Mobility is a fundamental aspect of human life, and mobility data offers valuable insights into user behavior. Yet, this data also exposes users to privacy risks given pattern unicity in their trajectories, i.e., the singularity in the displacements made by users. Existing strategies to quantify such user exposure either focus only on the sequences of places visited by each user, as the widely used uniqueness measure, or are tied to specific attack models. We here introduce MoBES, a novel, scalable, customizable and highly interpretable measure of user exposure in mobility data. MoBES leverages multiple existing metrics to build a multi-dimensional space, which in turn is used to capture each user's mobility signature behavior. MoBES quantifies user exposure based on how distinct a user's signature is from her neighbors in the defined metric space. As such, MoBES is designed to be a fundamental expression of user behavior, and not tied to any specific attack model. We evaluate MoBES on a real mobility dataset, showing that it effectively captures user exposure within the behavioral metric space. We also compare MoBES with the uniqueness measure, showing that MoBES is able to uncover users who, even though visiting the same places as others in the crowd, are still at risk of exposure due to the unicity of their mobility behavior.

Vision Transformer (ViT) is a deep learning model that plays a significant role in advanced computer vision and pattern recognition tasks but faces challenges in inference due to high computational costs and energy consumption. Split computing, which distributes the load between an edge server and a mobile device, is proposed to address these issues. Unfortunately, most literature focuses on split computing for CNNs, with limited attention to ViT split computing. In this paper, we explore the use of split computing to optimize ViT's inference process by limiting usage of bandwidth. We propose compressing the latent space data by introducing more sparsity in the intermediate features. This sparsity is then exploited through a compression algorithm before transmitting the data through a communication channel. To understand and explain the performance of our approach, we analyze the latent space data using several metrics. Our approach obtains a significant compression ratio without causing a substantial decrease in accuracy. It is computationally efficient and do not require retraining the model.

Edge Computing (EC) is a computational paradigm that involves deploying resources such as CPUs and GPUs near end-users, enabling low-latency applications like augmented reality and real-time gaming. However, deploying and maintaining a vast network of EC nodes is costly, which can explain its limited deployment today. A new paradigm called Vehicular Cloud Computing (VCC) has emerged and inspired interest among researchers and industry. VCC opportunistically utilizes existing and idle vehicular computational resources for external task offloading. This work is the first to systematically address the following question: Can VCC replace EC for low-latency applications? Answering this question is highly relevant for Network Operators (NOs), as VCC could eliminate costs associated with EC given that it requires no infrastructural investment. Despite its potential, no systematic study has yet explored the conditions under which VCC can effectively support low-latency applications without relying on EC. This work aims to fill that gap. Extensive simulations allow for assessing the crucial scenario factors that determine when this EC-to-VCC substitution is feasible. Considered factors are load, vehicles mobility and density, and availability. Potential for substitution is assessed based on multiple criteria, such as latency, task completion success, and cost. Vehicle mobility is simulated in SUMO, and communication in NS3 5G-LENA. The findings show that VCC can effectively replace EC for low-latency applications, except in extreme cases when the EC is still required (latency < 16 ms ).

Edge computing (EC) consists of deploying computation resources close to the users, thus enabling low-latency applications, such as augmented reality and online gaming. However, large-scale deployment of edge nodes can be highly impractical and expensive. Besides EC, there is a rising concept known as Vehicular Cloud Computing (VCC). VCC is a computing paradigm that amplifies the capabilities of vehicles by exploiting part of their computational resources, enabling them to participate in services similar to those provided by the EC. The advantage of VCC is that it can opportunistically exploit part of the computation resources already present on vehicles, thus relieving a network operator from the deployment and maintenance cost of EC nodes. However, it is still unknown under which circumstances VCC can enable low-latency applications without EC. In this work, we show that VCC has the potential to effectively supplant EC in urban areas, especially given the higher density of vehicles in such environments. The goal of this paper is to analyze, via simulation, the key parameters determining the conditions under which this substitution of EC by VCC is feasible. In addition, we provide a high level cost analysis to show that VCC is much less costly for a network operator than adopting EC.

