Mohammed Al-Siyabi
email: m.alsiyabi@surrey.ac.uk

Haitham Cruickshank
email: h.cruickshank@surrey.ac.uk

Zhili Sun
email: z.sun@surrey.ac.uk

Godwin Ansa
email: g.ansa@surrey.ac.uk

Fairness and Satisfaction Model for DTN Applications Using Various Transportation Means

Keywords: Delay Tolerant Network (DTN), Quality of service (QoS), Aircraft and User's Satisfaction

Delay Tolerant Network suffers from lack of resources and disconnected contact nature. In DTN, all possible methods are used to transmit data including the physical transportations means. Aircrafts in commercial routes have been proposed to carry data from ground users along their flying routes. Delivery probability is compared when using aircrafts, buses and ferries. Results show that aircrafts provide higher delivery probability which is up to 62% better compared with buses and ferries. Furthermore, when there is lack of resources, it is difficult to satisfy all users' demands for traffic. We propose a Fairness and Satisfaction (FS) model to enhance the users' satisfaction during DTN limited and scarce resources. Various scenarios are tested for the FS model through intense simulations. FS model, when implemented, will improve user's satisfaction up to 18 % and DTN fairness up to 20 % compared with the same scenario lacking the model.

Introduction

Delay tolerant networks work in the environment of limited communication infrastructures where links are disconnected and might not be available for a long time. In addition to using the standard transmission medium in these challenged conditions, transportation means have been used in DTN literature to carry data for DTN applications. Buses, ferries, bicycles, motorbikes and trains have been proposed for DTN [START_REF] Farrell | Delay-and Disruption-Tolerant Networking[END_REF][2][3] [START_REF] Jain | Routing in a delay tolerant network[END_REF], sometime by physically transporting the saved data on a storage medium such as USP memory or by wirelessly exchanging data, whenever a transportation vehicle comes in range with the users. We have proposed using aircrafts in commercial routes [START_REF] Al Siyabi | Delay/Disruption Tolerant Network Architecture for Aircraft Datalink on Scheduled Routes[END_REF] because they have larger coverage area and fly over remote areas where communication infrastructure is limited. In this paper, we will set a scenario and compare between using buses, ferries and aircrafts. The results show that using aircrafts will provide better delivery probabilities and more user satisfaction. This is mainly because all requesting users are in range with the flying aircraft at most of the time due to the flying high altitude whereas this is not the case with buses and ferries. Furthermore, these transportation means are considered as contacts and represent the main resources in DTN. The more number of vehicles will mean more opportunities to send data and therefore more resources. However, these resources are normally scarce and limited in DTN which might degrade its ability to satisfy all users' requests of traffic. The users will compete with each other to get access to the passing contact and this will lead to congestion. Whenever a contact exists, all users will try to get access to it in order to transmit their data but unfortunately not all of them will be able to send theirs due to the contact limited capacity and numbers. However, after another wait, another contact might arrive, where the same competition will be repeated among users and again not all of them will be able to send their data. Therefore, there is a need for a system that should try to improve each and every user chances of getting access to the contact resources especially during severe shortage of resources. The system should be fair to allocate the resources equally among the users and give a chance to as many users as possible to send their data.

Throughout this paper, we will show the improvement in the level of user satisfaction when applying the FS model compared with the same scenario lacking our model. FS model will be applied to aircrafts, buses and ferries which act as intermediate node/gateway to help it decide fairly which user to select. This decision will be used by other vehicles to ensure that they satisfy as many users as possible.

Related Work

DTN has increasingly become a hot research topic and the IRTF DTNRG [START_REF]Home -Delay Tolerant Networking Research Group[END_REF] is leading the effort to expand the research of DTN in various topics. In DTN literature, many transportations means are used to carry data for DTN applications. We proposed using an aircraft as a DTN carrier at remote locations [START_REF] Al Siyabi | Delay/Disruption Tolerant Network Architecture for Aircraft Datalink on Scheduled Routes[END_REF]. At UMass Amherst [START_REF] Zhang | Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing[END_REF], they used public buses travelling on scheduled routes to exchange data using Wi-Fi nodes in a project called Diesel Net. Ref [START_REF] Zhao | A message ferrying approach for data delivery in sparse mobile ad hoc networks[END_REF] proposed using ferries and suggested that the users could schedule their movement to match the ferries movements in order to send their data. We will compare between these three types of transportations in DTN environment in the coming sections.

However, there are limited references that discuss the user satisfaction in DTN. Ref [START_REF] Ishimaru | DTN-based Delivery of Word-of-Mouth Information with Priority and Deadline[END_REF] proposed a data delivery method for DTN for various users based on the relative importance of their data requests, which is something like the users class of service CoS classification in DTN. They proposed a carry-and-forward technique to enhance the overall user satisfactions and achieved better users satisfactions compared with the conventional first in first out FIFO method. Our FS model will also consider the class of service CoS of the user, but furthermore, it will consider other metrics such as the history of users previous admissions as we shall see later in the model discussion.

Furthermore, many references in the literature discuss the resources management in DTN but each one is targeting a certain resource. Some of them discuss how to save the buffer resources which is important for some DTN applications like the sensors network. The concept of message ferrying is proposed in [START_REF] Li | Adaptive Optimal Buffer Management Policies for Realistic DTN[END_REF] where they proposed allocating the buffer resources between users at different connections sessions. In our aircraft application, we assume unlimited buffer space and we consider the aircraft communication bandwidth as the only resource. The bandwidth will increase with the longer time the aircraft stay in line of sight contact with the ground users. Also, more aircrafts will mean more contacts and therefore more resources. Ref [START_REF] Chuah | Integrated Buffer and Route Management in a DTN with Message Ferry[END_REF] also discusses the resource management using adaptive optimal buffer management scheme for various message sizes. It implements a dropping policy to enhance the delivery rate and reduce the delivery delay. Our FS model will decide which users to accept by managing the bandwidth resource of the aircraft.

Many references of DTN resource management are in the form of new routing algorithms while our model is a complementary mechanism applied to the routing algorithms. The Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET) [START_REF] Huang | PRoPHET+: An Adaptive PRoPHET-Based Routing Protocol for Opportunistic Network[END_REF] eliminates the production of replications based on history encounters in order to enhance the resource utilizations. Flooding is used in Epidemic routing [START_REF] Vahdat | Epidemic routing for partially connected ad hoc networks[END_REF] where the nodes replicate and transmit the bundles to all other nodes which do not have a copy of those bundles and this replication consumes many of the network resources. Spray & Wait routing algorithm [START_REF] Spyropoulos | Spray and wait: an efficient routing scheme for intermittently connected mobile networks[END_REF] sprays a number of copies into the network and waits for at least one of these messages to arrive at destination. The prioritizations concept is implemented in MaxProp [START_REF] Burgess | Maxprop: Routing for vehiclebased disruption-tolerant networks[END_REF] and is based on scheduled transmitted and dropped packets where the historical data of past meetings is used to decide the possibility of future meetings. MaxProb routing will be used in our scenario because it the best routing algorithms among the other three to provides better performance.

FS model can be applied to any DTN routing algorithm and we argue that it can provide extra improvements in the performance as we shall see in the coming sections.

Fairness and Satisfaction Model Concept

Routing can be considered as a data push operation where each node tries to forward its data bundles to other nodes through various routes, which ensures faster and optimum performance of data delivery from a source to a destination. In the case of DTN, the process will be a store and forward one because the next nodes might not be available for some time. On the other hand, FS model can be considered as a data pull operation where the node decides which one to admit first and which to reject, from a group of interested transmitting nodes.

As shown in Fig. 1, which shows pull/push concept, there are 5 users (1 to 5 at area X) who want to send/receive data from/to 5 other users (6-10 at area Y). There are 2 aircrafts (AA and CC) in direct contact with the users while 2 other aircrafts (BB and DD) acting as intermediate nodes. The aircrafts are considered mobile intermediate routers/gateways for the users. FS model is applied at those aircrafts that while receiving the data, they can apply the model to select which to accept among the interested users. This is considered as a pull operation. Now the aircraft wants to forward the data using the routing algorithm to the next contact. This is considered a push operation. For example, aircraft AA applies the pull concept (FS model) from users 1,2,3,4 and 5, while it applies the push concept (Routing) to forward it to intermediate aircrafts BB and DD. Fig. 1. Push/Pull concept of the FS model FS model will apply fairness during the selection process and for that we will use Jains fairness index [START_REF] Jain | A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Systems[END_REF] to evaluate the model performance. Jains index will evaluate the fairness of distributed resources for a group of users competing for a common bottleneck channel at a particular time.

