi-hazard eventsbecause of (i) their spatial extent,which may span various areas prone to different hazard types, and (ii) their interconnected nature, which facilitates the propagation of local losses across the whole system. Multi-risk assessment should consider interactions at both the hazard level (i.e. triggered events or independent events with a joint occurrence) and the vulnerability level (i.e. fragility models accounting for cumulated damage), thus requiring a harmonization effort on the various intensity measures, damage models or damage scales considered.

Therefore this paper presents a method for the multi-risk assessment of infrastructure systems, which concentrates on the harmonization of losses from different hazard types. The global system is first decomposed into its physical elements (e.g. bridges), which are in turn broken down into structural components (e.g. piers, decks, bearing, etc.). Each component type has a different exposure and susceptibility to different hazard types, such as earthquakes, ground failures and floods. Therefore it is necessary to identify all the hazard-specific failure modes for each combination of hazard and component. The occurrence of each of these component failure modes can then be quantified through the derivation of hazard-specific component fragility curves, provided that the adequate modelling tools are available.

In parallel, functionality losses, which are crucial metrics for the performance assessment of infrastructure systems, are estimated for each component failure mode through an expert-based survey. System failure modes can then be defined from combinations of component damage states to lead to consistent levels functionality losses (e.g. repair time, proportion of lane closure, etc.). Therefore, the consequences of various types of hazard-specific loading mechanisms can be harmonized in terms of functionality. The component fragility curves are assembled through Bayesian networks in order to update the joint probability of occurrence of the system failure modes: this results in fragility models that account for multiple hazard-specific intensity measures (i.e. vector-valued) and cumulated damage (i.e. use of component fragility curves for pre-damaged elements). Therefore such fragility functions have the potential to treat various multi-hazard configurations, such as independent or cascadingevents, while being directly associatedto functionality losses.

Based on fragility functions andthe duration of repair operations given global damage states of bridges and tunnels, an ABM infrastructure natural hazard response simulation model is developed to simulate the interactions among the different infrastructures that constitute the road network system: roads, tunnels and bridges under a certain hazard scenario.Furthermore,the overall road network recovery time canalso be estimated.Finally, the functionality losses that are potentially induced by multiple hazard events are aggregated and sampled (i.e. probabilistic scenarios), so that traffic modelling software may assess how the traffic flow changes as a consequence of road capacity reductions on the network.NEXTA, an open-source traffic equilibrium modelling software, is used to calculate additional journey time for travellers, based on a number of damage scenarios.