This paper proposes a modular and interoperable Digital Twin (DT) architecture compatible with large-scale road networks, leveraging standard-based technologies to support re-producible, scalable, and distributed deployments. Although scalability, interoperability, and real-time responsiveness are widely acknowledged as essential, most existing DT solutions address these challenges in isolation. The proposed architecture mitigates these limitations by focusing on road network-specific constraints, such as continuous real-time data flows, heterogeneous service integration, and edge-cloud coordination. We implement this architecture using open-source technologies, ensuring reproducibility across domains and suitability for diverse applications. The contribution of our work is the proposed architecture and we validate it through our performance evaluation, by testing several DT-oriented frameworks. The evaluation includes time distribution latency measurements following a realistic edge-oriented scenario. The results confirm the suitability of the proposed architecture for scalable, interoperable, and extensible DT deployments, while providing guidance for future implementations.

International audience

Online Social Networks (OSNs) have rapidly integrated into our daily lives since their emergence in 2004, primarily serving as platforms for sharing personal information. This paper introduces a novel category of social networks: Spontaneous and Ephemeral Social Networks (SESNs). Unlike traditional OSNs, SESNs are event-centric, facilitating real-time connections and content sharing among participants within specific contexts. The main objective of SESNs is to improve the production, sharing, and consumption of digital content among network members. SESNs operate on a distributed peer-to-peer architecture using ad hoc mobile networks, leveraging the capability of mobile devices to communicate directly with each other in peer-to-peer mode. SESNs are ephemeral by nature, dissolving once participants disperse from the event location. However, for future analysis, some content may be retrievable from an external server after the event. A potential concern is that collaborative content creation within SESNs resembles crowdsourcing. However, in SESNs, the service provider may retain control over the data even after the event concludes. This centralized management of user-generated content could pose risks to user anonymity. To address these concerns, we propose a blockchain-based architecture that certifies transactions and ensures data anonymity in a decentralized manner. The proposed architecture demonstrates its robustness through a performance evaluation and a comprehensive security analysis. Our solution guarantees data integrity, confidentiality, privacy, anonymity, and network resilience. Additionally, blockchain technology is employed to ensure SESN compliance with the General Data Protection Regulation (GDPR).

Vehicular Edge Computing (VEC) supports task offloading from vehicles to edge servers, improving latency and reducing onboard computation. However, traditional Federated Learning (FL) and Reinforcement Learning (RL) face challenges in dynamic vehicular contexts, including synchronization delays, privacy issues, and poor convergence. This paper presents a distributed offloading framework that integrates Twin Delayed Deep Deterministic Policy Gradient (TD3), Prioritized Experience Replay (PER), and a Mobility-Aware Asynchronous Federated Learning (MAFL) strategy. Vehicles that generate computation tasks-referred to as task vehicles-act as local learning agents, while Road Side Units (RSUs) perform weighted asynchronous model aggregation within a three-tier architecture that ensures local adaptability and global consistency. Simulations show that our method significantly outperforms synchronous FL, standalone TD3, and Asynchronous FL (AFL) baselines, reducing latency by 11.4 %, energy consumption by 11.9 %, and improving reward by 11.0 %. These results demonstrate the potential of combining deep RL and asynchronous FL for scalable and efficient offloading in intelligent vehicular networks.

In 1992, OMNeT++ has been developed at the Technical University of Budapest by Andras Varga. More than 30 years later, it remains a cornerstone framework in academia and industry for both simulating wired and wireless networks, with the integration of the INET package. While it is possible to generate environments within INET in the context of wireless communications in order to simulate the impact of obstacles on the signal propagation, it necessitates manual construction of each element of the structure. Conversely, Digital Twins of structures are increasingly utilized within Industry 4.0. In this work, we introduce an OMNeT++ Open-Source Plug-In, named BOMNET, designed to automatically generate the environment using the Digital Twin of a structure. We first present OMNeT++ and INET features before explaining BOMNET process for extracting relevant Digital Twin data and generating the environment. We then present an illustrative example of BOMNET’s capabilities, comparing simulated results with and without the use of the Digital Twin to real-world measurements of a WiFi emitter testbed situated in a large building.