FS Model Design

FS model is working based on collecting feedback administration metrics data from all users of their class of service CoS, Time of their request, number of past acceptances and number of past rejections. FS model will assign a weight value for each metric and decide which user to admit first based on the highest metrics weight. Each contact will use the latest feedback administration data for his decision and these data will be updated after each contact session. The model will allocate the whole bandwidth to users one after another and will not distribute it among users. During scarce resources, if the contact carries a fragment from all users, it might end up -in the worst cases-that none of them is able to process his data because they all haven't received all their fragments. Therefore, FS model is providing fair and better resources allocation using this concept which is more useful at scarce resources only.

QoS Contact-to-Contact /End-to-End

There are requirements to provide end-to-end QoS and hop-to-hop QoS in traditional networks. However, in DTN the links are not guaranteed and the next hop arrival might not be known and there might be other local hops for the node within its area. Therefore, providing this concept might be difficult and impractical in DTN environments. Instead, we think the QoS in DTN disconnected environment should be provided per contacts because it is the main resource. Whenever a contact becomes available, it will implement the QoS requirements through collaborations with other contacts by referring to the past data recovered from the nodes. This way, providing the QoS contact-to-contact in DTN is more visible and this can be illustrated in Fig. 1 as users 1 to 5 and aircraft AA or users 6-10 and aircraft CC. Furthermore, end-to-end QoS can be provided based on the performance of the routing algorithms and the availability of contacts and this can be illustrated in Fig. 1 as users 1 to 5 and users 6-10 exchanging data using intermediate aircrafts AA, BB, CC and DD.

We will have two sets of results. The first is contact-to-contact which considers the average results of all contacts applying the model within a group of users' nodes and a particular contact. This set will evaluate the FS model performance results with respect to the satisfied users and fairness. The second set of results will be based on the end-to-end satisfied users and fairness from source to destination. It might consider multiple contacts and the routing algorithms among nodes and contacts.

Simulation Results

We will evaluate the performance of using aircrafts compared with using buses and ferries. Furthermore, we will evaluate the performance of FS model.

Simulation Scenario

The scenario assumes three remote locations, A, B and C where each area has a group of users. These locations have limited communication infrastructure and they use all types of available links including the transportation means of buses, ferries and aircrafts. As shown in Fig. 2, the areas are at the shore of a big lake where buses, aircrafts and ferries operate between the three locations. Whenever any of the vehicles (bus or aircraft or ferry) approaches the area, the ground user will try to send their data to the passing vehicle which in turn will deliver it to the next area and take other data to another area and so on.

Fig.2. FS Model Senario

Simulation Configuration

Fig. 3. Scenarios configurations in ONE simulator

We have used Opportunistic Networking Environment (ONE) simulator [START_REF] Ker\änen | The ONE simulator for DTN protocol evaluation[END_REF] which is a DTN simulator platform for testing the routing and applications protocols. FS model is configured in ONE simulator and the cluster movement model is used for the model scenario as shown in Fig. 3 which illustrates the simulator GUI. The scenario assumed three locations, i.e. A, B and C, which appears in Fig. 3 as the group of dotes. The transportation vehicle, which appears in Fig. 3 as the circle in route between the clusters locations, is moving between them and collecting data from ground user nodes to send them to other nodes in another area. Every time the vehicle passes by cluster nodes, the decisions of which nodes are selected or rejected will be updated and used by the next vehicle to enable it to take acceptance decision and so on until all vehicles pass throughout the set simulation time.

Simulation time is in seconds and it is assumed that a full day of 24 hrs is equivalent to 5000 seconds simulation time. 24 hrs is chosen because the transportation means normally have daily schedules of their journeys routes which will be repeated every day. All users and vehicles have equal data rate and ground users create and try to send messages only when the vehicle contact is their LOS range. It is also assumed that all transportation means are in continuous movements in their routes for the whole day and they only stop at the end of the day. All scenarios are run for 5000 seconds which represent 24 hrs in the scenario time. The scenarios are set for 20 users distributed randomly per location and 2 vehicles for each transportation type. Table 1 shows the setting of the various transportations means. Different vehicle speeds are used where the aircraft is the highest of 900 km/hr and the ferry is the lowest with 60 km/hr. Transportations vehicles are set to stop and wait at each cluster, i.e. the duration time they are in LOS with the ground users. Aircrafts have longer wait time because they fly at high altitude of around 11000 m; therefore, they will have longer contact time and larger coverage range. Each real value will have an equivalent simulator value based on the assumption that 24 hr is equal to 5000 seconds. The performance metric is the delivery probability, user's satisfactions, average processing delay and the fairness. It will be used to evaluate the performance of FS model for various scenarios.

Simulation Results

The simulator is run for various simulation times. From 1000 to 5000 seconds and the results are obtained as follows:

Transportation Means Comparison

Fig. 4 shows the obtained delivery probability results for a scenario of 2 vehicles and 20 users per location when FS model is enabled. The delivery probability is the overall delivered messages out of the generated one regardless of the users. It shows that aircrafts have higher delivery probability between 85-95 % compared with buses and ferries whose values range between 20 to 30%. Overall, aircrafts are having up to 62 % improvements over buses and ferries. This is due to that all users are within the aircraft range; therefore, all have better opportunities to send their data. On the other hand, buses and ferries have smaller range because they are at ground level; therefore, not all the users are in their range. Furthermore, the wait time i.e. contact LOS, is larger with the aircrafts because they are at high altitude and therefore can stay longer time over the ground users to enable them to send more data, while the buses and ferries are staying shorter time and they become out of range. This shows the potential of using aircrafts in this special situation and its usefulness at remote locations.

FS Model Comparison

The previous section compared the results of aircrafts with ferries and buses when FS model is implemented. However, in this section we will compare the results between the three transportation means with and without FS model.

Contact-to-Contact Results

The first set of results is between the ground users and the approaching contacts. The results are obtained for users' satisfaction within contacts. It does not consider the end-to-end routing of data or delivering the data to its final destination. All it considers is the transfer of the user's messages to the passing contact. Users satisfactions is a measure of how many users successfully delivered all their generated messages out of the overall available users. Fig. 5 shows user satisfaction improvements in applying FS model compared with the lack of it for the three transportation means which varies between 2-18%. Transportation type has no major impact on the model results because the outcome will mainly depend on how long the contact is available for the ground users. As mentioned earlier, messages are generated during contact time only. Therefore, buses have the best user satisfaction because they have the shortest wait time and therefore have less number of generated messages per user, which increases the user's chances of acceptance. Aircrafts have the second best user satisfaction level because they have the highest speed which means more wait time occasions for the ground users. The second set of results applies for the end-to-end concept which utilizes the routing to deliver messages to their final destinations. MaxProp [START_REF] Burgess | Maxprop: Routing for vehiclebased disruption-tolerant networks[END_REF] DTN routing is used to route the messages for the three transportation means. The obtained results in contact-to-contact section are relatively close to each other between the three transportation means. However, it is not the case with the end-to-end results which indicates a very high priority for using aircrafts compared with buses and ferries. Fig. 7 shows the user's satisfaction of aircrafts reaching up to 90 % and improvement varies between 5-18 % compared with the lack of FS model. The improvements are higher at the early runs of simulation. On the other hand, buses and ferries have very limited user satisfaction, less than 10 %. These results come to an agreement with the results of the overall delivery probability shown in Fig. 4, but the difference is that Fig. 7 results are per users who delivered all their messages, while Fig. 4 is per overall generated messages regardless of who generated them. Aircrafts have provided higher users satisfactions due to their large coverage which can reach all users, while this is not always the case with buses and ferries. Furthermore, applying FS model has improved this satisfaction by providing better allocations of resources for more users. On the other hand, there is a small drawback for the model represented in the slight increase in the processing average delay which is the average time in seconds between message creation and delivery. Applying FS model will sometimes cause the delay to increase between 0 second in the best cases to 100 second in the worst case. Aircrafts have the smallest amount of delay due to their high speed and therefore will mean quicker delivery time and hence less processing time. It is not a very critical increase, and DTN is delay tolerant and hence the extra delay is considered insignificant.

Conclusions and Future Work

Various transportation means have been proposed for carrying DTN data due to the scarce and limited resources of its environments. Aircrafts in commercial routes are compared with buses and ferries. The simulation results showed higher delivery probability for using aircrafts due to their large coverage range. Furthermore, FS model is proposed to allocate the resources among users in DTN conditions. The model showed improvements in user's satisfactions and better fairness compared with the same scenario lacking the model. However, FS model will sometimes cause a slight increase in the average processing delay which is not very significant in DTN situation.

As part of our future work, we will implement the aircraft concept together with the FS model in various and more complicated scenarios for a larger number of vehicles and users to analyze its performance.

Fig. 4 .

 4 Fig. 4. Delivery probabilty of various transportation means

Fig. 5 .Fig. 6 .Fig. 7 .Fig. 8 .