Vehicular Ad hoc Networks (VANETs) offer a promising approach to enhancing road safety. Cooperative Awareness Messages (CAM) is an essential service in VANETs, allowing vehicles to transmit radio beacons containing their positions and velocities. These messages inform nearby vehicles about the traffic situation. This paper focuses on Extended Cooperative Awareness Messages (ECAM), which include additional information about nearby vehicles. ECAM beacons consist of a vehicle's speed, position, and data on the positions and velocities of other vehicles in its vicinity. This comprehensive information enables nearby vehicles to understand the traffic situation and take appropriate actions to prevent potential collisions. Studies demonstrate that ECAM has the potential to significantly improve road safety by providing comprehensive and up-to-date traffic information. This paper uses stochastic geometry to evaluate different versions of ECAM services and compare the results with simple simulations. The evaluation assumes random vehicle placement using a homogeneous Poisson Point Process and models the ECAM service using the Matern Point Process.

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure, and service providers must adapt to users’ different mobility patterns, which can be challenging, especially for location-based services (LBS). Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users’ privacy and mobility when offloading to the edge. Different privacy and mobility scenarios are simulated and discussed to shed light on the trade-offs (e.g., privacy protection at the cost of increased latency) among privacy protection, mobility, and offloading performance.

WiFi-based crowdsensing is a major source of data in a variety of domains such as human-mobility, pollution-level estimation, and, opportunistic networks. MAC randomisation is a backbone for preserving user-privacy in WiFi, as devices change their identifiers (MAC addresses). MAC association frameworks in the literature are able to associate randomized MAC addresses with a device. Such frameworks facilitate the continuation and validity of works based on device-based identifiers. In this paper, we first question and verify the reliability of these frameworks with respect to the datasets (scenarios) used for their validation. Indeed, we observe a substantial discrepancy between the performances obtained by these frameworks when confronting them with different contextual environments. We identify that the device heterogeneity in the input scenario is privacy-preserving. Henceforth, we propose a novel metric: randomization complexity, capable of successfully catching the degree of randomization in evaluated datasets. Existing and new frameworks can thus be benchmarked using this metric to ensure their reliability for any datasets with similar or lower randomization complexities. Finally, we open discussions on the potential impact of the benchmarks in the domain of MAC randomization.

In this paper, we propose the first framework that introduces the concept of the user’s bounded trajectory. We propose to leverage the signal strength of users’ public WiFi probe requests collected from measurements of multiple deployed WiFi sniffers. First, we investigate and characterize errors in RSSI-based radial-distance (between the user and each sniffer) estimation. Then, we approximate such radial distances leverage and deduce bounds associated with a user’s position. Finally, we infer a user’s bounded trajectory using the spatiotemporal bounds of users’ locations over time. We guarantee the bounds to enclose a user in space and time, with 95% confidence and a 10% margin of error. Using real-world and large-scale synthetic datasets under heterogeneous contexts and wireless conditions, we infer trajectories with bounds’ width of less than 10m in 70% of cases with users’ inclusiveness close to 100%.

Bluetooth devices naturally emit many public signals. It opens new paths for passive mobility analysis, and allows building larger datasets direly needed by the research on mobile systems, but also raises new practical challenges. One of them is the correlation between the public packets and the emitters. The Bluetooth standard forces devices to change the identifier they embed within the public packets regularly, but we show in this paper it does not prevent packet correlation. The number of devices changing their MAC address at any given time is expectedly small; we only need to find a property differentiating a handful device at a time for MAC association. In this paper, we propose such a property and demonstrate the efficiency of the association strategy on Bluetooth Low Energy.

The use of vehicle's on-board sensors in increasing the perception of cooperative vehicles is becoming prevailing and essential for the road safety. However trusting only on the on-board sensors could limit the perception of the cooperative vehicles for example an object/vehicle can not be detected if it is impeded by some obstruction. The perception efficiency of vehicles can be increased beyond their field of view (FoV) by exchanging the sensor detected information through V2X communication. The perception of vehicles can be more accurate as much as frequent they share the information. However by transmitting numerous messages could saturate the communication channel and deteriorate the perception improvement specially in the dense circumstances. In this paper we address both these issues and propose two different schemes, (i) an information propagating mechanism to increase the cooperative vehicle's perception above their FoV and (ii) an optimal dynamic cooperative priority based beaconing model to coup with the channel congestion. By using the information propagation mechanism we can increase the perception of cooperative vehicles up to their maximum communication range and on the other end probability based dynamic cooperative priority beaconing scheme selects the most suitable cooperative vehicle to transmit based on its on-board sensor facility. These proposed mechanisms immensely decrease the contention on the channel while extend the perception beyond the FoV. We evaluate both the mechanisms for the performance in terms of vehicles awareness, channel load and communication range through simulations for several vehicle densities. The simulation results show that these proposals provide better performance compare to the classical beaconing schemes.

