Security and privacy are crucial in V2X networks due to sensitive user information. Public Key Infrastructure (PKI) is widely used in C-ITS to ensure security and privacy. However, the practical implementation of PKI faces challenges in achieving seamless communication across diverse ITS projects worldwide. The absence of interoperability between PKI systems and unre-solved issues in existing PKI standards hinder global adoption. Despite available security standardizations using centralized PKI technology for V2X, a universally adopted PKI-based security architecture is necessary. Furthermore, the progress made in 5G V2X technology has demonstrated significant potential for revolutionizing V2X communication in the future. Enhancing the level of trust through integration of the 5G Core Network (5GC) into the PKI security mechanism can lead to more secure and efficient V2X communication. To address these challenges, we propose a blockchain-based architecture that integrates the 5GC network with the PKI infrastructure, aiming to enhance privacy and security in 5G V2X communication. Our solution is designed to be distributed and interoperable, aligned with existing ETSI ITS PKI standard. By utilizing Hyperledger Fabric (HLF) platform, a permissioned blockchain framework, we present the architecture and conduct a comprehensive security analysis to ensure compliance with security and privacy requirements of V2X communications.

Over the past decade, there has been a surge in the popularity of 360° videos streaming. This immersive technology provides users with captivating experiences, driven in part by the growing accessibility of Head-Mounted Displays (HMDs). However, alongside their widespread adoption, 360° videos present several challenges that necessitate optimization models to ensure smooth streaming and a seamless viewing experience. Meeting these performance requires a significant volume of data for training and testing optimization models. This data must encompass various user behaviors to effectively address the complexities of 360° video streaming. Current datasets suffer from limitations in both the number of users and the duration of the videos. The traditional approach, which involves collecting sensor data from 360° videos viewing experiences, is not only time-consuming but poses privacy issues for users. This paper introduces a novel approach to address this data scarcity: the generation of synthetic 360° video viewing data. We aim to overcome the limitations of lack of user viewing data. We have modeled user’s fixation point as an autonomous agent in an environment represented with saliency map. This approach has the potential to revolutionize the development of 360° videos solutions, enabling researchers to create and test solutions with a wider range of user behaviors and video content. This approach can generate a significant amount of data for any 360° video, even without existing viewing data. The source code for the approach and demonstration videos are made available for the research community for tailored dataset generation.

The popularity of 360-degree video streaming has surged in recent years. However, delivering high-quality content with limited bandwidth poses a significant challenge, particularly in mobile delivery setups. Although techniques like predicting a user’s Field of View (FoV) can improve streaming efficiency, multicasting offers a superior solution for delivering content to multiple users simultaneously. However, when users focus on different zones (FoV) in 360° videos, multicast streaming becomes difficult to implement effectively. To address this challenge, we propose in this paper a novel approach that leverages userspecific FoV prediction to dynamically adapt multicast streams. We propose a dynamic architecture that clusters users for optimized streaming based on their predicted FoV. This clustering leverages a DBSCAN-inspired algorithm that incorporates user head movement data. Users with similar FoV preferences are grouped, enabling transmission of only the relevant portions of the 360° video to each cluster. This significantly reduces bandwidth consumption compared to traditional per-user delivery, thus enhancing the overall Quality of Experience (QoE) for users.

Electric vehicles (EVs) offer low carbon emissions; however, drivers frequently encounter difficulties scheduling charging sessions and locating available Charging Stations (CSs). These stations not only need to be conveniently located but also tailored to meet individual charging preferences. Addressing these challenges is crucial for overcoming barriers to the widespread adoption and efficiency of electric mobility, consequently enhancing the overall user experience. This paper introduces an innovative two-stage framework that improves the accessibility of EVCSs by integrating Graph Neural Networks (GNNs) with optimization algorithms. A bipartite graph representing user-station interactions is constructed in the first stage, and a GNN is utilized to leverage this structure. The GNNs efficiently capture complex relational patterns within the graph, enabling the generation of personalized station recommendations. In the subsequent stage, an optimization algorithm is employed to strategically assign users to these recommended stations. This algorithm considers station availability and proximity factors, ensuring optimal user assignments. The proposed recommender framework’s effectiveness is verified using data collected from the Yonne department in France. Experimental evaluations highlight the framework’s efficiency, achieving a high-performance measure of 98%, significantly reducing waiting times and maximizing user satisfaction for drivers compared to baseline approaches.