 5678 Fig. 5. Contact-to-contact users satisfactions

Fig. 8

 8 Fig.8shows the improvments in end-to-end fairness index for the various transportation means and FS model has improved the fairness for the end-to-end also by 5-18 % compared with the lack of it.

Fig. 9 .

 9 Fig. 9. End-to-end average processing delay

Table 1 .

 1 Simulator ONE settings

	Transportation	Bus	Aircra	Ferry
	Mean		ft
	Max Real Speed	120	900	60
	(Km/hr)			(32.4
				knot)
	Equivalent	33.3	250	16.6
	Simulation speed
	(m/s)
	Real Wait Time	5	30	10
	(min)
	Equivalent	17.35	104.1	34.7
	Simulation Wait
	Time (s)
	Range	5	100	5
	Km


Rabiullah Khattak
email: rabiullah.khattak@ltu.se

Anna Chaltseva

Laurynas Riliskis

Ulf Bodin

Evgeny Osipov

Comparison of Wireless Network Simulators with Multihop Wireless Network Testbed in Corridor Environment

This paper presents a comparative study between results of a single channel multihop wireless network testbed and the network simulators ns-2 and ns-3. We explore how well these simulators reflect reality with their standard empirical radio modeling capabilities. The environment studied is a corridor causing wave-guiding propagation phenomena of radio waves, which challenges the radio models used in the simulators. We find that simulations are roughly matching with testbed results for single flows, but clearly deviate from testbed results for concurrent flows. The mismatch between simulations and testbed results is due to imperfect wireless propagation channel modeling. This paper reveals the importance of validating simulation results when studying single channel multihop wireless network performance. It further emphasizes the need for validation when using empirical radio modeling for more complex environments such as corridors.

Introduction

Nowadays most of the research in the field of wireless networking is based on network simulators. Simulators are attractive for researching network protocols and mechanisms since they allow creating controlled and reproducible environments. Creating such an environments in real test beds is both expensive and time consuming. Real production networks at the same time often do not allow to obtain repeatable data sets needed for research analysis. Various network settings and large parameter ranges can be tested through simulations at low effort since creation and modification of network scenarios as well as data gathering are easy.

In this paper we study how well simulations reflect the reality with commonly used empirical models of wireless propagation channel that requires little configuration and are memory and computationally sparse. In particular, we explore disparities between simulations in ns21 and ns3 2 and testbed results for a multihop wireless network located in a corridor. This environment challenges the wireless propagation channel model present in the network simulators.

The performance of network protocols in a testbed is affected by wireless channel properties that depend on the physical environment, location and mobility of the nodes, and the external interference. Accurate wireless channel modeling for simulations is known to be difficult. The commonly used wireless propagation channel for path loss in simulators is empirically modeled, and path loss is computed depending on distance between transmitter and receiver. Consequently, accumulative interference caused by hidden terminals' concurrent transmissions, and spatial reuse ratios of testbed network may not be correctly represented by the simulators. Due to these reasons simulation results often do not match perfectly with the testbed results [START_REF] Cavin | On the accuracy of manet simulators[END_REF][START_REF] Kotz | Experimental evaluation of wireless simulation assumptions[END_REF].

The modeling of wave-guiding propagation phenomena of radio waves in corridors as well as modeling of the losses caused by reflections, diffraction and scattering of radio waves are more accurately captured by deterministic channel modeling methods [START_REF] Molisch | Wireless Communications[END_REF]. The deterministic wireless channel modeling, e.g based on ray tracing techniques require the exact knowledge of location, shape, dielectric and conductive properties of all objects in the environments and it also requires extensive computational efforts for accuracy. Thus such models are site specific. In addition these models also considerably increase simulations' run time [START_REF] Schmitz | The effect of the radio wave propagation model in mobile ad hoc networks[END_REF].

The aim of our paper is to make the wireless network researchers aware of the differences of the simulations from real wireless testbed. The differences are mainly caused by empirically modeled wireless propagation channel of the simulators. Empirical wireless propagation channel models of the simulators are simple from implementation perspective but they do not cover all the properties of the wireless propagation channel such as losses due to reflections, diffraction, scattering and penetration of the radio waves. We demonstrate the differences between simulations and testbed for two specific scenarios composed of single and concurrent flows transmissions over a single multihop path.

We find that for single flow transmissions over multiple radio links, ns2 and ns3 simulations roughly match the testbed results. Deviations between simulations and the testbed results are explained by the wireless propagation channel models used in the simulations are not accurately reflecting the accumulative interference caused by hidden terminals' concurrent transmissions and the spatial reuse ratio3 of the testbed network. This shortcoming of the wireless propagation channel model becomes more evident for simultaneous flows transmissions, which have higher strength of accumulative interference than single flow. Simulations indicate considerably worse matching of the throughput fairness of simultaneous flows with testbed results, which reveals that the wireless propagation channel models of the simulators are not correctly representing the wireless channel properties in the corridors.

The article is organized as follows. Section II presents a brief overview of the background and related works. Section III presents the details and specifications of the testbed network and experiments. Section IV gives the details of the simulation setup and also provides an overview of path loss and multipath fading models. Section V presents the comparison of the experimental results with simulations. Section VI concludes this comparative study.

Network simulators ns-2 and ns-3 are de-facto standard simulation tools in the academic networking research community. Simulations in ns-2 are constructed with C++ code and OTcl scripts; the former provides modeling of applications, simulation nodes, communication channels and other mechanisms involved in networking, while the latter is used to control simulations and define additional features, for example the network topology.

Simulations in ns-3 are fully based on C++, but can also be created with Python. The ns-3 simulator was developed from scratch and cannot directly use the code developed for ns2. Many objects are ported from ns-2 to ns-3 but not all, and hence ns-2 incorporate capabilities not present in ns-3. However, ns-3 has capabilities not implemented in ns-2 such as support for multiple interfaces on nodes, use of IP addressing and closer resemblance with the TCP/IP model, and more detailed 802.11a/b/s models.

The accuracy of wireless channel models for simulations naturally determines the quality of the outcome. It could be expected that the more detailed modeling of the IEEE 802.11 MAC protocol in ns-3 would result in more accurate results for certain network scenarios. Obviously it is expected that the simulations results may deviate from the reality. It is therefore important to understand the degree of reality reflection by the simulators.

Other studies presenting comparisons between simulations of IEEE 802.11 based networks and testbeds include [START_REF] Rachedi | Wireless network simulators relevance compared to a real testbed in outdoor and indoor environments[END_REF], which presents a comparative study between an IEEE 802.11a based testbed and three network simulators (ns-2, QualNet and OPNET). It aims to assess the relevance of these simulators in indoor and outdoor environments. The simulation results match to some extent with the testbed. The authors highlight that tuning of physical layer parameters and selected propagation models have great impact on the results. This study is conducted for a single hop network and no comparison with ns-3 simulations is presented.

In [START_REF] Baldo | Validation of the ieee 802.11 mac model in the ns3 simulator using the extreme testbed[END_REF] the authors present a validation study of the IEEE 802.11b MAC model in ns-3 by comparing simulations with testbed results. The study shows that ns-3 simulations nearly match with reality after proper tuning of the devices in the testbed. It is also shown that for mismatching between the simulation and testbed results, simulator is not always wrong but specific selection and configuration of the devices in the testbed can be culprit. However, in the testbed wireless channel propagation effects on measurements are ignored because the communication between the devices is via coaxial cables.

The authors in [START_REF] Tan | Comparing simulation tools and experimental testbeds for wireless mesh networks[END_REF] point out the disparities between a wireless network tesbed and ns-2 and Qualnet. The disparities are explored based on antenna diversity, path loss, multihop, transmission rate, interference and routing stability. However, ns-3 simulations have not been taken into account and intra-path interference 4 is discussed only for a single flow traffic over a linear multihop network. For wireless connectivity each node is equipped with D-Link DWL-G122 wireless USB adapter 5 with an omni-directional antenna. The operating system is Linux (kernel 2.6.29) and the WLAN driver is p54 6 . At the MAC layer we switched off the options for frame fragmentation, dynamic rate adaption and disabled the RTS/CTS exchange. The transceivers operate on channel 3, the transmit power at each node was set to 18dBm, the physical channel data rate is set to 11Mb/s.

We experimented with TCP traffic generated by Iperf (version 2.0.8) traffic generator. We used TCP-Cubic configured with the default settings. The routes were configured statically in order to eliminate the effect of routing protocols on network performance [START_REF] Osipov | Evaluating the effect of ad hoc routing on tcp performance in ieee 802.11 based manets[END_REF].

In the testbed we conducted two types of experiments. The first experiment (further on referred to as experiment-1) was performed with a single flow running over a different number of hops. The second experiment (further on referred to as experiment-2) was conducted with multiple TCP flows running concurrently over different number of hops. To obtain the results from both experiments, we used tcpdump (version 3.9.8) to capture all the traffic generated in the network and measure the per-flow throughput.