With growing privacy concerns over the last decade, two of the most notable wireless technologies – i.e., BLE and WiFi – are being more and more investigated in terms of privacy vulnerabilities. In this paper, we explore this problem, prospect the related consequences, and alert the need for privacy-preserving public packets. We identify key flaws in the current design of public packets like beacons and probe requests. We discuss them as the cause of privacy issues that require the community’s attention. We address the flaws in detail and propose solutions that facilitate the devices to protect user privacy. We also give recommendations based on the findings to the standard.

In recent developments in machine learning, a trend has emerged where larger models achieve better performance. At the same time, deploying these models in real-life scenarios is difficult due to the parallel trend of pushing them on end-users or IoT devices with strong resource limitations. In this work, we develop a novel technique for executing parts of a single model successively through multiple devices (IoT, edge, cloud) while respecting each device’s resource limitations. For that, we introduce a new offloading mechanism where, during computation, a decision can be made to offload work, together with the ability to exit early in the computation with intermediate results. The decision itself is tuned through Deep Q-Learning.

This paper focuses on offloading multitasking applications to a Mobile Edge Computing (MEC) environment. We address this issue through a mobile user's perspective that is seeking to obtain the execution result of a resource-hungry multitasking application, possibly through offloading some tasks to a mobile multiple edge servers' environment. The completion time of the application is constrained by a predefined strict deadline and the offloading decision aims to minimize the terminal's energy consumption. Moreover, the multitasking application is modeled by a weighted Directed Acyclic Graph (DAG) which characterizes the dependencies between tasks. To tackle this issue, we first model the multitasking offloading problem in a mobile multiple edge servers' environment as a Zero-one Integer Programming problem. Then, we propose an efficient adaptive offloading algorithm, named eTOMEC (Elastic Tasks graph Offloading for MEC), which decides which task must be offloaded and accordingly selects the edge server that will perform the task execution. Compared to many state-of-the-art offloading approaches, our proposal allows parallel offloading of tasks to several edge servers. Assessment results show that our proposal achieves better performances in terms of completion time for the application and energy consumption of the user's terminal.

Mobile Edge Computing is a promising paradigm that can provide cloud computing capabilities at the edge of the network to support low latency mobile services. The fundamental concept relies on bringing cloud computation closer to users by deploying cloudlets or edge servers, which are small clusters of servers that are mainly located on existing wireless Access Points (APs), set-top boxes, or Base Stations (BSs). In this article, we focus on computation offloading over a heterogeneous cloudlet environment. We consider several users with different energy—and latency-constrained tasks that can be offloaded over cloudlets with differentiated system and network resources capacities. We investigate offloading policies that decide which tasks should be offloaded and select the assigned cloudlet, accordingly with network and system resources. The objective is to minimize an offloading cost function, which we defined as a combination of tasks’ execution time and mobiles’ energy consumption. We formulate this problem as a Mixed-Binary Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation-based heuristic approach that relies on the Lagrangian decomposition method. To solve the subproblems, we also propose a greedy heuristic algorithm that computes the best cloudlet selection and bandwidth allocation following tasks’ offloading costs. Numerical results show that our offloading policy achieves a good solution quickly. We also discuss the performances of our approach for large-scale scenarios and compare it to state-of-the-art approaches from the literature.

Coupling Software Defined Networking (SDN) and Network Function virtualization (NFV) has proved to be a promising paradigm for flexible resource provisioning in future networks. Network slicing is a very recent methodology that can be used in this paradigm for accommodating new services with wide different requirements over the same physical network. In this work, we propose a mathematical formulation of an optimization problem for an end-to-end (E2E) network slices deployment for different 5G-based use-cases. Each use case such as video streaming, intelligent transport, e-Health and public safety, has its own availability, reliability and delay tolerance requirements. Then, in view of the fact that the optimization problem is NP-Hard, a low-cost and efficient heuristic algorithm has been proposed. Last, the efficiency of the proposed algorithm is validated through extensive simulation. Applying our algorithm improves the quality of service (QoS) afforded to the users.