A recent surge in interests regarding Digital Twins in the networking community has emphasized its potential across various applications, as of the useful big data it contains. One of these studied applications involving 3D Digital Twins of buildings is the optimization of indoor IoT networks deployments. Indeed, maximizing the coverage of the deployed devices while minimizing their number and fitting technical constraints is a tedious problem, particularly in complex environments. In this paper, we propose a detailed analysis of the impact of using the Digital Twin by comparing the effects of incorporating it to optimize IoT Networks deployments against scenarios where it is not utilized. We first present the impact of the 3D Digital Twin on the environment and channel modeling, before providing a performance evaluation of Quality of Service (QoS) using a deployment optimization method based on a genetic algorithm, conducted using numerical and simulation tools on two different environments, contrasting scenarios with and without the use of the Digital Twin onto two different buildings. The obtained results show that leveraging the Digital Twin yields enhanced QoS in terms of average quality of link and coverage rate, while providing new solutions not discerned in the alternative environment within the context of a challenging technical constraint.

Nowadays, sensor networks are used in various fields and for multiple purposes in medicine, agriculture or industry. In a network, different kinds of sensors are often involved. This heterogeneity makes it complex and time-consuming to implement the exploitation of the data acquired by these sensor networks. We notice a growing need for scalable and rapidly deployable infrastructures to allow for interoperability for the processing, the storage or visualization of collected data. Faced with these challenges, we define a framework to facilitate the automatic deployment of data exploitation applications in heterogeneous sensor networks. We propose a Model-Driven Engineering-based approach that provides a high-level model to describe such infrastructures. Along with automatic code generation, our model aims to facilitate the exploitation of collected data through a graphical interface. The model we define allows for the description of heterogeneous sensor networks with a data exploitation point of view.

Recently, we have witnessed an operational deployment of 5G in big cities to increase the communication capacity of users. However, with the ever-increasing of devices in dense networks, the current 5G could fail due to its unscalable issues. Scientists in academia and industry are actively analyzing this open issue within the context of Beyond 5G (B5G) and 6G before their realistic deployment. Moreover, the high densities of devices lead to the challenges of over-energy consumption and unfair Quality of Service (QoS) caused by the distances from the base station and existing obstacles separating the communicating devices. The network relay election method can be seen as an appropriate option to overcome these challenges, but this solution should be optimized according to the environment's dynamics. Therefore, in this paper, we propose an energy-efficient election-based method that selects relays so that it maximizes the overall network lifetime while maintaining a certain level of QoS. Considering that our system is deployed in an unknown dynamic environment, we employ a deep reinforcement learning technique that aims to optimize the network relay selection, maximize the residual energy levels of devices, and ensure fair and high data rates. Our system has been tested through a discrete-event simulator, and its performances have been evaluated and compared to a set of baseline and benchmark methods. It has been shown that our proposed system outperforms existing relevant solutions.

In response to the growing demand for efficient task offloading in the context of the Internet of Vehicles (IoV), this study presents a new approach aimed at optimizing computing resources and minimizing latency. The proposed solution relies on a framework based on deep reinforcement learning to simultaneously improve computing resource utilization and reduce latency in Internet of Vehicles task offloading scenarios. The framework harnesses the power of deep reinforcement learning to facilitate intelligent decision-making and adaptive resource allocation. By taking into account both the costs associated with computing resources and the requirements of delay minimization, our approach achieves significant improvements in performance and profitability. Rigorous experimentation validates the effectiveness of our method, demonstrating substantial reductions in resource expenditure and vehicle task delays compared to local task execution.

This paper presents a novel framework for secure management of Electronic Health Records (EHRs). It addresses the challenges of privacy protection, data ownership, and regulatory compliance. By adopting a patient-centric approach, the architecture ensures compliance with international regulations while providing efficient data security and access control mechanisms. Leveraging blockchain technology, smart contracts, De-centralized Identifiers (DIDs) and InterPlanetary File System (IPFS), the model facilitates decentralized au-thentication and authorization, ensuring patients full ownership and enabling multiple user access to medical records. Blockchain technolgy and IPFS ensure data integrity and immutability and transaction transparency and auditability. Smart contracts are implemented to leverage multiple transcations to grant, get, delegate, and revoke access to eHealth data. As a proof of concept, we implemented the proposed architecture on a polygon blockchain and integrate the walt.id API for DID generation, enabling the patient with the right to grant and revoke access for his own records. The proposed architecture aligns with General Data Protection Regulation (GDPR) directives concerning personal data privacy, guaranteeing both data security and transaction transparency and traceability.

