Summary Social question and answer (Q&A) communities or websites, such as Quora, StackOverflow, become popular from the last decade. They provide collected answers in a crowdsourcing way by encouraging the interactive between users. The ranking of answers in such Q&A websites depends on many factors, such as received upvotes, downvotes, publishing time, and so on. When there exist malicious users, they can manipulate the ranking of answers by launching voting spamming attacks. In this article, we propose a metric, diversity of visibility, to characterize the balance between rankings of answers with conflicting opinions and propose a time‐diversitybased voting scheme to reach a high diversity of visibility, so as to minimize the impact of voting spamming attacks. We evaluate the proposed metric and voting scheme in the case study of a Chinese Q&A website, Zhihu, and show that the proposed voting scheme consistently shows a good balance of answers with conflicting opinions in both actual scenarios and synthetic scenarios.

In vehicular networks, the benefits of jointly-optimizing data prefetching and caching with broadcast transmission scheduling and rate adaptation strategies at road-side units (RSUs) have already been demonstrated in the literature. Nevertheless, the effectiveness of the solution depends greatly on the accurate knowledge of vehicular trajectories. In practice, as specified in the standards, this is at odds with the important issue of privacy. One of the main contributions of this work is to address this issue by providing a scheme that jointly optimizes the throughput of broadcast-transmission scheduling from RSUs to vehicles, and the privacy of vehicles’ locations, by enabling them to disseminate obfuscated locations to the server from time to time. We formulate this problem as a reinforcement learning (RL) problem, where the vehicles learn when to report obfuscated locations, in order to maximize a utility that encompasses both the network capacity (related to vehicles’ throughput) and the level of privacy achieved. The proposed scheme is shown to consistently outperform alternative randomized schemes considered in past work, and in particular it proves to be robust against prediction errors of the future locations of the vehicles.

There is an increasing need for charging station recommendation to minimize the overall charging time for electric vehicles and balance load for the charging stations. To grant this need, we model the recommendation problem as a Markov Decision Process (MDP) problem. However, the traditional MDP model has the issue of ‘curse of dimensionality’. To address this issue, we propose an extension of MDP: multiple-phase MDP, in which the state transition of MDP is decomposing into several phases, so as to reduce the state space and state transition complexities. This is done by introducing two states other than the normal state defined in MDP: post decision state and intermediate decision state. Then, we propose an online learning based algorithm to solve the formulated multiple-phase MDP model. Thanks to the reduced complexities of the state space and state transition, the proposed online algorithm can converge fast. By comparing to other recommendation mechanisms, such as game theory based recommendation and Q-learning based recommendation, our simulation evaluation demonstrates that our proposition can bring good performance.

The technological advances in the transportation and automotive industry led to the use of new types of sensing systems more cost-effective and adapted to large-scale dense deployment. Those sensing techniques allow continuously gathering traffic measurements times series in different geospatial locations. The accuracy of the obtained raw measurements is often hindered by different factors related to the sensing environment and the sensing process itself and thus fail to capture the short-term traffic variations crucial for real-time traffic monitoring. In this paper, we propose the DGC-LSTM model for area-wide traffic estimation from error-prone measurements time series. The backbone of the DGC-LSTM model is a graph convolutional Long Short Term Memory model with a dynamic adjacency matrix. The adjacency matrix is learned and optimized during the model training. The adjacency matrix values are estimated from the set of contextual features that impact the dynamicity of the dependencies in both the spatial and temporal dimensions. Experiments on a realistic synthetic labelled Bluetooth counts dataset is used for model evaluation. Lastly, we highlight the importance of transfer learning methods to improve the model applicability by ensuring model adaptation to the new deployment site while avoiding the extensive data-labelling effort.

The technological advances in the transportation and automotive industry led to the use of new types of sensing systems more cost-effective and adapted to large-scale dense deployment. Those sensing techniques allow continuously gathering traffic measurements times series in different geospatial locations. The accuracy of the obtained raw measurements is often hindered by different factors related to the sensing environment and the sensing process itself and thus fail to capture the short-term traffic variations crucial for real-time traffic monitoring. In this paper, we propose the DGC-LSTM model for area-wide traffic estimation from error-prone measurements time series. The backbone of the DGC-LSTM model is a graph convolutional Long Short Term Memory model with a dynamic adjacency matrix. The adjacency matrix is learned and optimized during the model training. The adjacency matrix values are estimated from the set of contextual features that impact the dynamicity of the dependencies in both the spatial and temporal dimensions. Experiments on a realistic synthetic labelled Bluetooth counts dataset is used for model evaluation. Lastly, we highlight the importance of transfer learning methods to improve the model applicability by ensuring model adaptation to the new deployment site while avoiding the extensive data-labelling effort.

This paper presents an analytical model for the average Packet Error Rate (PER) of Bluetooth in Basic Rate (BR) mode. The effects of the physical layer and the MAC layer are taken into account. A Nakagami-m block fading is considered to take into account the effects of wireless channel fading. A Gaussian Frequency Shift Keying (GFSK) modulation scheme is used. Also, Forward Error Control (FEC) coding effects are taken into account. The interference between the different piconets is captured by a simple Medium Access Control (MAC) layer collision model. An approximation of instantaneous PER is obtained as a first result. Then, a closed-form expression is derived for the PER at the physical layer of Bluetooth. Finally, the overall average PER at the physical and MAC layer of Bluetooth is computed. Extensive simulations are performed to show the accuracy of the obtained results.