In this paper, we focus on tasks offloading over two tiered mobile cloud computing environment. We consider several users with energy constrained tasks that can be offloaded over cloudlets or on a remote cloud with differentiated system and network resources capacities. We investigate offloading policy that decides which tasks should be offloaded and determine the offloading location on the cloudlets or on the cloud. The objective is to minimize the total energy consumed by the users. We formulate this problem as a Non-Linear Binary Integer Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation heuristic based on Lagrangian decomposition approach. To solve the subproblems, we also propose a greedy heuristic that computes the best cloudlet selection and bandwidth allocation following tasks' energy consumption. We compared our proposal against existing approaches under different system parameters (e.g. CPU resources), variable number of users and for six applications, each having specific traffic pattern, resource demands and time constraints. Numerical results show that our proposal outperforms existing approaches. We also analyze the performance of our proposal for each application.

In this paper, we focus on vehicular safety applications based on the Dedicated Short Range Communication (DSRC) standard. We propose a new mechanism to alleviate channel congestion by reducing the beacons load while maintaining an accurate awareness level. Our scheme is based on the collective perception concept which consists in sharing perceived status information collected by vehicles equipped with different types of sensors (radars, lidars, cameras, etc.). To achieve our goal, we propose two main schemes. The first one consists in implementing the collective perception capability on vehicles and adding a new category of status messages to share locally collected sensor data in order to reduce channels load and enhance vehicles' awareness. The second scheme concerns the accuracy level of the received information from the collective perception enabled vehicles by fixing a prior error threshold on the position. The method proposed is validated by simulations and the results obtained are compared to those of an application based on the traditional beaconing scheme of the IEEE802.11p standard. The simulations show that the proposed scheme is able to significantly reduce the load on the control channel incurred by the beacons and the packet error ratio for different network densities and built-in sensors characteristics.

The recent advances in wireless sensors and Unmanned Aerial Vehicles have created new opportunities for environmental control and low cost aerial data gathering. In this paper, we propose to use an Unmanned Aerial Vehicle (UAV) for data gathering. Basically, we have proposed a method for UAV path planning based on virtual forces and potential fields. In addition, and more importantly, we present a new approach to compute the attractive forces of the potential field.

In this paper, we investigate the use of a swarm of drones as mobile data gathering sinks for scattered wireless sensors over large areas. Precisely, we address the path planning issue with the objective of minimizing the drones' travel duration. Several criteria are also considered, such as: 1) energy autonomy of the drones, 2) a good fairness regarding route lengths, 3) collision avoidance and 4) drones' tracking enabled by the transmission of their positions to terrestrial base stations. These base stations, and consequently the drones' paths, must be carefully determined in the aim to statistically guarantee a minimum threshold on the delivery ratio of drones' position packets. The problem is formalized as a multiple Traveling Salesman problem, which is known to be NP-Hard. To cope with the computational complexity that rises for realistic parameters, we propose a heuristic approach based on a column generation approach.

The high densities of network nodes has made spatial reuse an essential characteristic of modern wireless networks. In this paper, we evaluate the maximum throughput of Carrier Sense Multiple Access (CSMA) for Vehicular Ad-hoc Networks (VANETs) when spatial reuse is taken into account. We begin our study by extending a simple stochastic model in order to fit a VANET pattern and to obtain the spatial density of throughput in terms of the main network parameters. This model uses a Matern selection process with a random pattern of nodes distributed as a Poisson Point Process (PPP). Each node of the process receives a random mark and the nodes that have the smallest mark in their neighborhood are elected for transmission. We study both 1D and 2D network cases with an SIR (Signal over Interference Ratio) model. In order to verify the correctness of the model, extensive simulations are carried out using two simulation platforms: the network simulator, ns-3, and a simulator which is dedicated to CSMA systems. Fairly good matching between the results of the model and those obtained from simulators are observed, confirming the reliability of the theoretical model. Although the results did not perfectly match due to the number of assumptions made for the model, the results obtained nonetheless show the potential for a significant improvement in the overall throughput for VANETs and similar distributed networks.

The market of drones is expected to grow significantly by the near few years, while military, civil and commercial applications continue to emerge. In an earlier study we focused on the path planning problem for a swarm of UAVs for data gathering mission with the objective to minimize the travel duration with respect to the energy autonomy, a better fairness regarding tours and finally avoiding collisions among the drones of the same swarm. In this paper, we address the tracking and the data gathering issue and thus we evaluate both the received tracking and gathering packets rate under different conditions of speed and interference.