The setup for experiment-1 is shown in Fig. 2(a). The experiment consists of seven scenarios with different number of wireless hops for the monitored flow. In order to capture the effects of multipath fading and the accumulative interference on the network performance each scenario was repeated six times by changing the position of the source and destination node along the multihop chain for a given number of hops. The duration of each trial is three minutes.

In experiment-2 (see Fig. 2(b) ) the layout of the nodes and the network specification had been kept the same as in experiment-1. The experiment consists of six scenarios. In all scenarios the traffic is alway generated from node 8. We start experimenting with two flows of one and two hops running in parallel. In each subsequent scenario we add flows as shown in the figure. As a result in the sixth scenario we experiment with seven concurrent TCP flows. We perform two trials for each scenario and record an average value of the per-flow throughput. The duration of each experiment is two minutes. The throughput values are used to compute a fairness index as explained below.

Simulation setup

We replicate the testbed experiments in ns-2 and ns-3 simulators. Firstly, we configure the parameters of the simulators with the corresponding values in the testbed. In particular, the transmission power, characteristics of the antenna and the corresponding transmission ranges are set according to the specification of the D-Link DWL-G122 wireless USB adapter. According to the device's data sheet the transmission range is set to 100 m. According to [START_REF] Razak | How do wireless chains behave?: the impact of mac interactions[END_REF] the carrier sensing range in commercial wireless cards is twice or more than the transmission range.

On the transport layer we used TCP-Cubic as in the testbed experiments. Note that while the implementation of TCP-Cubic in ns-2 is a simulator-specific, ns3-links the real implementation from Linux via the Network Simulator Cradle (NCS).

Special attention was paid to the proper configuration of the path loss and multipath fading models in order to reflect the radio environment of the testbed. In the indoor environment, the propagation of radio waves is mainly affected by two types of losses: the path loss and the loss due to small and large scale fading. The small scale fading arises due to the multipath propagation effect and the large scale fading is due to the shadowing effect. The model which closely reflects the path loss in the indoor environment is the log-distance path loss model [START_REF] Rappaport | Wireless Communications Principles and Practice[END_REF]:

L p = L 0 + 10n log 10 d d 0 + X θ . (1)
In ( 1) n is the path loss distance exponent, d 0 is the reference distance (1 m), d is the distance in meters between the transmitting and the receiving nodes, L 0 is the reference path loss at the reference distance (dB), L p is the path loss (dB) and X θ is a log-normally distributed random variable (dB) with standard deviation σ and zero mean describing the attenuation caused by the obstacles due to shadowing effect.

Note that the value of n depends on the operating frequency and the characteristics of the propagation environment. In the case of the indoor environment, the type of the construction material and the position of the nodes within the building. In corridors due to wave-guiding propagation phenomena of radio waves, n takes values in the range [1.3, 1.9] at 2.4 GHz [START_REF] Giannopoulou | Measurements for 2.4 GHz spread spectrum system in modern office buildings[END_REF][START_REF] Lu | Investigation of indoor radio channels from 2.4 GHz to 24 GHz[END_REF]. From Friis propagation loss model L 0 = 20 log 10 4πd0 λ

where λ is the wave length in meters. Note that in our testbed there is no large scale fading due to line-of-sight communication between the adjacent nodes, therefore, X θ = 0 and (1) reduces to

L p = L 0 + 10n log 10 d d 0 . (2)
However, the small scale fading exists in the corridors due to multipath propagation. In corridors the small scale fading is described by Nakagami distribution [START_REF] Sheikh | Indoor mobile radio channel at 946 mhz: Measurements and modeling[END_REF][START_REF] Tarng | A novel and efficient hybrid model of radio multipath-fading channels in indoor environments[END_REF]. The probability density function for the Nakagami m-distribution is

pdf r = 2 Γ (m) m ω m r 2m-1 exp - m ω r 2 . (3)
In (3) r ≥ 0 is the amplitude of the received signal, Γ (m) is the Euler's Gamma function, ω = r2 is the mean square received power and m = ω 2 (r 2 -ω) 2 is the fading depth, where m ≥ 1/2. For m = 1, the Nakagami m-distribution becomes Rayleigh distribution.

In ns-3 the log-distance path loss and Nakagami fading models are implemented separately. Both models can be used and configured individually. In ns-2 the log-distance path loss model is not yet implemented, however, the Nakagami fading model is implemented together with three-log-distance path loss model. The three-log-distance path loss model of ns-2 is different from the log-distance path loss model with three distance fields namely near, middle and far. Each field has different path loss exponent. For the three-log-distance model a fourth distance field is also defined from 0 to near distance, however, the loss over this field is zero. The limits of four distance fields along with their corresponding path loss exponents are explained as

0 • • • • • • 0 d 0 • • • • • • n0 d 1 • • • • • • n1 d 2 • • • • • • n2 ∞
So each field starts at the end of the preceding one and hence the resultant three-logdistance path loss model is a continuous function of the distance:

L p =          0 d ≤ d 0 L 0 + 10n 0 log 10 d d0 d 0 ≤ d < d 1 L 0 + 10n 0 log 10 d1 d0 + 10n 1 log 10 d d1 d 1 ≤ d < d 2 L 0 + 10n 0 log 10 d1 d0 + 10n 1 log 10 d2 d1 + 10n 2 log 10 d d2 d 2 ≤ d. (4)
In ( 4) n 0 , n 1 , n 2 are the path loss distance exponents and d 0 , d 1 , d 2 are three distance fields(meter). The Nakagami fading model in ns-2 defines three parameters m for three distance fields as

0 • • • • • • m0 d 1 • • • • • • m1 d 2 • • • • • • m2 ∞
The defaults values of distances and fading parameters m in ns-2 are d 1 = 80 meter, d 2 = 200 meter, m 0 = 1.5 and m 1 = m 2 = 0.75. In the testbed the maximum distance between the adjacent nodes per hop is less than 80 meter, so ns-2 is using log-distance path loss model part of (4).

In order to find a better match of the path loss and multipath fading with the real testbed the simulations are conducted for five combinations of the path loss exponent n = 1.9 and five Nakagami fading parameters m i.e. 1.5, 1.75, 2.0, 2.25 and 2.50. Note that doing simulations with higher values of the fading parameter m is not realistic because the higher m means stronger LOS component of the propagation model. This is not the case with the D-Link devices used in the testbed, which are equipped with omni-directional antennas. The next section report the results of the analysis.

5 Comparative analysis 5.1 Experiment-1: testbed vs. simulations Fig. 3(a) and Fig. 3(b) show ns-3 and ns-2 simulations of experiment-1 along with corresponding testbed results of experiment-1. As expected the TCP throughput decreases with increasing number of hops. This is because of the increase in accumulative interference due to hidden terminal problem and the decrease in spatial reuse ratio of the network due to the exposed terminal problem.

In case of testbed results, the most severe effect of accumulative interference and lower spatial reuse ratio on the throughput is observed in the five hops scenario. This severe effect is due to the specific positions of the hidden and exposed terminals in the connected corridors. It is, however, observed that the throughput of the six hops is higher than that of the five hops due to its higher spatial reuse ratio and hence lower effect of the accumulative interference. Similarly the seven hops has higher throughput as compared to both six and five hops because of its further higher spatial reuse ratio. The higher spatial reuse ratios in the six and seven hops scenarios are attained due to the specific positions of the nodes in the corridors.

Fig. 3(a) shows that ns-3 simulated results are matching with the results from the testbed in the single hop scenario for fading parameter m = 2.0 while diverging in all other scenarios except for the six hops scenario. There the simulation results of all fading parameters m are almost identical with the testbed results. Fig. 3(b) shows that for the single hop scenario the ns-2 simulation results are almost identical with that of the testbed for fading parameter m = 2.0. The figure shows that ns-2 simulated results in three and four hops scenarios are matching with the testbed for various fading parameters m. Notably, simulations of ns-2 and ns-3 fail to reflect the higher spatial reuse ratio behavior of the six and seven hops than five hops like the testbed.

The simulation results from both ns-2 and ns-3 show that none of the fading parameters m has a persistent match with the testbed results in all multihop scenarios. We also observe that in contrast to ns-3, the ns-2 simulations have a closer match with the testbed results except for six and seven hops cases, where ns-2 results are diverging from the testbed larger than ns-3. It is observed as well that the throughput of a single hop TCP flow is higher in the testbed than the simulated results for certain values of fading parameter m. This is due to wave-guiding signal propagation phenomena not correctly captured by the propagation models in the simulators. It is, therefore, hard to conclude which of the two simulators closer reflect the reality except for stating that both simulators give a rough match of the testbed results.