Vehicular edge computing (VEC) provides an environment for offloading tasks from vehicles. Indeed, the advantage through VEC is to push power computational and storage capacities at the edge nodes near the vehicles to handle the enormous resources required by some applications. On the other hand, in order to manage efficiently these resources, it would be necessary to partition them into several parts, each dedicated to a specific service. Thus, integrating network slicing in VEC appears to be relevant. Therefore, in this paper we study the task offloading problem from vehicles to wireless 5G new generation nodes (gNBs) and road side units (RSUs) hosting sliced edge computing servers. We formulate the problem as an integer linear programming problem and propose a new algorithm, which follows a centralized control strategy to holistically view and manage the whole network, and the sliced edge nodes. In addition, it follows network function virtualization framework to separate the logical network from the physical resources. The simulation results show that, in terms of acceptance ratio, the proposed algorithm provides very close results to the optimal solution, and when compared to state-of-art algorithm, integrating slicing is better when there is enough resources on the hosting nodes, but it still guarantees the differentiation among services.

International audience

Prefetching and caching content at road-side units (RSUs) and broadcast-transmission scheduling can be shown to improve the throughput in vehicular networks considerably, provided vehicular trajectories prediction are accurate enough. As such many studies assume implicitly that such trajectories are accurately available via GPS or location tracking. In practice, as discussed in the standards, this raises a big issue of privacy. In this paper, we focus on jointly optimizing the broadcastscheduling throughput from RSUs to vehicles, while preserving the privacy of vehicles by enabling them to disseminate obfuscated location information to the server. As it is difficult to predict the vehicular throughput according to its disseminated obfuscated locations, we propose to use the capacity based on reported information as an approximation. We formulate the problem as a reinforcement learning (RL) problem, where the decision variables concern the action to obfuscate disseminated location according to current location and the last disseminated location, with the objective of maximizing a utility function that consists of a weighted sum of the capacity and the level of privacy. Simulation results show that, the proposed scheme consistently outperforms the randomized benchmark, and is insensitive to the prediction accuracy of vehicles' future locations.

We propose a simulation model of Bluetooth (BT) passive scanning for vehicular traffic monitoring. The model is designed to simulate packet detection in the physical and Medium Access Control (MAC) layers for fixed BT sensors. At the physical layer, we consider the radio propagation effects of path loss, small- and large-scale fading. At the MAC layer, we model channel hopping and packet collisions. Associated to a vehicular traffic simulator, our proposal provides a cost-efficient way to generate large-scale BT traffic measurement datasets including both sensor and ground-truth data, avoiding deployment costs. We validate the model by comparing the simulation output to experimental data and we show a simulation use-case of vehicular traffic monitoring in a complex urban context.

Prefetching data at the road side units (RSUs) and transmitting them to interested vehicles can help reduce the traffic load in vehicular networks, and improve the data retrieval time. Most prior work in this area opted for supporting data delivery by using a single data rate, in stark contrast to common networking knowledge that good tradeoffs can be achieved in terms of performance by adopting different, more appropriate data rates for different users. In particular, in general wireless networks it is well known that when the data rate is small, the transmission is robust but takes too long; while when the data rate is high, the data delivery time is smaller but the coverage area of the RSU becomes smaller and so does the robustness. In this paper, we study the joint problem of caching and scheduling to decide, what data to prefetch, when to deliver it, and which data rate to use, with the objective of maximizing the volume of data delivered to the mobile vehicles in the network. This problem is subject to many constraints imposed by the real system limits, such as the mobility pattern of the vehicles, the distribution of data requests by the vehicles, and the limited buffer space available at the RSUs. We formulate the problem as an integer linear programming problem, and propose a heuristic caching algorithm, and a heuristic scheduling algorithm to find an approximate solution that is shown via simulation to improve the throughput, compared to standard alternative approaches.

During the last decade, Bluetooth has become a widespread feature in the automobile industry, meaning that its signal activity can be correlated with road traffic. Using this technology as a means for assessing traffic is very cost-effective and has a low impact on the infrastructure. Nevertheless, unlike other techniques, it does not provide direct sensing of vehicles only. Statistical analysis methods need to be applied to the collected data. In this paper, we first propose a machine learning method for traffic flow estimation using Bluetooth sensors. We also propose a method for estimating the mean travel speed between two sensors. The performance of the proposed methods is evaluated through eight weeks of experimentation. Finally, we envision a potential solution for building real-time Origin- Destination matrices.

Electrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction.

Due to the highly dynamic network topology of Vehicular Ad-hoc Networks (VANETs), an effective clustering algorithm is always required to both solve the scalability problem and enhance vehicles' connections. In this paper, a discrete- time finite-state Markov chain model is proposed based on our previous clustering framework, in order to provide a comprehensive analysis of cluster stability, including cluster's lifetime and cluster member's lifetime. Moreover, the future clustering performance can be predicted through the proposed model. Numerical results are presented to evaluate the model, which show high consistency between analytical and simulation results.

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

In this paper, we present a link duration prediction method in VANETs. It utilizes AdaBoost algorithm to combine several link metrics, such as distance, difference in velocities, link lifetime, and etc., to form a predictor with a higher accuracy. The proposed method is applicable to different traffic scenarios and does not rely on any assumption of vehicles' velocity distribution. We evaluate the performance of this Adaboost-based link duration prediction algorithm by various traffic traces generated by SUMO that represents different typical scenarios. The evaluation result shows that, the proposed method effectively improve link prediction accuracy. Compared with other machine learning based solutions such as linear regression and support vector regression, the proposed method also shows less prediction error consistently.

Cooperative Intelligent Transportation Systems are rapidly gaining momentum in the scenario of modern wireless communications. Within these environments, messages are exchanged continuously. The latter should be secure and ensure users' privacy. Public Key Infrastructures (PKIs) represent the major solution to meet security needs. On the other hand, communications between the PKI and the vehicle stations or the Road Side Unit stations should also be secure. Main current security standards do not address and define a complete detailed secure end-to-end mechanism to send requests to the PKI and receive the associated responses. In this paper, we propose a detailed security protocol, based on ETSI security standards, that ensures confidentiality, integrity and authentication. The evaluation of the latter shows its ability in meeting security needs.