Maximizing the throughput of point-to-point communication has been the crux of wireless networks. In IEEE 802.11 networks, the first and prominent wireless technology, the model of point-to-point communication is still applicable today: the transmissions are between the wireless nodes and the access point, which usually serves as a gateway to the Internet. But this model is not well suited to more recent wireless systems such as Wireless Sensor Networks (WSNs) and Vehicular Ad Hoc NETworks (VANETs). In such networks, a very significant part of communication is between one node and its neighbors and simultaneous transmissions or, in other words spatial reuse, is required to insure good performance. When we consider communication from one node to its neighbor, an important metric is the density of successful simultaneous transmissions. Several studies such as [1], [2] have shown how this density of transmissions can be improved in Aloha or in CSMA networks. The aim of this paper is to show that the use of directional antennas can greatly improve the performance of the network in our neighbor-to-neighbor communication model because interference is greatly reduced. The model we build here allows a quantitative study of the performance and the improvement obtained with directional antennas to be be achieved. The study of Aloha (slotted and non-slotted) is very easy to accomplish and leads to closed formulas for the density of successful transmissions. The study of CSMA is more complex. We use a Matern selection process to mimic the behavior of CSMA in a random pattern of nodes distributed as a Poisson Point Process (PPP): each node receives a random mark and the nodes that have the smallest mark in their neighborhood are elected for transmission. Previous studies, such as [2], show that in CSMA networks, the density of successful transmissions is greatly influenced by the carrier sense detection threshold, which is one of the main parameters of CSMA. In this study we will assume that the carrier sense detection threshold is optimized to obtain the best performance of the CSMA network and our evaluations are performed under this condition. Our analytical models and our computation show that using directional antennas can lead to an improvement of up to more than 100% in the density of throughput compared to the normal use of unidirectional antennas.

Vehicular Networks are an ideal target for various types of rational and non-rational attackers. To cope with, reaction methods against these attacks must exist. In this direction, game theory applied to the security of wireless networks (also known as security games) can be a good way of modeling. In this paper we propose a reaction method against DOS attacks in VANET. In this method we have the choice between two proposed reaction games. Design methodology and defined metrics are inspired from game theory models. To the best of our knowledge, no similar games have been proposed before. The simulations showed the efficacy of our proposal measured by the performance of the obtained results.

Vehicular Networks must be sufficiently secured against the wide variety of security challenges to which they are exposed. Cryptography provides tools to solve many of these security problems. In this paper, we address the secure group communications problem in VANETs. By generating a secret group key that can be used to encrypt or authenticate, the members of a VANET platoon can thus guarantee secure communication between them. To address this need, we propose a new secure variant of the Diffie-Hellman algorithm for groups that we fortified by a pre-shared secret to withstand the famous Man in the Middle attack. As necessary, our proposal can be used by several types of authentication and encryption VANET applications. We demonstrate the unforgeability and the privacy of our scheme and we study its secure-efficiency.

One of the main challenges on the next generation local wireless networks lies in providing scalable media access control protocols in order to support the predicted high density. Research results have indicated that the current backoff mechanism is ineffective for dense networks, even more under the foreseen growing charge for the next five years. This paper investigates the proposal of the contention window enlargement, particularly by the reduction of time slot. The proposal intends to be implemented in the the 802.11ax amendment. Our main contribution lies in highlighting the impact of this proposal under different classes of traffic, reflecting on the access categories for data priority and their respective window time. Simulation results indicate that the proposal mitigates collision issues inherent to the backoff procedure, and it decreases frame loss. This study shows improvements over throughput and retransmissions, and it also warns to the impact on the clear channel assessment process. Results contribute with the literature pointing out the advantages and disadvantages of the adaptive proposal considering different classes of traffic and promoting the advance of the next generation local wireless networks.