Immersive systems and$360^{\circ}$video have grown in popularity in recent years. Nevertheless, due to the high bandwidth needs and massive size of these videos, streaming them presents an important issue. A possible solution to this problem is to employ edge caching, which keeps a portion of the video closer to the viewer to reduce latency and assure a higher Quality of Experience (QoE). In this paper, we present a caching policy approach for$360^{\circ}$video streaming that employs a tile-based graph representation of videos. Our method focuses on finding the most relevant tiles for caching. The suggested solution is intended to work efficiently for many videos competing for a single cache, ensuring that the most relevant tiles are cached to maximize performance. It exhibits robustness even in scenarios with a limited number of users, while still being able to effectively handle a vast amount of videos. Our technique can adapt to different video content and user needs by using the graph structure of the videos, making it a flexible and scalable solution for cache management in video streaming. Performance evaluation demonstrate the effectiveness of our method that outperforms existing caching strategies in terms of Cache Hit Ratio (CHR).

Electric vehicles (EVs) have long been recognized as a solution to the shortage of fossil fuels and the environmental problems associated with increasing CO2 emissions. However, charging an electric vehicle can take significant time at certain charging stations. Additionally, the limited deployment of charging stations is a significant barrier to the widespread adoption of electric mobility (e-mobility). In fact, many drivers struggle to locate a convenient charging station before their vehicle’s battery runs out. This study introduces a novel approach to addressing the issue of congestion at public charging stations and reducing the amount of time drivers spend waiting in line by predicting their occupancy. Previous research has relied on traditional Deep Learning (DL) techniques for prediction, which require centralized data collection. Nevertheless, each Charging Station Operator (CSO) holds sensitive data about its charging stations and users that cannot be shared with external parties. To address these privacy concerns, we propose a Federated Deep Learning approach where each CSO trains a DL model locally and then sends the model updates (or parameters) to a server for aggregation. Experiments on a real-world dataset demonstrate that predicting occupancy using the Federated Deep Learning approach achieves promising results (86,21% of accuracy and 91,49% of f1-score ), guarantees privacy, minimizes data transfer costs over the network, and allows individual CSOs to benefit from the rich datasets of others without sharing their sensitive data.

Multimedia applications based on 360° video use a remote server and require a very high bandwidth. Thus, the full transmission of the video from the server can have a negative impact on the quality of the traffic that passes through different communication networks to the end users. One of the solutions to reduce the throughput of 360° videos is to introduce the notion of field of view (FoV) which allows to transmit at a given time only a portion of the video with a higher quality. The remaining portions of the video will be transmitted with a lower quality. In this work, a lossy forecasting approach is proposed to predict the motion trajectory of users of a 360° streaming system in order to ensure better quality of experience (QoE). A new Time Series Forecasting approach using Classification (TSFC) that transforms real data into discrete data so that classification algorithms can be used to make forecasts from them. Our approach can provide online and offline learning and can guarantee live streaming where it ensures a prediction time under 6ms and remarkably well below existing approaches.

Thanks to advanced techniques such as carrier aggregation (CA), alongside with the emergence of the LTE on the 5Ghz band (LTE-U), it has become possible to boost the performance of existing cellular networks. However, LTE-U threatens the performance of co-existing Wi-Fi systems operating over the 5Ghz bands. We will investigate in this paper, an effective coexistence mechanism, that aims at creating a common ground between LTE-U's QOS satisfaction, and the WIFI's performance. We will use a poly-game generator approach, where a set of games is predicted by a neural network, to solve the issue.