In the literature, there are many clustering algorithms proposed for the vehicle ad hoc networks (VANETs) to improve network stability and scalability. However, there is a lack of comprehensive comparison among them. In this paper, we show that there exists unfair comparison of clustering algorithms, in the aspect of simulators, performance metrics, simulation scenarios, and configuration of algorithms. To start the first step to tackle this problem, we propose a general framework to Fairly Compare Clustering algorithms in VANETs (FCC). Under this framework, we show that, i) misconfiguration of clustering algorithms can lead to significant performance degradation, ii) there is no winning clustering algorithms in all scenarios, iii) the design of clustering algorithms should be scenario-dependent.

In this paper, we present a data-driven solution to address the energy efficient routing problem in a multi-layer femtocell network. The basic idea is to divide problems into categories by the clustering of traffic demand vectors, and solve a problem by a most suitable algorithm, in terms of throughput, energy consumption and computation time, corresponding to its category. The simulation results show that, our proposed approach can achieve a similar performance in throughput and energy saving as the previous JRN algorithm, by reducing 80% computation time.

Cooperative Intelligent Transportation Systems (C-ITS) are gaining ground and are almost part of our everyday life. Within these environments, huge amounts of messages are exchanged. Besides, these messages should be secure in order to ensure users' privacy. Public Key Infrastructures (PKI) represent the most common security solution. Due to the vehicles speed, the communication with the PKI should be fully optimized. The European Telecommunications Standards Institute (ETSI) proposes a PKI architecture for C-ITS environments. However, unlike most of security standards as IEEE 1609.2, there is no Abstract Syntax Notation One (ASN.1) specification for the used certificates. For this reason, in this paper, we propose an ASN.1 definition for the ETSI certificate to help developers in its implementation. In addition, we provide an extensive comparative study of the different encoding schemes, applied to this proposal.

Effective clustering algorithms are indispensable in order to solve the scalability problem in Vehicular Ad-hoc Networks (VANETs). Due to the highly dynamic network topology, an effective cluster merging scheme is always required in clustering algorithms, aiming to prevent the collapse of clusters. In the literature, there is a lack of comparison of cluster merging schemes, which makes it hard to analyze the impact of this component on clustering performance. In this paper, we analyze the existing cluster merging schemes and propose a Leadership-based Cluster Merging (LCM) scheme. Then, a comprehensive comparison of different cluster merging schemes under various traffic scenarios is presented, and our scheme is shown to achieve better performance on cluster stability.

Clustering in vehicular ad hoc networks (VANETs) is a challenging issue due to the highly dynamic vehicle mobility and frequent communication disconnections problems. Recent years' research have proven that mobility-based clustering mechanisms considering speed, moving direction, position, destination and density, were more effective in improving cluster stability. In this paper, we propose a new mobility-based and stability-based clustering algorithm (MSCA) for urban city scenario, which makes use of vehicle's moving direction, relative position and link lifetime estimation. We evaluate the performance of our proposed algorithm in terms of changing maximum lane speed and traffic flow rate. Our proposed algorithm performs well in terms of average cluster head lifetime and average number of clusters.

Cellular networks are facing serious traffic overload problems due to the high users traffic demands, emphasized by the important penetration of smart phones and their greedy applications. The originality of this paper is to investigate upon the use of Intelligent Transportation Systems (ITS) for offloading the cellular infrastructure. We provide an analytical study based on an optimization problem formulation where the aim is to select a maximum target set of flows to route through the Vehicular Ad hoc Network (VANET). Offloading decision considers the VANET link availability, channel load (using contention), vehicle to vehicle link quality and the maximum volume that could be offloaded from the road side unit to the vehicular nodes. Simulation results show that the data offloading fraction is closely tied to the data volume of the flows, to the channel load and to the path quality. Results show also that 100% of best effort data traffics could be offloaded in some specific scenarios.

We study the problem of information dissemination in vehicular networks in this paper. Crucial information, such as traffic jamming status, is usually broadcasted inside the network. Each vehicle in the network has its own choice to either forward or drop packets for others. Although the energy consumption in general is not an issue in vehicular networks, forwarding every received packets is still not preferred as it may result in congestion in transmission. We model this problem via evolutionary game theory to investigate the cooperative behavior among vehicles. Simulation results show that, the cooperation ratio in the network is proportional to the setting of synergy factor for the evolutionary game. When nodes change their strategies according to the evolutionary game, information can be disseminated as fast as flooding scheme, while more bandwidth can be reserved.

Automotive industry is interested to ITS as it provides attractive services for connected vehicles aiming at creating a new and very promising business market. However, the security remains a major challenge for the ITS deployment as several critical threats has been identified. In this paper, we present our Cooperative ITS Vehicle Application Oriented Security Framework CIVAS for vehicle ITS stations. Our focus is given first to ITS vehicle security requirements and constraints. Then, we provide an overview of the “Defense-in-Depth” model that we adapted for securing ITS vehicle. The proposed security framework follows a modular design and is application oriented. To illustrate the interaction between designed CIVAS layers, we consider a vehicle stationary as example of a road safety use case.

Offloading a part of cellular traffic through other kinds of networks, such as Wi-Fi hotspots and femtoCells represents an interesting solution for operators to cope with the high user traffic demand increase. In this paper, we propose to use the Vehicular ad hoc Networks (VANETs) for the same purpose. We present an analytical study based on an optimization problem formulation to evaluate the potential of VANET to vehicle part of the cellular traffic. The offloading decision considers several constraints related to vehicle-to-infrastructure link availability, channel and medium contention, vehicle-to-vehicle link capacity and quality, data flows volume and the link connectivity duration between the vehicle and the road side unit. Moreover, the originality of this work is the consideration of flow's service class in the offloading decision. Numerical results show that the data offloading fraction is closely affected by the data volume of the flows and by vehicular link quality. Results show also that best effort and background traffics have more priority to be offloaded than the video streaming traffics.