Experiment-2: testbed vs. simulations

In experiment-2, from the simulations of each simulator, we are getting five plots for the average throughputs (over all scenarios) for five Nakagami parameters. So for sake of simplicity of explanation and limitation of pages of the article, we present comparison of the results with a concise performance metric call throughput fairness index. We find that fairness index behaviours of both the simulators are quite different from the testbed, which implies that average throughputs behaviours of simultaneous flows of simulations are also different from those of experiment-2 of the testbed. The details of the throughput fairness index are given as follows.

In experiment-2 we compared the performance of the simulators with the testbed using Jain fairness index f s [START_REF] Kotz | Experimental evaluation of wireless simulation assumptions[END_REF]. The index takes values between 0 and 1. In (5) X (s) i is the network's throughput share obtained by i th flow and s is the number of simultaneous flows.

f s = s i=1 X (s) i 2 s s i=1 (X (s) i ) 2 .
(

) 5
In order to use the index in the case of flows with unequal characteristics we have to relate the actual measured throughput with a throughput share of the flow under ideal sharing condition [START_REF] Jain | A quantitative measure of fairness and discrimination for resource allocation in shared systems[END_REF]. Therefore, X (s) i is computed as in [START_REF] Lu | Investigation of indoor radio channels from 2.4 GHz to 24 GHz[END_REF]. There a (s) i is the actual throughput of the i th flow measured in simulations and in the testbed and d

(s) i = a (s) i d (s) k if a (s) i < d (s) k 1 otherwise (6)
As we observe from Fig. 4 Looking at the results from ns-2 simulations in Fig. 4(b) we observe that the fairness indices increase with increasing number of simultaneous flows and hops. In scenario three the fairness index in simulations matches exactly the values in the testbed. Although the absolute values of the index obtained from ns-2 do not significantly deviate from the measured in the testbed, however the overall development of the index is different from testbed.

It is to be noted that wave-guiding propagation phenomena of radio waves in the testbed are present in both single and simultaneous flow scenarios. However, in the simultaneous flow scenarios the probability of concurrent transmissions along the multihop path is higher than single flow scenarios. This results in higher value of the accumulative interference in experiment-2. We know that wave-guiding propagation phenomena of radio waves reduce the signal strength loss as compared to common radio waves propagation in space. So due to wave-guiding propagation, the accumulative interference has higher range to affect the desired reception of the signal along the multihop path. Hence the deviations of the simulations of simultaneous flow scenarios from testbed are larger than single flow scenarios. Overall, as in the case with experiment-1 none of the simulators was able to exactly reproduce the performance of the testbed. Partially it depends on simulator specific implementation of protocols on MAC and (or) Transport layers. ns-2, for example, uses own implementation of TCP-Cubic congestion control. We however observe a better match of the network behavior produced by ns-3 simulator which uses native Linux implementation of TCP. The major problem in our opinion comes however from inability of the simulator's propagation models to capture all signal impairments mechanisms in the particular communication environment. The corridor environment of the tesbed exposes strong wave-guiding propagation phenomena, which places a great impact on the accumulative interference and the spatial reuse ratio.

Conclusion

This paper presents a comparative study between testbed and simulations of the network simulators ns-2 and ns-3. The testbed is multihop wireless network deployed in corridors in a non-linear chain topology, which challenges the commonly used empirical models of wireless propagation channel that are currently available in these simulators. The experiments done for this study include single and concurrent flows transmissions over a single multihop path. The goal is to explore how well these simulators reflect the reality represented by this testbed carrying those flows.

Our simulations roughly match with testbed results for single flow transmissions, which cause only limited accumulated interference and allow for good spatial reuse ratio of the network. Simulations deviate however more clearly from testbed results for simultaneous flows transmissions. These transmissions increase the accumulative interference compared to single flow transmissions and thereby decreases the spatial reuse ratio of the network. In particular, for simultaneous flows transmissions simulations indicate considerably worse fairness between flows compared to testbed results. This reveals that the wireless propagation channel models of the simulators are not correctly representing the wireless channel properties in the corridors, especially in scenarios involving accumulated interference in difficult environments such as corridors.

Deterministic wireless channel modeling for example based on ray tracing techniques can better capture reality into simulators, but require exact knowledge of location, shape, dielectric and conductive properties of all objects in the environments and it also requires extensive computational efforts for accuracy. This complexity motivates the use of empirical radio modeling although its shortcomings in correctly modeling complex environments. Our paper emphasizes the need for validation when using such modeling to study single channel multihop wireless network performance for more complex environments.

Future work include to extend this study by exploring how results match between simulations and real multihop wireless networks with linear chain topology for other indoor environments as well as outdoor environments. Thereby we can further characterize possible discrepancies important to be aware of when using simulations to predict performance in real networks.

Fig. 1 .

 1 Fig. 1. Testbed: Layout of nodes in corridors.

3

  Testbed and experiments descriptionWe built an IEEE802.11b based multihop wireless testbed in an indoor corridor environment. The network consists of eight nodes placed as illustrated in Fig.1. The logical topology is a chain, the placement of nodes ensures the line-of-sight communications with the immediate neighbors. All nodes are Intel Pentium 4 based desktop PCs with 2.40 GHz processor, cache size 512 KB, RAM memory 256 MB and six USB 1.0 ports supporting the data transfer rate of 12 Mb/s.

  (a) Layout of Experiment-1. (b) Layout of Experiment-2.

Fig. 2 .

 2 Fig. 2. Setup of experiments in the testbed.

  (a) ns-3 results. (b) ns-2 results.

Fig. 3 .

 3 Fig. 3. TCP throughput in experiment-1 versus ns-3 and ns-2 results for path loss exponent n = 1.9 and different Nakagami parameters m.

  k s is the throughput share under ideal sharing conditions over k hops. It is computed by dividing the throughput T k of a single flow over k hops measured in experiment-1 by s.

  X

  (a) and Fig. 4(b) the fairness indices obtained from the ns-2 and ns-3 simulators have opposite trends for different values of simultaneous flows and the number of hops. It is observed from ns-3 simulations in the Fig. 4(a) that the fairness index is decreasing in scenarios one to four. The lowest fairness indices are observed in scenarios five, six and seven for certain values of fading parameters m. Like in the testbed, ns3 simulations of scenarios five and greater show an increasing trends in the fairness indices for different fading parameters. Clearly the fairness index obtained from ns3 simulations is lower than the one in the testbed for all values of the fading parameters. It is however worth pointing out that the overall behavior of the index for Nakagami parameters m = 1.5 and m = 1.75 matches the index's behavior in the testbed in scenarios three to seven.

  (a) ns-3 results. (b) ns-2 results.

Fig. 4 .

 4 Fig. 4. Jain fairness index in experiment-2 versus ns-3 and ns-2 results for path loss exponent n = 1.9 and different Nakagami parameters m.

ns-2 NetworkSimulator. Online. Available: http://www.isi.edu/nsnam/ns/

ns-3 Network Simulator. Online. Available: http://www.nsnam.org

Spatial reuse ratio is the total number of concurrent transmissions accommodated in network.

Interference between the packets of the same end-to-end flow due to hidden terminals.

DWL-G122 High Speed 2.4GHz (802.11g) Wireless USB Adapter. Online. Available: http: //www.dlink.com/products/?pid=334

Online.Available: http://linuxwireless.org/en/users/Drivers/p54


Bruno Sousa
email: bmsousa@dei.uc.pt

Kostas Pentikousis
email: k.pentikousis@huawei.com

Marilia Curado
email: marilia@dei.uc.pt

UEF: Ubiquity Evaluation Framework

Keywords: Ubiquitous computing, Multihoming, MIPv6, HIP, modeling and performance evaluation

Ubiquitous computing (UbiComp) systems rely on heterogeneous technologies to enable the anytime-anywhere communication paradigm. Despite the momentum that UbiComp systems have, most new proposals are typically evaluated through varying sets of criteria, making direct comparisons that are far from straightforward. In this paper, we introduce the Ubiquity Evaluation Framework (UEF) aiming to overcome the limitations of hitherto evaluation mechanisms. UEF is unique, as particular protocols of a UbiComp system can be evaluated without requiring a functional system or involving questionnaries to a group of experts in the field. UEF enables the comparison of protocols intended for the same task in the design phase, by employing objective metrics. The generic applicability of UEF is demonstrated in this paper through a case study comparing UbiComp support in two well-established mobility management protocols, namely Mobile IPv6 (MIPv6) and Host Identity Protocol (HIP).