Facilitated by the widespread adoption of Internet of Things (IoT) networks, Location-based services (LBS) have emerged as a new type of services, requiring a high quality of service (QoS) and to provide access to all devices within predefined zones of interest. This is made possible via specific IoT Networks architectures based on the Software Defined Network paradigm. To address the challenge of unstable IoT networks management, where devices can move, appear, or vanish unpredictably, we propose a novel architecture based on a selection process of dominant devices acting as gateways, ensuring continuity of service. We investigate two selection processes, respectively based on Connected Dominating Sets and Deep Q-Network techniques. The objective of this method is to optimize energy consumption while providing high QoS and extending network access to offline devices within predefined zones of interest. In order to evaluate the performance of the proposed architecture with different selection processes, we conducted experiments using emulation tools allowing communication mode demand generations. The metrics used were the proportion of dominant devices, the energy consumption savings, the quality of service and the network extension to offline devices. Ultimately, we present a recommendation concerning the selection process based on the needs of the system.

Mobile communication networks could make a significant difference in rescuing affected people in post-disaster scenarios. However, the existing communication infrastructures tend to be out of service in such scenarios. To solve this issue, Unmanned Aerial Vehicles (UAVs) could be launched as flying base stations to provide the required coverage to Rescue Members (RMs) and allow them to communicate and transmit crucial information through the established links. Meanwhile, with the unpredictable movements of RMs, three serious issues are affecting the deployment of UAVs: (i) the control of their mobility, (ii) their limited energy capacity, and (iii) their restricted communication ranges. Aiming to address these issues, we propose deploying an intelligent connected group of energy-efficient UAVs assisting RMs and providing them communication coverage in the long run. These requirements are satisfied using a deep reinforcement learning strategy to learn the environment dynamics and make good trajectory decisions. Simulation experiments have demonstrated the potential of our framework compared to baseline methods to provide temporary communication networks for emergency response teams during disaster relief missions.

Most of the recent Internet of Things (IoT) applications are highly dependent on the freshness of collected data from IoT devices. Therefore, the concept of Unmanned Aerial Vehicle (UAV) assisted IoT has received a lot of interest where UAVs are deployed as data collectors and can effectively meet the requirement of IoT applications in terms of reducing the Age of Information (AoI) metric of collected data. However, as it is widely known, UAVs are energy-constrained devices with limited computational and communication capacities, preventing them from timely completing their missions. This energy issue can be addressed by deploying fixed charging stations, but this option would interrupt UAVs from their missions, and they should regularly return to these stations to charge their batteries. In this paper, a UAV data collector is dispatched to serve a set of IoT devices while being permanently followed and supported by an Unmanned Ground Vehicle (UGV) as a mobile charging station. Considering an unknown dynamic IoT environment, the problem of AoI minimization is reformulated as a Markov Decision Process (MDP). Indeed, we employ the multi-agent deep Q-network (MADQN) method called AGAIN to minimize the average AoI of IoT devices, timely recharge the UAV using UGV, and optimize the UGV trajectory on the ground by considering the terrestrial obstacles and the IoT movements. A series of simulations are run to demonstrate the efficiency of the proposed method in reducing the AoI of IoT devices compared to baseline methods.

As vehicles continue to integrate with the Internet of Things to access better services, the abundance of vehicular applications and network fluctuations pose a challenge to in-vehicle terminals to perform efficient computation. To tackle this issue, a task offloading algorithm for the Internet of Vehicles (IoV) was developed using a four-stage local edge cloud and reinforcement learning model. The algorithm involved implementing communication methods and cost functions between vehicles, analyzing their computational requirements and real-time status-based cost function, and proposing an experience-based offloading strategy using multi-agent reinforcement learning. The simulation results demonstrated that the algorithm improved the likelihood of task success while balancing task vehicle utility, and service vehicle utility under different constraints.

Wireless Camera Sensor Networks (WCSN) have emerged as a promising technology in recent years, due to their extensive applications in various fields, including critical contexts such as evacuation. However, optimizing a WCSN layout to reduce its cost while maximizing coverage is tedious. Cameras can be interconnected thus may require connectivity, and the evacuation context requires full-view of the targets and zones of interest to cover in priority. In this paper, we propose a new method for optimizing WCSN indoor layouts under k-connectivity constraint while considering full-view requirement and predefined zones of interests. This method utilizes an ISO 23601 evacuation plan as input to generate grid environments and an Evacuation-Oriented Multigraph. This allow to model the full-view requirement and build a relevant set of initial solutions for a Pareto-based multi-objective genetic algorithm, which computes the optimized WCSN layout. The performance evaluation is conducted using numerical and simulation tools on scenarios with different connectivity constraints and fields of view. The obtained results show that the proposed solution outperforms existing solutions and random layouts in terms of coverage.