In this work, we investigate upon the potential of considering the Intelligent Transportation Systems (ITS) for cellular traffic offloading as an alternative to Wi-Fi and femtoCells approaches. We propose an analytical study based on an optimization problem formulation called VOPP where the objective is to elect the maximum number of data flows that could be routed to the downloaders through the Vehicular Network (VANET). VOPP considers the link availability between the infrastructure and the downloader, the medium contention, interferences, data volume, gateways capacity and the channel aggregation to evaluate the VANET capacity to support additional traffics. Numerical results of our analytical approach show that the data offloading fraction is closely tied to the data volume of the flows and to the vehicles positions and VANET topology.

This paper addresses the appearance of congestion in Wireless Sensor Networks (WSNs) using mobile Mini-Sinks (MSs). Several mobile MSs are moving according to an arbitrary mobility model inside the sensor field for collecting data within their coverage areas based on the controlled Energy Conserving Routing Protocol (ECRP) and forwarding it towards the single main sink. ECRP, based on route diversity, is implemented in MSs and sensors. Thus, a set of multiple paths between MSs and sensors is generated to distribute the global traffic, so as to partially reduce the appearance of congestion over the entire network. Simulations were performed in order to validate the performance of our model. We compare the results obtained with those for a single static sink and mobile sink, and show that our approach achieves better network performances in terms of packet delivery ratio, throughput, end-to-end delay, network lifetime, residual energy, energy and multiple paths overhead.

Data transmission in Wireless Sensor Networks (WSNs) is physically constrained by interference, throughput, latency and many other physical phenomena. We propose a distributed hybrid algorithm to perform the selection of communication channels in such a way that overall throughput, latency, energy and overhead are improved. A tree structure is built out from the sink, in order to elect sensors with the highest degree of connectivity as parents, and sensors with the lowest degree of connectivity as leaves. A set of parents and leaves are assigned to a single fixed channel. Specific sensors, called mediators, are assigned to several orthogonal channels. So, they can switch dynamically to the static channels of parents for collecting the data. This allows the data to be efficiently propagated in parallel on multiple channels from parent to mediator to parent towards the sink. We compare the results obtained with those for single and multiple channels by taking sink throughput, broadcast latency, energy consumption and routing overhead as performance criteria. We show that our method provides better results.

In this paper, we address the gateway selection issue in a hybrid system composed of a VANET network and a 4G LTE Advanced infrastructure. The VANET network is structured via a clustering scheme. Our algorithm is based on a multi-criteria and QoS related attributes approach used to make a decision of the appropriate gateway that might be used to connect a source vehicle to the LTE Advanced infrastructure. Considered criteria for making the decision include load, Received Signal Strength and Link Connectivity Duration of the cluster head and other candidate gateways. Simulation results demonstrate that our algorithm makes efficient decisions for electing the appropriate gateway with the best VANET and infrastructure features.

We propose a new approach based on the use of many data collectors, which we designate Mini-Sinks (MSs), instead of a single sink in order to improve Wireless Sensor Network (WSN) performances. One or more MSs are mobile and move according to an arbitrary mobility model inside the sensor field in order to maintain a fully-connected network topology, collecting data based on the controlled Energy Conserving Routing Protocol (ECRP) within their coverage areas and forwarding it towards the single main sink. ECRP, based on route diversity, is implemented in MSs and sensors in order to optimize the transmission cost of the forwarding scheme. A set of multiple paths between MSs and sensors is generated to distribute the global traffic over the entire network. Simulations were performed in order to validate the performance of our new approach. We compare the results obtained with those for a single static sink and mobile sink, and show that our approach can achieve better results in terms of packet delivery ratio, throughput, end-to-end delay and multiple paths overhead.

In this paper, we propose a cooperative traffic transmission algorithm in a joint cellular and mobile ad hoc hybrid network architecture. The mobile ad hoc network is based on IEEE 802.11p standard. The focus of this paper is on defining an algorithm aiming to select the appropriate gateway for a given source vehicle under the scope of V2I communications. In this work, we consider the LTE Advanced infrastructure. We propose a multi-criteria and attribute scheme used to make a decision over the appropriate gateway to connect a source vehicle to the LTE Advanced infrastructure. It is a QoS-balancing gateway selection algorithm where the decision over the gateway depends on the traffic to be transmitted to the infrastructure. Other criteria are also considered such as loads, Received Signal Strengths (RSS) and Link Connectivity Durations of the cluster head and other gateway candidates. Simulation results demonstrate that our algorithm makes efficient decisions for electing the appropriate gateway that fits best the cluster and infrastructure features. An improvement of the RSS and the Link Connectivity Durations, while comparing to the commonly used gateway selection scheme, is noted.

Efficiency applications in VANETs are focused on increasing the productivity of the road resources by managing the traffic flow and monitoring the road conditions. The performance of most such applications is dependent on an effective density estimation of the vehicles in the surroundings. Of the various methods, clustering demonstrates to be an effective concept to implement this. However due to high mobility a stable cluster, within a vehicular framework, is difficult to implement. In this work, we propose a new clusterhead election policy for direction based clustering algorithm C-DRIVE. This policy facilitates to attain better stability and thus accurate density estimation within the clusters. Simulation results show that the C-DRIVE is rendered stability through new clusterhead election policy by electing fewer clusterheads in the network. This supports for a better accuracy in density estimation with fewer overheads.