Introduction

Ubiquitous computing (UbiComp) is a model where computers live in the world of people, aware of their location, but in a transparent form to the user [START_REF] Symonds | Ubiquitous and Pervasive Computing: Concepts, Methodologies, Tools, and Applications[END_REF]. Modern devices and networks to some degree implement this vision for ubiquitous computing as they enable users to access online services 24-hours a day at "any time" and from "any place". In principle, the choice of technologies, system architecture and protocols must be considered in the design phase of UbiComp systems. However, the evaluation approaches taken for UbiComp Systems so far [START_REF] Resatsch | Ubiquitous Computing Developing and Evaluating Near Field Communication Applications[END_REF][START_REF] Stevenson | Ontonym: A Collection of Upper Ontologies for Developing Pervasive Systems[END_REF] typically consider user-perspective ratings only, or assess a limited set of functionalities. For example, some assessments follow a prototypebased approach and thus have high development costs while not always fully representative of the final system. Others rely on user surveys requiring at least a partially complete and functional system. Moreover, when multiple choices for the software components are available, said approaches do not provide insights for the selection of the best ones during the design phase.

This paper introduces the Ubiquity Evaluation Framework (UEF), which assess the ubiquity support of a protocol by considering its technical features (i.e., those related with the capabilities of UbiComp systems, including security and extensibility), as well as protocol extensions, i.e. features that improve the capabilities of UbiComp systems, but are not mandatory. Both technical features and extensions of UbiComp systems are considered regarding the functionalities of the assessed protocol. To showcase the use of UEF we consider in this paper two mobility management protocols, namely MIPv6 [START_REF] Johnson | Mobility Support in IPv6[END_REF] and HIP [START_REF] Gurtov | Host Identity Protocol (HIP): Towards the Secure Mobile Internet[END_REF], and study their respective ubiquity support. In doing so, we demonstrate the accuracy and general applicability of UEF.

The contributions of UEF in the evaluation of UbiComp systems are threefold. First, evaluations do not require prototypes or working systems or even experts in the UbiComp area, as UEF does not rely on user interviews. Second, performance can be assessed at any phase of system development, ranging from the design phase to deployment. Third, UEF establishes objective metrics that allow the comparison of protocols intended for the same task in an objective manner. The remainder of this paper starts with Section 2, which reviews related work. Then, Section 3 introduces UEF and Section 4 assesses ubiquity support in MIPv6 and HIP based on our framework. Finally, Section 5 concludes the paper.

Related work

UbiComp systems include different components, namely the computing platform, such as the hardware technologies supported, the software platform, and the users interacting with the system [START_REF] Niemel | Survey of Requirements and Solutions for Ubiquitous Software[END_REF]. In this section, we overview the approaches so far taken when evaluating, on the one hand UbiComp systems, and on the other mobility management protocols.

UbiComp systems can be evaluated in terms of quality, which assesses the level of capabilities (i.e., technical characteristics) and the level of extensions [START_REF] Kwon | A Multi-Layered Assessment Model for Evaluating the Level of Ubiquitous Computing Services[END_REF][START_REF] Scholtz | Toward a Framework for Evaluating Ubiquitous Computing Applications[END_REF]. The assignment for each capability/item usually relies on interviews with experts in the field (e.g., with ubiquitous computing experience), giving a classification in the range {1, 2, ..., 7}. These solutions require the involvement of experts, limiting a general applicability of this type of methodology.

UCAN is a ubiquitous computing application development and evaluation process model [START_REF] Resatsch | Ubiquitous Computing Developing and Evaluating Near Field Communication Applications[END_REF] that allows the evaluation of ubiquitous applications, such as radio frequency identification (RFID) applications. The evaluation includes different stages and methods, for instance, the original idea can be evaluated using interviews, while the prototype (pilot) is assessed through user acceptance methods. Whilst UCAN requires prototypes and is tailored for applications relying on user satisfaction metrics, in [START_REF] Kwon | A Multi-Layered Assessment Model for Evaluating the Level of Ubiquitous Computing Services[END_REF] the overall system is evaluated.

Ontonym [START_REF] Stevenson | Ontonym: A Collection of Upper Ontologies for Developing Pervasive Systems[END_REF] is a framework that allows the evaluation of pervasive systems. The framework models context based on ontologies. For instance, people are modeled by using classes with different attributes such as Name and Religious-Name. The evaluation considers three aspects: design principles, (e.g., extensibility and documentation); content (e.g., clarity and consistency); and purpose (in which domain the evaluation is performed). Despite using established standards, Ontonym focuses on the context representation problem, and therefore does not provide objective and comparable metrics to evaluate UbiComp systems.

The performance of IP mobility management protocols has been a popular research topic. Due to space considerations, we only review the most salient work in this area in terms of performance metrics support. Usually, metrics include packet delivery cost, handover delay, location update cost, signaling cost, multiple interfaces and simultaneous mobility support. The packet delivery cost metric, for instance, determines the cost (e.g., processing or transmission) of the different packet delivery mechanisms (e.g., tunnel, direct) [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF]. The handover delay metric includes movement detection, address configuration, security operations and location registration [START_REF] Kong | Mobility Management for All-IP Mobile Networks: Mobile IPv6 vs. Proxy Mobile IPv6[END_REF][START_REF] Liu | Handover for Seamless Stream Media in Mobile IPv6 Network[END_REF]. The signaling cost is a compound metric that combines the packet delivery cost and the handover cost, commonly designated by location update cost [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF]. The location update cost is determined according to the network model (e.g., number of hops, number of domains, wired and wireless links), message rate and respective message length. The difference between the different proposals resides on the fact that some of them include the functions of each involved entity (e.g., home agent, correspondent node), while others only include the mobile node or only the cost of specific operations (e.g., tunneling) [START_REF] Makaya | An Analytical Framework for Performance Evaluation of IPv6-Based Mobility Management Protocols[END_REF]. The support of simultaneous mobility metrics is often neglected in evaluations, assuming fixed correspondent nodes [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF][START_REF] Makaya | An Analytical Framework for Performance Evaluation of IPv6-Based Mobility Management Protocols[END_REF], although some consider the probability of simultaneous movement as in [START_REF] Wong | Simultaneous Mobility: Analytical Framework, Theorems and Solutions[END_REF]. Paging efficiency is another metric to consider when evaluating mobility management protocols in a ubiquitous environment, specially due to energy efficiency. Paging support evaluations assess the power consumption cost, the paging delay cost but in a technology-dependent or application-dependent way [START_REF] Lee | Shall We Apply Paging Technologies to Proxy Mobile IPv6?[END_REF][START_REF] Do | A Comparison of Different Paging Mechanisms for Mobile IP[END_REF][START_REF] Tang | Paging issues and methods for multiaccess[END_REF].

Based on this survey of previous work in these two areas, we conclude that none of the existing approaches can assess protocol performance regarding its functionality and taking into consideration technical features and extensions of UbiComp systems. As will see later, UEF addresses these limitations, providing a consistent benchmark toolset for a variety of protocols.

UEF -Ubiquity Evaluation Framework

UEF defines the ubiquity metric -U M H (see Def. 1) that combines aspects of UbiComp systems with the functionalities of a protocol. Metrics and methods to assess technical features and extensions of UbiComp systems are presented first. The degree of mobility support assesses IP mobility management performance, which is then combined with features of UbiComp systems, expressing the Ubiquity metric. Definition 1 -Ubiquity is the ability to support secure and optimized mobility to enable access to services anywhere and anytime, with acceptable quality levels.

HIP √ √ √ √ -- √ √ - √ - √ 0 -0 √ ------9 MIP -- √ √ ------- √ √ - √ √ ------6 (S) √ √ √ √ √ - √ √ √ √ √ √ √ - √ √ - √ √ √ --17 (U) ------------- √ -- √ --- √ √ 4 (H) -- √ √ √ ----- √ √ - √ √ ------7

Formulation

The first contribution of UEF is the ubiquity metric that combines technical capabilities, lC, and extensions, lU , of UbiComp systems, according to the degree of mobility, Ψ ,supported by a protocol. Eq.1 formulates Ubiquity -U M H , where W lC and W lU are the weights for technical characteristics and extensions aspects, respectively. Weights are assigned as follows: W lC + W lU ≤ 1. A simple rule to assign weights relates the number of technical or extensions items and the overall number of items (technical + extensions), having W lC = nLC/(nLC + nLU ) = 0.65 and W lU = nLU/(nLC + nLU ) = 0.35.

U M H = (W lC * lC + W lU * lU ) • Ψ (1)
Technical capabilities and extensions are determined for each component of a UbiComp system, namely user (U), software (S) and hardware or computing platform (H), as shown in Table 1 andTable 2. MIPv6 and HIP fall in the software component category (S). Thus, capabilities and extensions of the protocols are evaluated by considering only the capabilities and extensions of the software component. A "

√ " in the Table entries means that the respective capability is supported; "0" means that it is not supported; and "-" means that the capability is not applicable.