The unlicensed band was found to be useful in terms of users applications QoS Regarding the downlink throughput. The unlicensed band, once used exclusively by the military and medical industry, will soon be exploited for the public interests. The large bandwidth guaranteed by the 5GHZ frequency band will help reduce the data transmission latency. It is for these reasons that the LTE-U (Long Term Evolution on Unlicensed band) was developed and will play a key role in the wireless networks of the 2020's. Its deployment, however, will be at the expense of other existent wireless networks technologies, specifically the Wifi. Indeed once the Wifi networks deployed alongside with the LTE-U, their users applications will be constantly interrupted, since the LTE-U is a dominant technology Regarding the channel access. This issue will cause a degradation of a large part of those deployed users in the heterogeneous networks regarding the QoS. Which is why it is important to figure out a solution that allows Wifi-exploiting applications in heterogeneous networks to serve their users with a minimum value of QoS. In this publication, we will detail the important steps towards a coexistence between the Wifi and the LTE-U, using the cooperative game theory, and more specifically the canonical coalition game, for a fairer channels/sub-channels allocation. The paper also solves the time allocation problem between the access points and the small cells, thanks to the bankruptcy game, which is an entitlement problem involving the allocation of a given amount of a perfectly divisible time among the wireless nodes. The publication will also show that the proposed solution provides a higher average user's throughput than the Bargaining Game solution, whether the user is in the Wifi or the LTE-U network.

LoRaWAN is the new transmission protocol for Low-Power Wide Areas (LPWA) networks. In addition to long range communications, one of the most solid arguments in favour of the LoRaWantechnology is its ability to operate at orthogonal spreading factors (SFs). This enables simultaneous transmissions at different data rates on the same frequency channel. But Recently, it has been shown that transmissions at different SFs are not perfectly orthogonal which generates cross-SF interferences (i.e. interferences between different spreading factors' transmissions). Therefore, we propose here an analytical model of a single cell LoRaWAN under unsaturated traffic, duty cycle and multi-channel deployment conditions. The model considers both perfect and imperfect SF orthogonality scenarios. The network performance are then derived in terms of achievable throughput at the gateway as a function of the offered load, the number of received packets per device and the percentage of successful transmissions. All the Results show that if spreading factors are assumed to be orthogonal, the capture effect improves the network performance in comparison with the theoretical pure Aloha access scheme. Whereas, the consideration of imperfect SF orthogonality revises the LoRaWAN performance downward.

Cluster-based cognitive radio sensor networks (CRSNs) are envisioned as a strong driver in smart grid (SG) network development. Added to the sensors' capability to exploit the temporally available licensed spectrum, a hierarchical CRSN allows a better network organization in such a harsh electrical environment. However, in CRSNs, one common control channel (CCC) is usually assumed to exist allowing sensors to exchange control messages before every data transmission. This assumption is not always possible given the licensed signal random activities. Furthermore, SG sensors have to be aware of their neighboring nodes' priorities. Indeed, sensors monitor different applications that have heterogeneous impacts on the SG. Accordingly, in this paper, we design a new solution that allows hierarchical data transmission in CRSNs without being dependent on a CCC and while considering the prioritization in the SG network. We first propose a new clustering algorithm. Then, channels are assigned to the sensors based on local estimates of other nodes' spectrum availability and priority. Given the opportunistic CRSN, we use a Partially Observable Markov Decision Process (POMDP) to allocate channels distributively. Performance evaluation reveals that the spectrum resources are fairly shared between sensors and that our solution outperforms existing work in terms of spectrum utilization.