Efficiency applications in VANETs are focused on increasing the productivity of the road resources by managing the traffic flow and monitoring the road conditions. The performance of most such applications is dependent on an effective density estimation of the vehicles in the surroundings. Of the various methods, clustering demonstrates to be an effective concept to implement this. However due to high mobility a stable cluster, within a vehicular framework, is difficult to implement. In this work, we propose a direction based clustering algorithm with a clusterhead switching mechanism. This mechanism is aimed to overcome the influence of overtaking within the clusters. The proposed algorithm facilitates to attain better stability and thus accurate density estimation within the clusters. Simulation results show that the proposed clustering algorithm is rendered stability through switching mechanism and hence provides a better accuracy in density estimation.

In this paper, we propose enhanced Simple Forwarding over Trajectory (eSIFT), a novel opportunistic trajectory-based routing protocol for Vehicular Ad hoc Networks (VANETs), based on Simple Forwarding over Trajectory (SIFT). eSIFT is designed to operate in highly dynamic vehicular networks for data dissemination. eSIFT presents advantages such as no discovery step and low overhead. We also present performance evaluation results of eSIFT, in comparison to SIFT. Simulations show that eSIFT improves SIFT, in particular in term of delay, and slightly in term of hop count and packet delivery ratio.

In this paper, we propose Well-Connected Dominating Set Channel Assignment (WCDS-CA), a distributed hybrid algorithm that uses a well-connected dominating set to perform a selection of communication channels in wireless sensor networks in such a way that the number of channels used and interference among sensor nodes remains minimal. Since the network topology allows each sensor node to be equipped with several radio interfaces, a set of parents and leaves are statically assigned to a single fixed channel. The routers are assigned to several orthogonal channels using the corresponding radio interfaces in such a way that they can switch dynamically to the static channels of parent nodes in order to minimize network interference. The tree based on Breadth-First Search is built out from the sink, with the result that the shortest between each sensor and the sink is established. This allows the data to be efficiently propagated from parent to router to parent towards the sink in order to reduce the number of individual transmissions. We compare the results obtained with those for a single channel by taking the common links used by each node on each channel and packets transmitted as performance criteria, and show that our proposal gives better results in dense wireless sensor networks.

In urban scenarios traffic signal controls are the main mechanism to control vehicular flow at the intersections. However, traditional systems fail to adjust the timing pattern in accordance to the time variability. An alternative to such systems is to develop dynamic systems which will alter the timing patterns according to the traffic demand. In this paper, an adaptive traffic signal control system based on car-to-car communication is designed and developed. This system reduces the waiting time of the vehicles at the intersection along with the reduction in queue length. It's also verified that the proposed solution is collision free at the intersections. The proposed system is compared with a classical pre-timed system. The simulations also show that the data convergence time and the communication delay between vehicle and traffic signal do not compromise the efficiency of the system.

The past few years have seen an increasing interest in the development of vehicular ad hoc networks resulting in many routing protocols proposals. Scalability issues in such networks are attracting increasing attention these days. To improve data delivery performance in large scale networks, we propose SIFT, a trajectory based routing scheme that requires sparse infrastructure and rely on low quality information. It merely uses the trajectory and the location of the last node that forwarded the packet to forward a data packet from a source to a destination. SIFT is evaluated against DREAM, a well-known routing protocol from the literature through realistic simulations using Omnet++. Simulation results demonstrate that under dense deployment, using a realistic mobility model scenario, the proposed scheme performs better in terms of delivery ratio, end-to-end delay and route length.

Quality of service (QoS) is a key problem in wireless environments where bandwidth is scarce and channel conditions are time varying and sometimes implies highly packet losses. IEEE 802.11b/g/a wireless LAN (WLAN) are the most widely used WLAN standards today, and the IEEE 802.11e QoS enhancement standard exists and introduces QoS support for multimedia applications. This paper presents a class based admission control algorithm for 802.11e based wireless local area networks. The strengths of our admission control is dynamicity and flexibility of the algorithm, which adapts to the situation of the BSS, like global load, number of best effort AC, and position of QSTA by report of QAP, thing that have never been taken together, but each solutions have used a point of sight separately. Thus it achieves higher throughput than other admission control for 802.11 e. The idea which consists of changing parameters [AIFSN, CWmin and CWmax] of best effort flows, for decreasing collisions, is used, so we think that it is an efficient way to protect QoS flows from best effort flows, and to allow reducing then number of collisions. So we increase AIFSN [best effort flow], CWmin and CWmax only at 70% of load of network, to prevent starvations of best effort flows, and so we increase rate utilization of channel. We also use the current rate transmission of QSTAs, according to their positions, instead of the minimum rate transmission used by standard 802.11e, for calculate the load of network and derived the TXOPi necessary for all the stations, with i=1 to number of active stations. The 802.11e standard starves the low priority traffic in case of high load, and leads to higher collision rates, and did not make a good estimate of weight of queues, so there is an unbalance enters the flows with high priorities. A discussion is presented in detail using simulation-based evaluations, with an aim of comparing results of our admission control algorithm, with the 802.11e standard and the FHCF algorithm. Results reveal an improvement of the network load and a decrease of the number of collisions.

This paper compares different alternative aggregation techniques for Wireless Sensor Networks Optimization based on tree construction. The main idea is to efficiently elect parent nodes responsible of data aggregation according to their connectivity degree. Thus, the quantity of data transmitted on the network remains minimal during the transfer from all sensors towards the sink. Simulations have shown that our new approach provides a better performance than existing techniques such as BFS, DFS and flooding, especially in terms of quantity of data packets transmitted from all sensor towards the sink.

User mobility management is one of the important components of mobile multimedia systems. In an IP-based mobility case, a mobile node should be able to seamlessly obtain reserved resources after handover to a new access router. This is essential for both service continuity and quality of service assurance. In this paper, we introduce a hybrid architecture which allows a mobile node to seamlessly obtain reserved resources from its new location (seamless reservation), as well as to quickly release obsolete resources along the old path. In this architecture, reservation on access networks is performed by using path-decoupled approach, while path-coupled approach is used on backbone networks. Moreover, instead of using sender-initiated or receiver-initiated approach, mobile-node-initiated approach is used in this architecture to further improve reservation performance. In our performance analysis, we analyze advance reservation delay and release delay, as well as reservation underutilization.