UEF should be usable without full expertise in UbiComp systems and at any stage of system development (from design to deployment phases). As such, capabilities and extensions are evaluated using a Boolean scale (0-not supported and 1-fully/partially supported). Moreover, to avoid ambiguity in the evaluation, UEF employs the meaning of each capability/extension according to standard dictionaries [START_REF][END_REF]. Finally, UEF considers non-overlapping capabilities and extensions as opposed to [START_REF] Kwon | A Multi-Layered Assessment Model for Evaluating the Level of Ubiquitous Computing Services[END_REF][START_REF] Scholtz | Toward a Framework for Evaluating Ubiquitous Computing Applications[END_REF] that evaluates an item twice, namely as a capability and as extension. Each capability/extension is determined according to Eq. 2 specified in [START_REF] Kwon | A Multi-Layered Assessment Model for Evaluating the Level of Ubiquitous Computing Services[END_REF], where n is the number of supported capacities/extensions, C i is the value of the capacity (0 or 1) with M axScale = 1, and n ξ is the number of capacities/extensions that apply to the component.

C ξ = n i=1 C i n ξ • M axScale , with ξ ∈ {u, s, h} (2)
UEF assesses mobility support through the degree of mobility Ψ , which is a compound metric of performance and cost aspects of the mobility management process, as per Eq. 3. Common approaches, such as [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF][START_REF] Makaya | An Analytical Framework for Performance Evaluation of IPv6-Based Mobility Management Protocols[END_REF] consider cost aspects only. Performance aspects include the level of energy efficiency Ef and handover procedure preparation rate λ prep . Cost aspects include handover Hc and signaling Sc costs as well as the handover procedure finalization rate λ f ina . The term N •maxS corresponds to the number of cost aspects and the maximum cost value, respectively, with maxS = 1, and N = 3. The weight W m is used to distinguish performance and cost aspects.

Ψ = N • maxS + W m (Ef + λ prep ) -(1 -W m ) • (Hc + Sc + λ f ina ) (3)
In IP mobility management evaluation, UEF includes metrics for energy efficiency and the procedure preparation rate, a significant improvement over previous work that only evaluates mobility management performance by assessing costs [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF][START_REF] Makaya | An Analytical Framework for Performance Evaluation of IPv6-Based Mobility Management Protocols[END_REF][START_REF] Wong | Simultaneous Mobility: Analytical Framework, Theorems and Solutions[END_REF]. UEF explores the end-host mobility approach, when all procedures are triggered by the mobile node, and includes support for simultaneous mobility events. In the latter case, the correspondent node plays a dual role as it is also a mobile node. The procedure rates include the procedure preparation rate before the handover, λ prep , and the procedure finalization rate, λ f ina , after the handover. Considering a total of n proc procedures and n proc = n prep + n f ina , the rates are λ prep = n prep /n proc and λ f ina = n f ina /n proc .

The handover cost, Hc, quantifies cost in terms of handover delay, d, measuring the sum of procedure delays in the n e entities. Handover delay, d, is determined as follows: d = ne e=1 nje j=0 ∆t procj,e , with n je procedures executed at entity e with ∆t proc processing time. The handover cost, Eq. 4, includes the cost of procedures invoked after handover only. A sigmoid function normalizes delay values that have increased granularity by a factor of d g (=1000 by default). Handover cost could consider other metrics, such as handover delay at Layer 2 [START_REF] Liu | Handover for Seamless Stream Media in Mobile IPv6 Network[END_REF], but this would tie UEF to a specific radio access technology and prevent us from apportioning the performance of the assessed protocol.

Hc = 1/(1 + e - √ d+1 dg ) (4)
The signaling cost, Sc, Eq.5, determines the procedure overhead (Gp set) of the protocol, as in [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF].

Sc = 1 + e - √ Cp/max(Cp) -1 ∀ p ∈ Gp (5)
The relation between the sum of all procedures C p and the maximum cost max(C p ) of all the procedures is the base for the signaling cost formulation. In UEF, the cost of a procedure C p is formulated according to the message size, the message transmission frequency or the number of transmissions, and the processing cost Φ of each entity. Common approaches rely mainly on the message size only [START_REF] Wang | Signalling Analysis of Cost-Efficient Mobility Support by Integrating Mobile IP and SIP in All IP Wireless Networks[END_REF]. Eq. 6 determines the cost of a procedure invoked nI times, with message size L i and transmitted nT x times or at a frequency Q i .

C p = nI n=0 nT x t=1 nM i=1 L n,t,i • Q n,t,i • e∈{••• } Φ n,t,i,e (6)
For the number nT x and frequency Q i of transmissions we make the following assumptions:

-Q i = 1, if nT x > 1, i.e. when there are retransmissions; -nT x = 1, if Q i > 1, for instance, messages that do not require any reliability but are sent frequently (e.g., router advertisements).

The processing cost, P c, of an entity Φ e is the relation between the processing cost of a procedure and the number of interfaces of entity e, Φ e = N if e •P c e . UEF considers multihomed nodes and does not rely on upper-layer parameters (e.g., session rate) to determine the processing cost. Instead, P c corresponds to the relation between the processing delay pDelay and the operation complexity, as given in Eq. 7. Complexity is modeled by the number of operations nOper, and the size of data structures sizeData. Whilst the size of the data structures can be dynamic, UEF only considers the size of a single record, for simplicity. When procedures do not involve data structures, sizeData = 0. sizeData differs from message length, since it accounts for the size of structures necessary to perform the operations in procedures (e.g., record in a routing table ).

P c = [nOper • (1 + sizeData)] • pDelay (7)
Energy efficiency, Ef , considers the rates of reducing the active area λ rdActArea and the paging cost λ rdP agC , as per Eq. 8. N is the number of cost aspects and maxS the maximum value of these costs, with N = 1 and maxS = 1. Power saving mechanisms at the physical layer are not included in order to meet the technology independence requirement.

Ef = N • maxS + W e • λ rdP agC -(1 -W e ) • λ rdActArea (8)
The rate of active area reduction, λ rdActArea , is the relation between the domain dArea and the paging area pArea: λ rdActArea = dArea-pArea dArea . The paging area is determined by considering the node that initiates paging till the endpoint (e.g., mobile node). Additionally, the area can consider the radius coverage (in meters), or simply the number of hops between the paging initiator and the endpoint as discussed in [START_REF] Lee | Shall We Apply Paging Technologies to Proxy Mobile IPv6?[END_REF]. The domain area is limited by the prefix management entity, for instance an IPv6 router, and the endpoints. Values close to 1 indicate that the paging area is too small, with reduced costs, but with few optimizations. The paging cost, P agC is given in Eq. 9, where L represents the message size, transmitted nT x times. Each entity e participating in the paging group Ga has N if interfaces in idle state during ∆t interval, and for each paging message the processing cost is P c. The paging group Ga includes all entities involved in paging signaling.

P agC = nT x t=1 L t • ∀e∈Ga (N if e,t • P c e,t • ∆t e,t ) (9)
The processing cost, P c, is determined according to Eq. 7 with sizeData = 0. The ratio of paging cost reduction, λ rdP agC , is the relation between paging cost at effective idle intervals, ∆t idle, and theoretical intervals, ∆t T idle, during which the MN could remain in idle state (e.g., no data transfer and no mobility management signaling exchanges), λ rdP agC = P agC ∆t idle P agC ∆t T idle .

UEF Use Case: Ubiquity in MIPv6 and HIP

After detailing the UEF specification, we now proceed with the evaluation of MIPv6 [START_REF] Johnson | Mobility Support in IPv6[END_REF] and HIP [START_REF] Gurtov | Host Identity Protocol (HIP): Towards the Secure Mobile Internet[END_REF] with respect to the effectiveness of their ubiquity support.

Ubiquity Derivation for MIPv6 and HIP

HIP supports mobility management with the RendezVous extension. The ubiquitous support is determined according to its technical capabilities and extensions.

Using Tables 1 and2 we see that MIPv6 and HIP have similar technical capabilities, that is, lC M IP = lC HIP = 26/32 = 0.81. MIPv6 does not gain any from its extensions support, lU M IP = 6/17 = 0.35, but HIP does, lU HIP = 9/17 = 0.53. The formulation of the degree of mobility for the registration (RG), security (AA), address configuration (AD) and movement detection (MD) procedures are based on Eq. 2. Mobility management in MIPv6 includes Mobile Node (MN), Home Agent (HA) and Correspondent Node (CN) entities. In HIP, the HIP Initiator (HI), the RendezVous Server (RVS) and the HIP Responder (HR) manage mobility. As some procedures rely on IPv6 mechanisms, we employ E1 as MN or HI, E2 as HA or RVS and E3 as CN or HR.

MIPv6 registration is based on binding messages. MN sends Binding Update (BU) to the HA and CN when new addresses are available. BUs are retransmitted till the reception of a Binding Acknowledgment (BA). Moreover, binding can be refreshed using Binding Refresh Request (BRR), or the CN can inform the MN about errors using Binding Error (BE) message. The cost is determined similarly to HIP.