Wireless sensor networks (WSNs) are considered as the key technology for IoT (Internet of Things) applications thanks to their robustness and their deployment ease. The IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for multi-hop WSNs. However, RPL poorly adapts to sensor nodes' movement which rapidly alters the network performance. Therefore, we propose in this paper, a Bayesian model to accurately predict the sensor nodes' speed distributions. Then, we introduce the Mobility based Braided Multipath RPL (MBM-RPL) to support mobility over RPL. MBM-RPL establishes a primary path based on a new routing metric that exploits the predicted sensor nodes' speed values. An alternative path is established to prevent links expiration along the primary path. To evaluate the performance of MBM-RPL, we first validate the accuracy of the Bayesian model using the Cooja simulator. Then, we compare the performance of MBM-RPL with other RPL based approaches in terms of packet loss rate (PLR) and average transmission delay (ADT).

Wireless sensor networks (WSNs) are considered as a crucial technology that will definitely ensure a permanent growth to emergent applications as smart homes, smart grids, etc. In the particular home area network (HAN) context, sensor nodes will be in charge of the power monitoring between the smart home appliances. But, the communications in WSNs generally relay on the ISM bands, characterized by a narrow bandwidth and a high collision/interference ratio. Hence, the cognitive radio technology can be used to overcome the spectrum resource scarcity in WSNs. In cognitive radio networks, the sensor nodes will opportunistically access licensed channels if they are vacant of primary signals, thus ensuring a better exploitation of the available radio resources. Generally, a common control channel (CCC) is used to exchange control messages in the cognitive network. But, relying on a CCC is not always feasible especially if the number of contenders in the network is important or if the sensors do not share the same spectrum resources. Therefore, we propose in this paper a new framework for the by-domestic energy control in smart homes using cognitive radio sensor networks (CRSNs). Our proposed framework, called Cooperative Spectrum Resource Allocation (CSRA), aims to completely avoid the CCC and to achieve a distributed fair spectrum resource sharing among the sensors in smart homes. The fairness of CSRA is ensured through partially observable Markov decision process (POMDP) that performs the channel assignment, to each node, based on local spectrum utilization estimates. Performance evaluation, using the OMNeT++ simulator, reveals that the proposed CSRA approach achieves a fair spectrum allocation between sensor nodes in smart home systems.

This paper addresses the channel occupation and the channel selection problem for the Long Term Evolution Unlicensed (LTE-U) technology when coexisting with the WiFi system on the unlicensed spectrum (typically the 5 GHz band). For instance, LTE is a communication standard developed by the 3GPP corporation based on the GSM/EDGE and the UMTP/HSPA technologies and conceived for high-spaced wireless communications. If we focus on LTE-U, it is an extension of the LTE-A (LTE-Advanced) scheme in unlicensed bands. In this paper, we will discuss the coexistence matters between the WiFi and LTE-U technologies. Therefore, we review the different solutions discussed in literature that addressed the coexistence issue. These works are either based on fair spectrum allocation between LTE-U and WiFi technologies or on the game theory paradigm where multi channel access and inter-dependance scenarios are discussed. As a future work, we intend to further investigate the coexistence issues between LTE-U and WiFi systems, especially in the case of inter-dependance scenarios and other configurations including hidden/exposed terminal problem in WiFi networks.

In this paper, we propose a new distributed approach for the electrical substation monitoring in suburban environment using the cognitive radio (CR) technology. For instance, suburban areas are characterized by the scarcity of the free-accessed wireless bands due to their proximity to inhabitants' zones and the abundance of the usually underutilized spectrum resources provided by the operators. Thus, our focus is to fully benefit from the cognitive radio sensor network (CRSN) technology to provide efficient data aggregation and reporting processes in substations upon unexpected events' detection. Our approach, called Distributed Event-driven data Aggregation and constrained multipath Routing (DEAR), will completely emancipate from the common control channel (CCC) limitations, generally used for the channel assignment in cognitive networks. DEAR uses the graph coloring paradigm for the licensed spectrum allocation during the data aggregation and reporting phases and a constrained multipath ”Beam” routing to prevent packet loss and failures in the harsh substation environment. Performance evaluation reveals that DEAR ensures a rapid data transmission and an efficient channel assignment in CRSNs.