In recent years, research on the Internet of Things (IoT) has seen widespread growth, with a particular focus on mobile networks, especially intelligent transportation systems (ITS), due to their potential application in smart cities. However, communication within ITS suffers from numerous issues, leading to multiple attacks. Therefore, ensuring the secure transmission of messages in ITS poses a significant challenge. Recently, Srinivas et al. have proposed “a three-factor authentication scheme for Big Data collection in IoT-based ITS, called UAP-BCIoT”. However, the proposed scheme has some shortcomings in terms of authentication. In this paper, we show how this flaw affects the users’ access to the data provided by the IoT devices, which are deployed on vehicles. In addition, we propose enhancements to overcome the scheme’s authentication issue. Furthermore, a comparative analysis reveals that the enhanced UAP-BCIoT achieves better efficiency, security, and performance compared to UAP-BCIoT and other schemes.

This study focuses on online evacuation management. We design a framework to solve dynamic population evacuation (DPE) problems by creating an initial evacuation plan and employing inter-vehicle communication under a cloud computing architecture. For creating an initial plan, we formulate and solve dynamically (i) the shelter allocation problem (SAP) to choose a shelter node representing safety, and (ii) dynamic traffic assignment (DTA) to choose the best path leading towards the chosen destinations. The DPE process then begins with the planning phase to design a plan for the evacuation operation. Afterward, a vehicular ad hoc network (VANET) is deployed according to vehicular cloud computing (VCC) to make it possible for evacuees to share information and have online guidance from the server. We use vehicular communication to revise vehicle decisions initially taken by the plan, considering the dynamic progression of the catastrophe risk and congestion levels. To approve our new framework, we apply it to a real test case of Luxembourg city. We measure the clearance time and the mean evacuation time of the proposed solution and compare them with the results of other planning methods in the literature. Results show that online solving of DPE can minimize mean evacuation time and network clearance time by more than 10% compared to other models with only planning optimization. In addition, we analyze the performance of the proposed framework by changing the penetration rate of connected vehicles, and we observe that the increase in penetration rate leads to a decrease in network clearance time.

Unmanned Aerial vehicles (UAV) are a cost-effective and flexible alternative to bring computing to the edge network as close as possible to end devices. Nevertheless, UAV’s capabilities are also limited. Considering the facts that UAVs are generally deployed in cases where the terrestrial networks are congested, damaged or absent, and that end devices’ mobility and traffic distribution directly affects UAV’s load and performance, we investigate in this paper UAV’s cooperation in a cluster of UAVs. Therefore, we propose the use of the cluster head for the computing offloading scheduling and balancing using a minimum-cost flow algorithm and present an objective function for a reliable offload path selection.

The Social Internet of Vehicles (SIoV) requires extensive information sharing and adequate protocols. The blockchain technology and the IEEE P1931.1 ROOF Standard are very promising for the SIoV. In the present work, we propose RoofCoin, a novel blockchain for SIOV, that meets the IEEE P1931.1 ROOF standard and explores the Vehicular Social Network (VSN). To provide reliable and efficient transactions within RoofCoin, we proposed PoSt(Proof of Stature), a new consensus protocol that selects the miners according to: the mining price, the reliability of the connection link between the vehicle and the miner, and the miner’s reputation.

IoT networks allow various new types of services, such as Location-Based Services (LBS), requiring an end-to-end Quality of Service (QoS) without any interruption. However, sustaining these networks is difficult when the devices can move and vanish at any time, as they can be carried around, might run out of battery, or be turned off by users. In this paper, we propose a new programmable multitier architecture and a continuity of service protocol named ConSerN, which are based on the Software Defined Network (SDN) approach and Connected Dominating Sets (CDS) techniques. Unlike the existing solutions, the proposed mechanism considers a dynamic topology where nodes are not static from a mobility and topology point of view. Furthermore, as most of the applications using LBS need to consider the geographic position of the connected objects, we have added it in the selection process of the dynamic gateways. The performance evaluation was conducted using experimentation and emulation tools allowing scenario generations. We used three main metrics i.e. the quality of link, the number of dominant nodes and the amount of control messages. The obtained results show that the proposed solution benefits from a high number of gateways to achieve great performance in terms of overhead and quality of link.

The Social Internet of Vehicles (SIoV) paradigm is a social network where every vehicle establishes social relationships with others. The IEEE Standards Association develops a number of market relevent standards for for 5G and Beyond. Among these standards, the IEEE P1931.1 standard provides a promising architectural framework for SIoV. In this paper we propose RoofCoin, a blockchain for SIOV based on the IEEE P1931.1 standard. In order to deal with constrains related to Internet of Vehicles, we also provide a novel consensus protocol for RoofCoin, named PoSt(Proof of Stature). In PoSt, the blockchain’s miners are selected according to three features: the mining price, the reliability of the connection link between the vehicle and the miner, and the miner’s reputation. In order to evaluate the miner’s reputation, we introduce a trust model that calculate the miner’s stature in the Vehicular Social Network (VSN). Experimental results have shown that the proposed PoStprotocol is efficient even in low coverage zones, where only two trusted miners are discovered.