HIP registration is performed in three steps (s1, s2, s3 ) according to the handover phase. In s1, HI and HR register with RVS using I1, R1, I2 and R2 messages. The cost of this step is determined by Eq. 10. The base exchange (step s2 ) corresponds to a four-way handshake between HI and HR and only involves the RVS to forward I1 messages.

C RG-HIPs1,s2 = nT x t=1 L I1,t • e∈{HI,RV S,HR} (N if e,t • P c e,t ) + L R1 • e∈{HI,RV S,HR} (N if e • P c e ) + L I2 • e∈{HI,RV S,HR} (N if e • P c e ) + L R2 • e∈{HI,RV S,HR} (N if e • P c e ) (10)
After the handover (step s3 ), HI needs to update the locator information on dest nodes, which include RVS and HR. For such purpose, it employs the update message with locator information, and issues an echo request, which status of update is reported in the echo response message. The registration cost of the update is determined according to Eq. 11.

C RG-HIPs3 = nT x t=1 L U P D(locator),t • e∈{HI,dest} (N if e,t • P c e,t ) (11)
+ L U P D(echo req) • e∈{dest,HI} (N if e • P c e ) + L U P D(echo resp) • e∈{HI,dest} (N if e • P c e )
MIPv6 can rely on external mechanisms, such as IPsec, to enable security. Nevertheless, our study focuses on the return routability, since it is an internal procedure of MIPv6 that allows the verification of addresses when the MN is at foreign networks. Eq. 12 formulates the cost of this procedure relying on the Home Test init (HoTI), Care-of Test init (CoTI) and respective reply messages. Integrity protection and encryption is performed in HIP by employing the Encapsulating Security Payload (ESP). The registration cost already includes the security cost C AA-HIP , as ESP security association is part of the base exchange.

C AA-M IP = nT x t=1 L HoT i,t • e∈{M N,HA,CN } (N if e,t • P c e,t ) (12)
+ nT x t=1 L CoT i,t • (N if M N,t P c M N,t + N if CN,t P c CN,t ) + L HoT • e∈{M N,HA,CN } (N if e • P c e ) + L CoT • (N if M N • P c M N + N if CN • P c CN )
Address configuration in MIPv6 and HIP nodes relies on IPv6 schemes that include Router Solicitation (RS), Router Advertisements (RA) and the messages in the Duplicate Address Detection (DAD) mechanism. Neighbor Solicitation (NS) messages are sent to multicast addresses with the reply of Neighbor Acknowledgement (NA) messages. In addition, IPv6 routers (at home and foreign networks, Rtr h and Rtr f , respectively) advertise prefixes via Router Advertisements (RA) frequently, while Router Solicitation messages are retransmitted on error events. Eq. 13 defines the cost of address configuration.

C AD = nT x t=1 L N S,t • e∈{E1,E2,E3} (N if e,t • P c e,t ) + L N A • e∈{E1,E2,E3} (N if e • P c e ) + nT x t=1 L RS,t • e∈{E1,E2,E3} (N if e,t • P c e,t ) + Q RA home • L RA home • e∈{E1,Rtr h ,E2,E3} (N if e • P c e ) + Q RA f oreign • L RA f oreign • e∈{E1,Rtr f ,E2,E3} (N if e • P c e ) (13)
Movement detection also relies in IPv6 schemes, namely the Neighbor Unreachability Detection (NUD) mechanism. NUD uses solicited NS and NA messages and the respective cost is formulated according to Eq. 14.

C M D = nT x t=1 L N S,t • e∈{E1,E2,E3} (N if e,t • P c e,t ) + L N A • e∈{E1,E2,E3} (N if e • P c e ) (14)
Finally, MIPv6 includes the tunnel cost, since packets can be forwarded to MNs at foreign networks via tunnels. The cost of tunnel establishment is determined in an application independent fashion, as tunneling relies on IPv6 encapsulation mechanisms. The tunnel establishment cost, as per Eq. 15, considers only the size of message headers and respective processing cost in MN, HA and CN.

C T U = nT x t=1 HdrT M N,t • (N if M N,t • P c M N,t ) (15)

Evaluation Methodology

In this UEF study case we consider the problem of choosing a protocol for a UbiComp system at the design phase. Hence, the evaluation does not target a particular scenario with specific technologies, but examines ubiquity support in generic UbiComp systems. Thus, MIPv6 and HIP protocols are assumed to be operating with the maximum message length (e.g., with all options filled). In addition, only the mandatory messages are considered; optional messages, such as HIP -NOTIFY, are not included.

The nodes (e.g., MN, HI, CN, HR) are configured with three interfaces, nif = {1, 2, 3}, a common configuration in mobile terminals. Moreover, MN/HI can communicate simultaneously with several correspondent nodes, ncns = {1, 5, 10}, within which different types of applications can be used. In addition, nodes move with different speeds, thus having to handle a number of handovers nho = {10, 50, 100, 200}. All sessions last 300s. Assuming we are in the design phase of a UbiComp system, values of processing delay cannot be measured (as no prototype is available). Thus for this study case, we take all processing times to follow normal and exponential distributions, with different means {1s, 10s}. Different distributions are used to accommodate different modeling mechanisms for processing times. The analytical evaluation has been performed using the R framework [START_REF] Development | R: A Language and Environment for Statistical Computing[END_REF] and considering that both MIPv6 and HIP do not include energy efficiency mechanisms Ef = 0, as no paging schemes are incorporated.

We use ubiquity weights W lC = 0.65 and W lU = 0.35 according to number of items in technical and extensions categories. The degree of mobility weight is equal to W m = 0.5, as no energy efficiency mechanisms are considered and thus the degree of mobility relies mainly on the cost. Values higher then 0.5 tend to neglect the impact of cost in mobility support.

Results

Figs. 1 and 2 present our evaluation results ordered by the number of handovers and type of protocol under study. For instance, 200 HIP is an HIP study case with 200 handovers. Statistical significance is based on 100 runs for each case.

UEF can assess ubiquity taking into consideration protocol functionalities in different conditions that UbiComp systems can face. Under mobile scenarios, the number of handovers impacts the performance of mobile management protocols. All procedures required to handle mobility are triggered often, introducing degradation in the performance, (Fig. 1), as signaling overhead increases, (Fig. 2). The number of handovers also impacts handover performance; observe the difference between single and 10 simultaneous nodes on left and right sides of Fig. 1, respectively. Location updates, due to mobile events, need to be forwarded to more nodes.

Results also put in evidence the particularity of UEF that considers protocol functionalities in the assessment of ubiquity and degree of mobility. In the UEF study case, HIP supports mobility better (∼ 2.4) than MIP (∼ 1.9 for 10 handovers with 10 simultaneous nodes) since some procedures are triggered before handover, as opposed to MIP, where procedures run after the handover.

Conclusion

We introduced UEF, a framework that assesses the ubiquity support of a protocol in an objective manner by considering multiple aspects of UbiComp systems regarding protocol functionalities. Evaluations carried out with UEF do not require any working system or prototype, as UEF metrics can be used at any phase in the development of a UbiComp System, ranging from system design to deployment. In addition, surveys involving users or field experts are not necessary. We used UEF in a study case to objectively evaluate MIPv6 and HIP concerning mobility management support. The study case showed that HIP supports ubiquity more efficiently than MIPv6, and for that reason HIP is a potential candidate to be deployed on UbiComp systems where mobility management and security are a concern.

To sum up, UbiComp systems integrate network protocols for diverse tasks. As multiple protocols can seemingly perform the same task, the choice of the most suitable and best-performing protocol is not always straightforward. UEF enables researchers and practitioners alike to objectively quantify the value of different technical features and extensions for mobility management in UbiComp systems.

Fig. 1 :

 1 Fig. 1: Single (left) and 10 nodes (right) Ubiquity and degree of Mobility boxplots using UEF.

Fig. 2 :

 2 Fig. 2: Single (left) and 10 nodes (right) Handover and signalling costs boxplots using UEF.

Table 1 :

 1 Technical Capabilities of UbiComp systems in UEF for (S) -Software, (U) -User and (H) -Hardware components (with a total of 39)

	-	√ √ √ √ √ √ √ √	---	√

Table 2 :

 2

	Authentication	Authorization	Automation	Autonomy	Context Reusability	Durability	Entity Tracking	Identity Tracking	Inferred Context	Location Tracking	Negotiation	Response Time	Seamlessness	Self-Control	Service Coverage	Standardization	Trust	User Context	User preference	User profile	User Satisfaction	Utility

Extensions of UbiComp systems in UEF for (S) -Software, (U) -User and (H) -Hardware components (total of 22).

Acknowledgment

The first author acknowledges the support of the PhD grant SFRH/BD/61256/2009 from Ministério da Ciência, Tecnologia e Ensino Superior, FCT, Portugal. This work is supported by CoFIMOM project PTDC/EIA-EIA/116173/2009.