Cognitive radio sensor networks (CRSNs) have been widely considered to monitor the smart microgrid environment. In such an environment, different applications with various characteristics may coexist in the network. As a consequence, the sensor nodes deployed to collect the generated data need to access the available spectrum resources with different priorities. Thus, achieving a fair channel allocation in the microgrid driven CRSN is a very challenging task. In this paper, we propose a new Distributed Unselfish Spectrum Assignment approach (DUSA) for One-Hop smart microgrid communication network. The proposed approach exploits the promising cognitive radio technology and the heterogeneity of the traffic generated by sensor nodes to satisfy the requirements of the smart microgrid applications. It achieves channel allocation without relying on a predefined common control channel (CCC) to exchange control messages. As DUSA is a distributed approach, it aims to dynamically predict the primary channel availability. Thereafter, each sensor node estimates the buffer occupancies of its neighbors through discrete Markov chains. Unselfish distributed channel allocation is achieved based on a Partially Observable Markov Decision Process (POMDP) formulation. Performance evaluation using the OMNeT#x002B;#x002B; simulator, reveals that DUSA achieves a fair sharing of the spectrum resources and improves the spectrum utilization compared to the CCC-based resource allocation scheme.

Wireless sensor networks (WSNs) are widely used nowadays in various domains. In general, the applications in which the WSN is deployed need that this network presents a minimal degree of reliability, effectiveness and robustness. However, the specificity of the nodes deployed in this type of networks makes them prone to failures. In this paper, we discuss the recovery from simultaneous failures for a large scale WSN in a multichannel context. Indeed, we propose a deployment scenario which uses thousands of sensor nodes and relay nodes. Then, we propose Simultaneous Failure Recovery based on Relay Node Relocation (SFR-RNR) approach which aims to recover from simultaneous failures. These failures lead to the damage of a whole segment of the WSN, and hence, the loss of the monitored field coverage and/or connectivity. SFR-RNR rearranges a minimal set of relay nodes to restore, partially, the coverage and improve or restore the connectivity; then it reallocates the channels to minimize the interferences. The performance of the proposed approach is evaluated by simulation.

The interest for wireless sensor networks (WSNs) is continuously growing especially with the emergence of applications such as smart grids, smart cities, e-health, etc. where billions of objets (sensors) will be permanently connected to the Internet. In this context, the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for the next generation multi-hop WSNs. The RPL scheme fits the sensor nodes characteristics since the protocol was originally designed for energy efficiency. Nevertheless, in the case of sensor nodes' movement, RPL poorly adapts to such scenarios which rapidly alters the network performance. In this paper, we investigate the problem of mobility support over RPL. Therefore, we propose a new Sensor Nodes' Speed Classifier (SNSC) to predict the sensor nodes' movement in the network. Then, we exploit the speed values-predicted by the SNSC model-to enhance the behavior of the native RPL in the mobility context. Our modified RPL scheme is called Mobility Prediction based RPL (MP-RPL). To evaluate the performance of MP-RPL, we first validate the SNSC model using the Cooja/Contiki simulation environment. Then, we compare MP-RPL to the native RPL in terms of Packet Loss Rate (PLR) and Packet Delivery Delay (PDD).

The IEEE 802.15.4 standard is considered as the most notorious MAC layer for Wireless Sensor Networks (WSNs) in both single hop and multi-hop contexts. For instance, in a multi-hop environment, the unslotted IEEE 802.15.4 is adopted as the medium access scheme. Several works evaluated the performance of the unslotted IEEE 802.15.4 in terms of average delay, exact average energy consumption, reliability, etc. But, none of the existing studies derived analytical energy consumption bounds of this MAC layer. In this paper, we propose an energy consumption analysis of the unslotted CSMA/CA based on a discrete Markov chain modeling of the backoff process. Therefore, we derive approximated average energy consumption and energy consumption bounds of beaconless IEEE 802.15.4. The probabilistic energy consumption bounds are computed based on generalized transition state diagrams. The bounds are expressed as a function of the probability that a packet energy consumption exceeds a certain value. Finally, we validate the analytical results by simulation using the Omnet simulator.