In this paper, we propose PowerDHT, a novel scheme to extend the classic DHT-based overlay to a network with scale free-like properties. PowerDHT has a distributed rewiring method to improve the structure of the overlay network to a power-law-like graph. Our scheme is characterized through minimal, typically local-only, changes. Through simulations, we show that our proposal constructs an overlay network with an extended peer's neighborhood knowledge and a reduced network diameter at no additional cost and that it supports a more effective flooding e.g. for generic search.

Peer-to-Peer overlays have become a popular paradigm for building distributed systems, aiming to provide resource localization and sharing in large-scale networks. To design an overlay network, the first step is to choose an overlay topology connecting all the overlay nodes. Many proposed distributed hash table schemes for peer-to-peer network are based on some traditional calculated interconnection topologies. In this paper, we propose to extend the DHT topology to a bidirectional graph to provide a super-peer based lookup algorithm and a scalable resource localization support. We consider bidirectional routing algorithms and show how the resulting unfair routing structure extends the search region and provides an efficient resource lookup service at a very little additional cost.

Sensor network which operates on battery are used to gather data in a variety of environments. The data collected by each node is communicated through the network to the sink, which uses all reported data to determine characteristics of the environment or detect an event. Prolonging sensor's operable lifetime is a main design challenge of these networks. A good energy saving technique in this direction is to schedule nodes sleep interval with the communication radio turned off. In this paper, we propose a distributed topology control algorithm, termed ECTC, which uses a clustering approach. It is built on the notion that when a region of a shared channel wireless sensor network has a sufficient density of nodes, significant energy saving is obtained by allowing redundant nodes to sleep. Using the two-hop neighborhood information, certain nodes sequentially select a subset of nodes to be active among all nodes in the neighborhood, to ensure connectivity. Moreover, to ensure fairness, the role of active nodes is rotated periodically to ensure energy-balanced operations. Results from stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensor nodes report data through the cluster heads to the sink.

In this paper, we propose to extend the topology of Chord to a bidirectional graph to provide a super-peer based lookup algorithm and an efficient resource localization service at a very little additional cost.

Reservation aggregation provides scalability to the IETF integrated services (IntServ) by reducing the high number of states stored at internal routers and the number of signalling messages processed at these routers. However, the latter benefit will be lost if the bandwidth of aggregate reservation changes frequently. Hence, previous works either hold resource requests during a waiting period before sending a single aggregate reservation for all received requests, or reserve maximum resources which will be requested in the following period. However these works involve an assumption of arrival distribution of resource requests. In this paper, we design a policy for enhancing the reservation aggregation performance, which is independent of arrival distribution. From the results of simulation, we observe that this policy outperforms other policies.

In this paper we introduce a novel security architecture and security mechanisms for vehicular networks environments. Our approach provides a layer-2 multi-hop authentication and credential delivery scheme between vehicles and the network operator authentication server. We analyze the performance of the proposed solution through extensive simulations, showing the success of our scheme (compared to the legacy single-hop scheme) in providing a robust and efficient authentication and credential delivery to vehicles. Based on the obtained performance analysis, we derive some guidelines aiming to optimize our solution under various vehicles densities.

Resource reservation in mobile environments is an important task in the future when the user’s point of attachment to the network changes frequently due to mobility. One crucial issue of this task is to release unnecessary reservation along old path after departure of mobile node(s). Although this can be done automatically by a soft state mechanism, default soft state’s lifetime which is conceived for wired network case is not suitable to mobility. In this paper, we introduce an additional value to soft state’s lifetime to optimize resource release along old path: besides the default value which is used when no handover occurs, the additional value is assigned when handover takes place. Through the comparison with three other mechanisms, we observe that our proposed mechanism outperforms the others in term of average cost and blocking probability.

In mobile ad hoc networks (MANETs), multihop transmissions rely on nodes collaboration to correctly route and forward packets to their final destination. However, collaboration of intermediate nodes is not guaranteed and non-collaborative nodes may drop packets they are asked to forward. Such behaviour, known as packet dropping, results of nodespsila selfishness or maliciousness and has dramatic effects on networks performances. Most of proposed solutions to counter packet dropping are either based on trust management or multipath routing, but no solution combines both trust management and multipath routing. Adaptive Secured Multipath for Ad hoc networks (ASMA) is a security framework which adapts security to the application requirements; evaluates trustworthy relationship based on localized trust model and efficiently combines multipath routing into trust management. In this paper we study ASMA performance against the most classical and simple packet dropping attack: Black Hole attack. Aiming to evaluate ASMA in dense networks, we use a realistic scenario representing a subway environment which brings interesting features and may be a prolific context for MANET application development. Through simulations we compare the performances of DSR and ASMA associated to DSR in attacked pure MANETs. Simulation results illustrate that ASMA-DSR outperforms DSR under different attack configurations and is totally adapted for dense and large networks.

To support complex queries and broadcast in DHTs, we propose an algorithm for an effective flooding that we implement and compare in a Chord DHT. We use flooding and replication that exploit structural constraints to achieve lower overhead and higher reliability.