In modern wireless networks especially in Machine-to-Machine (M2M) systems and in the Internet of Things (IoT) there is a high densities of users and spatial reuse has become an absolute necessity for telecommunication entities. This paper studies the maximum throughput of Carrier Sense Multiple Access (CSMA) in scenarios with spatial reuse. Instead of running extensive simulation with complex tools which would be somewhat time consuming, we evaluate the spatial throughput of a CSMA network using a simple model which produces closed formulas and give nearly instantaneous values. This simple model allows us to optimize the network easily and study the influence of the main network parameters. The nodes will be deployed as a Poisson Point Process (PPP) of a one or two dimensional space. To model the effect of (CSMA), we give random marks to our nodes and to elect transmitting nodes in the PPP we choose those with the smallest marks in their neighborhood. To describe the signal propagation, we use a signal with power-law decay and we add a random Rayleigh fading. To decide whether or not a transmission is successful, we adopt the Signal-over-Interference Ratio (SIR) model in which a packet is correctly received if its transmission power divided by the interference power is above a capture threshold. We assume that each node in our PPP has a random receiver at a typical distance from the transmitter i.e. the average distance between a node and its closest neighbor. We also assume that all the network nodes always have a pending packet. With all these assumptions, we analytically study the density of throughput of successful transmissions and we show that it can be optimized with regard to the carrier-sense threshold.

The objective of this study is to analyze a new disruptive deployment of wireless sensors in order to cope with the explosive demand for bandwidth while taking into account energy consumption considerations. The work is grounded on the idea of massive network densification by drastically increasing the number of sensors in a given area in a Time Division Duplex (TDD) mode. Using ideas from the recent Massive MIMO technology (more than 400 antennas, without any modification of the network infrastructure), we transpose the idea to a massive deployment of sensors and show the benefits of such an infrastructure. This research is expected to provide the optimal deployment of massive wireless sensor networks in terms of cost/performance/complexity/energy efficiency trade-off and define the next generation wireless Machine-to-Machine (M2M) communication.

In this paper we propose an optimized broadcasting mechanism which uses very limited signaling overhead. The main objective is to select the most appropriate relay nodes according to a given cost function. Basically, after receiving a broadcast packet each potential relay node computes a binary code according to a given cost function. Then, each node starts a sequence of transmit/listen intervals following this code. In other words, each 0 corresponds to a listening interval and each 1 to a transmit interval. During this active acknowledgment signaling period, each receiver applies the following rule: if it detects a signal during any of its listening intervals, it quits the selection process, since a better relay has also captured the packet. Finally, we split the transmission range into several sectors and we propose that all the nodes within the same sector use the same CDMA orthogonal spreading codes to transmit their signals. The CDMA codes used in two different sectors are orthogonal, which guarantees that the packet is broadcast in all possible directions.

ABSTRACT In this work, we aim to improve the gameplay of time‐sensitive video games over mobile ad hoc networks (MANETs). We focus on the disconnection problems. Precisely, when the path between a player and the game server fails, the game on the player side freezes and becomes unplayable until the network re‐establishes a new path toward the game server. In MANETs, disconnections occur mainly because of node mobility. To overcome this problem, we propose a multipath approach that we combine with an adaptive traffic balancing algorithm. Precisely, for each source–destination couple, we maintain two Optimized Link State Routing paths using the end‐to‐end delay metric. Each path carries a proportion of the game traffic. Our traffic balancing algorithm relies on path robustness estimation to dynamically balance the game traffic between the two paths. The results of our experiments, which are based on a real implementation of our proposals, show that using multipath routing with traffic balancing among different paths reduces route failures and improves the gameplay experience. Copyright © 2012 John Wiley & Sons, Ltd.

In this work, we deal with the Wireless LAN planning problem. We study this problem and we propose to couple its two major issues: AP placement and channel assignment to treat them jointly. Here, we propose a novel fast and scalable three-phase heuristic algorithm (TPHA). Our proposal is able to resolve the defined multiobjective problem to provide (1) the efficient number of Access Points (APs) to be deployed, while (2) ensuring the coverage of all Test Points (TPs) and (3) maximizing their nominal data rate. To achieve the first objective, we propose an heuristic called MCL-ILP combining the quick decision making yields from the Markovian CLustering algorithm and the exact solution provided by the Integer Linear Programming. Hence, a TPs-based Least Interfering Channel Search algorithm (TLICS) has been proposed for channel assignment to improve the throughput at TP locations. However, the Virtual Forces-based WLAN Planing Algorithm namely VFPA considers the results delivered by the two previous algorithms as an initial solution and tries to enhance it by adjusting the APs's positions and re-assigning their operating frequencies. Computational results exhibit that our proposal is highly beneficial to designing WLANs.

In this paper, we will address a WSN deployment problem. The main objectives are i) reduce the cost of deployment, ii) ensure the requested event detection probabilities, iii) guarantee the network connectivity, and iv) maximize the lifetime of the network. We will formalize the problem as multi-objective combinatorial optimization problem. To resolve the problem, we will propose a new deployment algorithm named MODA. It will be based on evolutionary and neighborhood search algorithms. The obtained results are better than the deployment strategies found in the literature.