Wireless Sensor Networks (WSNs) become one of the most common technologies that can be deployed in various domains. However, the networks suffer from many problems caused by the limited sensor resources and the harsh environments where these networks are deployed. Many algorithms have been proposed to manage data routing while respecting the specificity of such networks. One of the known proposed routing protocols is RPL (IPv6 Routing Protocol for Low power and Lossy networks). Nevertheless this protocol is designed with consideration of the limited sensor energy, it cannot be integrated in may WSN applications. In fact, RPL, as it has been proposed, assume that the sensor nodes are static and don't manage any type of mobility. In this paper, we propose a new approach, called BMP-RPL (Bayesian model Mobility Prediction RPL for wireless sensor networks), that aims to adapt native RPL to nodes' mobility scenarios. This approach is based on nodes' identification and velocity prediction as well as the estimation of the link duration. Thus, we introduce a new metric which constructs routes according to the node status and within information lost. The performance of our approach is proved through simulation.

Wireless sensor networks (WSNs) are widely used nowadays and in a various domains. However, the specificity of the nodes deployed in this type of networks makes them prone to failures. To overcome this problem and guarantee the continuity of the network functioning even in the presence of node failure, fault tolerance mechanisms need to be designed and integrated to the operation of those networks. These fault tolerance mechanisms will more precisely deal with recovering from a failures and resuming the correct functioning of a WSN. With that aim, we propose in this paper a new centralized curative approach, called Rotating Nodes based Failure Recovery (RNFR), dedicated to restore connectivity in multi-channel WSNs. The proposed solution targets the failure of particular nodes designated as articulation nodes, which leads to the partitioning of the WSN into many segments isolated from each other and leading to connectivity loss. Therefore, the main tasks of RNFR are the restoration of the connectivity after an articulation node failure using reorganization and reallocation of channels. Moreover, the solution uses a node rotation technique to communicate the recovery information to all the disjoint parts of the network. The proposed approach proved to be interesting by giving motivating results while evaluated through simulation.

One of the common challenges in Wireless Sensor Networks (WSNs) is the degradation of the performance due to several factors. In one hand, the interference between concurrent transmissions can affect the efficiency of the network and considerably degrades its performance. The exploitation of the multiple channels available in sensor technology and the development of protocols for WSNs can be a solution to mitigate this interference. In this case, the way the channels are assigned has a significant impact on the performance of multi-channel communication. In the other hand, the faults occurred in WSNs are another factor that degrades the WSN performance. In this paper, we propose a distributed energy-efficient solution for multi-channel allocation based on the routing. This solution implements a fault recovery mechanism to reconnect the network after an articulation node failure. A main task of the proposed approach is to minimize the number of interferences when allocating the limited number of available channels. As a second task, the approach minimizes the energy consumption by defining a sleeping/activity strategy. The fault recovery mechanism aims to restore the network connectivity and reallocate channels without affecting the whole WSN. The performance of the proposed solution is evaluated by simulation.

One of the most common needs in wireless sensor networks (WSNs) is the continuity of network function even in the presence of some node failures. This is called fault tolerance. In general, fault tolerant solutions can be preventive or reactive: the preventive techniques aim to prevent the failure by minimizing and balancing the energy consumption in each node, while the reactive techniques intervene after a failure was detected. In this paper, we propose a new preventive/reactive fault tolerant scheme dedicated to manage the energy consumption and reconnect the WSN in case of articulation node failure. The specificity of this scheme is the use of multichannel communications, allowing simultaneous data transmission, which decreases the interferences between nodes and then decreases data retransmissions. The second task of this scheme targets the reorganization of the network after a network portioning episode. Such episode means that the WSN is partitioned in many segments after an articulation node failure. More precisely, we present here two heuristics for channel allocation/reallocation and WSN reorganization after a network failure. The performances of the proposed scheme is evaluated and proved through simulation.