Since MANETs are mainly composed of lightweight devices with limited capabilities, efficiently manage security is crucial to reduce the performance degradation and resources consumption. Actually, managing MANETspsila security is a hard challenge due to the inherent complexity and the tremendous number of parameters which should be considered. To provide efficient security management, we focus on three main parameters: the secured device capabilities, the associated network security and the secured applications. The solution we propose, named Adaptive Secured Multipath for Ad hoc networks (ASMA) is a scalable, flexible and application-oriented framework able to manage security depending on the application requirements and the network security conditions. ASMA is based on a structure called macrograph combining both dynamic trust management and multipath routing. The macrograph structure is capable to estimate transmission security in order to assure that communications are established only when they match applications security requirements. ASMA flexibility offers compliance with most on-demand routing protocols and security tools. In this paper we present simulation results for ASMA associated with AOMDV (a multipath declination of AODV) routing protocol and compare results with AOMDV. We show that ASMA-AOMDV outperforms AOMDV, dividing by three the packet loss rate in networks including 20% of malicious nodes, while causing only 3% of additional loss in safe networks.

The recent proliferation of wireless devices extends the scope of mobile ad hoc networks (MANETs) applications beyond the military domain to include civil and commercial application scenarios. Since MANETs are composed of mobile terminals with limited resources, guaranteeing their security remains an unsolved and motivating challenge. Due to advantageous features like infrastructureless and spontaneous deployment, MANETs offer an opportunity to set up temporary, dynamic and local networks for low cost. However, some of their characteristics like unreliability of wireless links, dynamic topology, and absence of underlying infrastructure raise serious problems which become critical when security is also considered. Multipath routing scheme enhances the robustness of routing protocols and offers a mean to mitigate networks mobility impact and node misbehaviors. In this paper we compare the performances of five multipath routing protocols: three node-disjoint multipath routing protocols and two routing protocols based on a Untrusted Node Disjoint (UND) path scheme. Comparisons between these protocols highlight the improvements raised by the UND scheme under different attack configurations.

In this paper we present a performance analysis of a layer-2 multi-hop authentication and credential delivery scheme for vehicular networks. Our scheme allows drivers secure communication and serves network operators business. The performance analysis shows that our scheme succeeds in providing a robust and efficient authentication and credential delivery to vehicles. We finally derive some guidelines allowing our scheme to adapt to different vehicles densities.

In this paper we introduce a prioritization scheme for enhancing the performance of the authentication process in vehicular networks using dedicated short range communications (DSRC). We consider the authentication as a service that should be required by each user in order to gain access to the network and to the offered services. Instead of using a unique channel to transmit the messages of the authentication service as in the case of the legacy DSRC model, our scheme extends this latter through exploiting the various DSRC channels based on a channel sensing approach that aims to prioritize the transmission of the authentication messages (with respect to the transmission of other DSRC services messages of the same priority as the authentication service). We implemented our proposed solution under the GrooveNet vehicular network simulator, where we analyze its performance impact on a previously proposed vehicular network authentication mechanism. Extensive simulations show the success of our proposed scheme in providing a more efficient and robust authentication in the presence of other services' traffics.

This paper presents a comparative analysis of replication algorithms for DHT (Distributed Hash Table) architectures. These algorithms are applicable to all existing structured peer-to-peer systems, and can be implemented on top of any DHT. The performance of these algorithms is examined using emulation through virtualization. Significant differences are identified in terms of delays, control overhead, success rate, and overlay route length.

In this work we present a performance comparison study between SIFT (Simple Forwarding over Trajectory), an innovative and scalable trajectory-based approach, and DREAM (Distance Routing Effect Algorithm for Mobility), a stable, largely tested position-based scheme. In the literature, the latest studies indicate that control overhead is the most important drawback that routing protocols must face. Classical ad hoc routing schemes do not perform well in MANETS (Mobile Ad hoc Networks) because they were not designed to handle efficiently mobility handicaps. Position-based forwarding techniques perform better in highly dynamic scenarios, reducing control overhead consequences, but there may be some highly dynamic scenarios where they do not perform efficiently. Trajectory-based forwarding protocols solve the overhead problem and, thus, they perform efficiently in MANETS. Our study demonstrates that SIFT performs better than DREAM concerning delivery ratio, control overhead and route length in terms of number of hops. Its main handicap could be, a priori, the delivery delay. However, even if delay may be high in SIFT, control overhead may also causes even higher delivery delay in DREAM.

With the advent and ubiquitous of wireless technology, a wide range of advanced services are expected to be supported including appealing services that currently exist in wired systems. Nevertheless, the resource constraints in wireless environment may render difficulty to realizing all the desirable services. Consequently, an infrastructure with high data rate is necessary to complement the resource constraints and to act as anchor points linking mobile nodes to other fixed networks as the Internet. Hybrid wireless networks have emerged as a promising solution, allowing mobile clients to achieve higher performance and service access in a seamless manner independent of their existence in wireless LAN (WLAN) communication range. In this paper we address the benefits of hybrid wireless networks, showing their possible applications and presenting a classification for their emerging architectures. Also, we identify the research challenge arising from the problem of applying the grid computing concept in such hybrid wireless environment, showing the expected benefits from the aggregated fixed-mobile capacity. Finally, we propose our vision for a potential architectural model, which is expected to provide useful services by the network operator or the service provider in such a hybrid environment

Mobile ad hoc networks (MANETs) emerge as one of the most attractive technologies in radio networks. They are complex networks that are difficult to analyze due to their unpredicted and random high rate topological changes. Bandwidth limitations in such networks allows multicast to be an important routing solution, which reduces the communication cost via providing efficient saving in bandwidth and network resources. In this paper, we study the random graph as a model of such complex networks and we derive an analytical model for our proposed source routing-based multicast protocol (SRMP). This model, named reactive random graph (RRG), is investigated from the random graph theory, exploiting the phase transition behavior from the percolation theory. Based on our RRG model, we analyze critical properties in SRMP. Our main goal is to study and balance the tradeoff between multicast group connectivity and the optimum use of resources through minimizing the communication graph size. Useful results are drawn out through our work, providing us with significant factors for enhancing the performance of SRMP

In this paper, we propose a generalization of the 802.1X architecture using an extensible authentication protocol (EAP) for more general signaling data transport purposes. We develop EAP/SIG, an effective and easy-to-implement generic signaling protocol for future wireless LANs. We discuss the advantages of this approach and show how it can be implemented.