Video transmission over wireless networks is a very challenging task. The highly variable behavior of wireless channels in such networks makes QoS provision for video transmission a difficult problem. In this paper we show that MAC-level flow control in HSDPA has a significant impact on perceived video quality. Carried out simulations in this paper show the positive impact of our algorithm on video quality.

This paper addresses the issue of wireless resource allocation of a Mobile WiMAX cell based on OFDMA access technique. We compare two strategies: the MAX SNR which is the most used channel assignment technique and our proposal which is based on channel condition learning. As we distinguish four IEEE 802.16e-2005 service classes: UGS, rtPS, nrtPS and ErtPS, we combine these strategies with a scheduler block in order to satisfy the QoS constraints of these classes. In addition, we take into consideration the Adaptive Modulation and Coding (AMC) scheme, through the selection of the slot which is based on the modulation scheme. The main concern here is to propose a simple method to assign WiMAX physical resources called slot to satisfy QoS constraints of a user's scheduled MPDUs, which can be easily and efficiently used in a realistic Mobile WiMAX network.

With the recent evolution of network-based multiplayer games and the increasing popularity of online games demanding strict real-time interaction among players - like First Person Shooter (FPS) -, game providers face the problem to correlate network conditions with quality of gaming experience. This paper addresses the problem of the estimation gameplay quality during real-time games; in particular, we focus on FPS ones. Current literature usually considers end-to-end delay as the only important parameter and deducts system performance indexes from graphical ones. Player satisfaction, on the other hand, is usually evaluated in a subjective way: asking the player, or measuring how long he/she stays connected. In this paper we use a testbed with synthetic players (bots) to directly correlate network end-to-end delay and jitter with expected players' satisfaction. Running extensive experiments we argue about effective in-game performances degradation of penalized players. Performances are measured in terms of score and number of actions kills, actually - performed per minute.

This paper addresses the issue of wireless sensor network (WSN) deployment. We investigate this problem in the case where the monitored area is characterized by a geographical irregularity of the sensed events. Precisely, we consider that each point of the deployment area requires a minimum threshold guarantee on the event detection probability. Our proposed scalable deployment method, named potential field-based deployment algorithm (PFDA), is based on the potential field and the virtual force approaches. Our proposal is able to (1) satisfy the required event detection probability threshold for each point, in a large-scale area, while minimizing the number of deployed sensors and (2) to ensure the network connectivity. The results and evaluation analysis show that PFDA outperforms the other strategies proposed in literature.

In this paper, we address the wireless sensor network (WSN) deployment issue. Compared to similar works, we relax some assumptions that were generally considered in the literature. Precisely, instead of the classical binary detection model, we consider a distance-related probabilistic one. Moreover, we assume that the observed area is characterized by the geographical irregularity of the sensed events. Our resulting differentiated WSN deployment problem is formulated as a multi-objectives optimization one. To overcome the computational complexity of an exact resolution, we propose an original pseudo-random approach based on the tabu search heuristic. Our proposal is able to take into consideration the required detection probability threshold of each point in the monitored area while minimizing the number of deployed sensors. Performances evaluations show that our proposal achieves a much better satisfaction rate than several other approaches proposed in the literature.

In this paper, we extend the differentiated deployment method which we have proposed in our previous paper by adding the connectivity constraint. We assume a fixed communication ray and a probabilistic detection model. We also consider that to each point in the deployment area is associated a detection probability threshold, which must be satisfied by our deployment method. Finally, we suppose that the detection probabilities thresholds of the area are geographically nonuniformly distributed. Our differentiated deployment problem is modeled as multi-objectives optimization problem, which we resolve using our proposed Tabu Search-based algorithm. A comparison is made with the methods found in the literature. The performances obtained by our method are much better in term of required number of sensors, generated detection probabilities, while ensuring the network connectivity.

In this paper we propose a new content-aware ARQ scheme for H.264 video streaming over UMTS wireless links. We propose a new scheme which assesses the importance of each packet using the FMO (Flexible Macro-block Ordering) tool, a novel error robustness tool of H.264 video encoding standard. Temporal values are then given to video packets in respect to their importance. Carried out simulations show good improvement in video quality of the proposed mechanism.