Virtualization is a key technology to enable cloud computing. It enhances resource availability and offers high flexibility and cost effectiveness. However, the driver domain-based model for network I/O virtualization exhibit poor networking performance. In this paper, we evaluate the networking performance of VMs based on Xen. The I/O channel transferring packets between the driver domain and the VMs is shown to be the bottleneck. To overcome this limitation, we proposed a packet aggregation-based mechanism to transfer packets from the driver domain to the VM. Experimental performance evaluation showed that our proposal allows the VMs throughput to scale up at line rates. Furthermore, we studied the impact of the aggregation on the packets delay. For this purpose, we proposed a system modeling based on queuing theory. The proposed model allowed us to dynamically tune the aggregation mechanism to achieve the best tradeoff between the packets delay and throughput. The proposed I/O virtualization model henceforth satisfies the infrastructure providers to offer cloud computing services.

In this paper, we propose a new approach to ensure fault tolerance in wireless sensor networks (WSNs) while guaranteeing both coverage and connectivity in the network. Hence after reviewing the fault tolerance related works in wireless sensor networks and enumerating the requirements that must be satisfied by our fault tolerance solution, we present the different mechanisms we propose to maintain both coverage and connectivity in the network in the case of node failure. Our approach is a proactive one in the sense that it aims to replace the “up to fail” node before its defection. In the case of impossible replacement of the “up to fail” node, a fast rerouting mechanism is proposed to forward the traffic initially routed via the “up to fail” node. Performance evaluation of our fault tolerance approach shows that the number of nodes potentially eligible for the “up to fail” node replacement depends on a threshold about the node redundancy as well as the network density metric. Moreover, we show that, compared to a classical routing algorithm, our fast rerouting mechanism reduces the packet loss rate in the network.

We propose a new technique to relocate a set of redundant sensor nodes from their initial deployment area called ROI (region of interest) to a new location outside the ROI called COI (center of interest) where a new event happened (e.g., to measure the soil contamination level in the case of a chemical spill disaster). Relocating mobile sensor nodes from their initial deployment area ROI requires covering that area at a given center of interest while maintaining connectivity with the initial region of interest. Our proposed technique to relocate redundant mobile sensors nodes towards a new target position is a four-phase solution that includes the clustering phase and the cell head election, the evaluation of the number of nodes to be relocated, the propagation of the COI position, and finally the election of the redundant nodes and their relocation. A performance evaluation of our novel relocation approach shows that the consumed energy and the relocation time increase as the distance separating the initial ROI from the COI increases regardless of the size or the density of the initial deployment area.

Network virtualization is a promising technology that offers high levels of flexibility, isolation, extensibility and cost-effectiveness. In this paper, we focus on router virtualization. We evaluate the forwarding performance of virtual routers when the data plane runs in the guests. This scenario offers a high level of isolation and flexibility, however, it suffers from performance limitations due to the virtualization overhead. We show that the he I/O communication between the driver domain and the guests is the bottleneck. To overcome this limitation, we propose a new packets aggregation mechanism that transfers groups of packets between the driver domain and the guests. This enhancement makes the forwarding performance of one guest scale up to 1600 Kp/s. Furthermore, we propose a dimensioning tool in order to determine the maximum achievable throughput with regard to the container size.

In this paper, we propose a real time scheduling policy over 802.11 DCF protocol called Deadline Monotonic (DM). We evaluate the performance of this policy for a simple scenario where two stations with different delay constraints contend for the channel. For this scenario a Markov chain based analytical model is proposed. From the mathematical model, we derive expressions of the average medium access delay called service time. Analytical results are validated by simulation results using the ns-2 network simulator.

In this paper, we focus on the quantitative quality of service (QoS) guarantee in a differentiated services (DiffServ) domain that can be granted to an expedited forwarding (EF) flow in terms of end-to-end delay. This study shows that delays much smaller than the deterministic bound can be guaranteed with probabilities close to one. An admission control derived from these results is then proposed, providing a probabilistic QoS guarantee to EF flows.