The fundamental goal in future mobile multiservice networks is to provide an efficient mobile computing environment which enables a user with a portable computer equipped with multiple wireless access interfaces to move seamlessly between different providers' networks. In particular, if these networks are Wi-Fi WLANs (wireless fidelity wireless local area networks), security features should be defined precisely taking into account seamless roaming. The paper considers one of the most critical security issues in Wi-Fi networks -key management. We propose a centralized approach to deal with key management in the case of an interconnection of remoted Wi-Fi networks based on using a key management server. Security goals and system architecture guidelines are outlined. In respect to the IEEE 802.11i standard, the changing of keys requires only 3 messages and the roaming situation allows the establishment of a new secure 802.11 wireless channel by exchanging only 5 messages against at least 20 messages.

We focus on one critical issue in mobile ad hoc networks (MANETs) that is multicast routing. In fact, optimal routes, stable links, power conservation, loop freedom, and reduced channel overhead are the main features to be addressed in a more efficient multicast mechanism. MANETs are often analyzed through simulations and their performance results depend slightly on simulation network parameters including traffic generation models, radio transmission parameters and mobility models. In this paper we evaluate the performance of our proposed multicast routing protocol, named source routing-based multicast protocol (SRMP). SRMP constructs a mesh to connect group members, providing robustness against mobility. It also provides stable paths based on links availability according to future prediction of links state, and higher battery life paths tending to power conserving. We present a full performance evaluation and analysis for SRMP under realistic conditions including, realistic movements of mobile nodes in the form of different mobility models. We present our simulation results that illustrate the importance of choosing a mobility model in the simulation. Specifically, we illustrate the effect of choosing the mobility model on the performance results.

We analyze the implications of the integration of the current IP micromobility concepts with the 802.1X access control. We show that the independent user location tracking of IP micromobility and 802.1X provokes a significant additional handover delay and signaling load degrading the overall performance. To overcome these difficulties, we propose a system architecture which uses EAP as a general signaling protocol. We then discuss the new opportunities of our approach. Finally, we show that our system architecture can be applied in almost all access networks by exploiting the generality of the 802.1X approach.

An authentication model using secured smart cards implementing IP services is presented. In this model, some authentication functions usually found in the access network are moved inside the smart card. This innovative architecture simplifies current authentication schemes and helps to design new services.

In this paper, we propose a new on-demand QoS-aware routing protocol, named QoS-adaptive source routing protocol (QoS-ASR). QoS-ASR attempts to minimize routing overhead in order to meet multimedia QoS requirements applications. It is an adaptive soft-QoS protocol with aggregate flows that applies the source routing mechanism defined in the dynamic source routing (DSR) unicast protocol. Specially, our protocol is anchored on a new idea based on exploiting application-related constraints (transmission delay, available bandwidth, packet loss rate) as well as ad-hoc network features (battery life, link stability, node congestion state) in order to provide robustness against mobility and wireless channel fluctuations. The performance of the proposed scheme is evaluated via simulations and is compared to DSR.

Multicast routing in mobile ad hoc NETworks (MANETs) is a recent research topic. In this paper, we present a performance study of three multicast protocols: ODMRP, ADMR, and SRMP. Source routing-based multicast protocol (SRMP) is a new on-demand multicast routing protocol that applies a source routing mechanism and constructs a mesh to connect group members. The strength of SRMP lies on its nodes selection criteria during mesh construction. Instead of using the shortest path as most of the other protocols, SRMP provides paths in terms of connectivity strength, higher battery life, and links' availability. A performance comparison with ODMRP and ADMR shows that SRMP provides better route lifetime and battery lifetime.

Wireless channels are highly affected by unpredictable factors such as cochannel interference, adjacent channel interference, propagation path loss, shadowing and multipath fading. The unreliability of media degrades the transmission quality seriously. Forward error correction (FEC) schemes are frequently used in wireless environments to reduce the high bit error rate of the channel. In this paper, we propose an adaptive FEC scheme for multicast communication in wireless networks based on dynamic variation of coding parameters as a function of the channel bit error rate, desired QoS in terms of reliability, number of receivers and efficiency in terms of bandwidth use. Reed-Solomon erasure codes are used throughout this study because of their appropriate characteristics in terms of powerful coding and implementation simplicity. We make a numerical analysis of a set of Reed-Solomon erasure codes. The observations made throughout this numerical analysis are the basic principals of our adaptive FEC scheme. Numerical results show that our adaptive scheme provides the best trade-off between transmission overhead and guaranteed QoS.

This paper presents an alternative simple loop-free bandwidth-efficient distributed routing algorithm for mobile ad hoc networks, denoted as distributed dynamic routing (DDR). Although DDR benefits from classical concepts like zone and forest, unlike previous solutions it achieves several goals at the same time. Firstly, it provides different mechanisms to drastically reduce routing complexity and improve delay performance. Secondly, it is infrastructureless in a strong sense: it does not even require a physical location information. Finally, zone naming is performed dynamically and broadcasting is reduced noticeably.

Multi-agent systems technology can be useful for efficiently designing and maintaining secure networks. Indeed, networks evolve at a rapid pace in terms of the number and type of components and user access queries as well as intrusion possibilities. Features such as autonomy, adaptability and flexibility of the "intelligent" agent paradigm allow the managing of network evolution in a controlled way. The focus of our work concerns one critical security management issue, that is, intrusion detection. We propose a novel approach called IA-NSM (intelligent agents for network security management) for intrusion detection using intelligent agent technology. IA-NSM provides a flexible integration of a multi-agent system in a classical networked environment to enhance its protection level against inherent attacks.