Sensor networks are present in various fields such as medicine, agriculture, industry, or urban areas at different scales from building to city-scale. They are useful for numerous applications, often using different kinds of sensor technologies bringing in their own particularities, thus contributing to the heterogeneity of such networks. In this context, there is an in-creasing need for scalable and rapidly deployable infrastructures to allow interoperability for the management and processing of collected data. In this paper, we focus on an approach aiming to facilitate the automatic deployment of heterogeneous sensor networks. We propose to introduce a Model-Driven Engineering (MDE) approach providing a high-level model to describe such infrastructures along with automatic code generation to facilitate the exploitation of collected data through a graphical interface. Hence, we present a model allowing to describe heterogeneous sensor networks with a data exploitation point of view. We use a high-level intermediary generic code structure to allow automatic code generation towards different targets. Such automation is intended to speed-up establishing new services from existing infrastructures as well as upgrades that might follow. The evaluation of our model is done through a real example.

In a multi-tiers blockchain (BC) architecture, we find heterogeneous BC with different security levels that need to communicate. This communication is ensured by node on each BC tier, called the gateway. This node has a sensitive role in the network, it represents a weak point and a source of attacks propagation between networks. In this paper, we focus on the selection of the gateway node using a distributed trust model to evaluate nodes behaviors inside each BC network. Our solution is blockchain-based and will be implemented above the blockchain without affecting its initial structure. The trust model is inspired from the Spence's model where signals, cost and rewards are used to incite nodes to collaborate and to act honestly. In addition, we introduce penalties for nodes acting maliciously or being selfish. Moreover, the gateway undergoes severe penalties in case he changes his behavior after election. Nodes can complain about the current gateway, their complaints are considered if they are trustworthy and quite numerous. Then, we propose a 2 phases algorithm based on the proposed trust model for a dynamic selection of the gateway node. The 2 phases ensure the election of an honest node with the optimal performances.

With the diversification of 5G services especially the emergence of applications related to the Internet of Things (IoT), slicing is considered as the best way to offer the optimal Quality of Services (QoS) and Quality of Experience (QoE) using the same shared infrastructure. In this paper, we focus on the optimal resource reservation using recent methods and techniques in order to meet the highly variable and dynamic demands. That's why, we propose a taxonomy of different approaches using Artificial Intelligence/Machine Learning (AI/ML) for slicing resource optimization and orchestration. In addition, a comparison study of different solution is proposed. Finally, we highlight future challenges and discuss future research directions.

In blockchain, transactions between parties are regrouped into blocks, in order to be added to the blockchain's distributed ledger. Miners are nodes of the network that generate new blocks that meet the consensus protocol. Thus, when a miner adds a valid block to the distributed ledger, the miner is rewarded. Due to the difficulty of the problem to be solved by the miner in order to find a valid block, it becomes difficult to a single miner to gain rewards. Therefore, miners join mining pools, where the powers of miners are federated to ensure stable revenues for miners. In public blockchains, access to mining pools is not restricted, which makes mining pools vulnerable to considerable threats such as: block withholding (BWH) attacks and distributed denial of service (DDoS) attacks. In the present work, we a new blockchain named PoolCoin that manages reputation in mining pools. In addition, we provide a trust model for PoolCoin, inspired by the job market signaling model. The proposed PoolCoin blockchain allows pool managers to accept trusted miners in their mining pools, while miners are able to evaluate pool managers. The performance evaluation is conducted and the obtained simulation results are presented and discussed. In order to study the efficiency of the proposed trust model, a performance evaluation was provided. Thus, the model parameters' are optimized in order to detect and exclude misbehaving miners, while honest miners are maintained in the mining pool.

Vehicular fog computing is an extension of fog computing in the context of vehicular networks. Vehicles are used to serve mobile users at the edge of networks. The vehicular fog is characterized by its dynamicity due to the mobility of vehicles. The change of vehicular fog members over time causes the variation of the available resources. So, the selection of the suitable fog node (i.e. vehicle) to execute a task is challenged. In some cases, the users' requests can be redirected to the cloud to be processed if no suitable vehicle is found to fulfill local processing. To this aim, we suggest a multi-objective based vehicular fog resource allocation scheme. A software-defined networking (SDN) controller is deployed to manage the allocation of the vehicular fog resources based on the information gathered from vehicles and the received requests. The proposed problem is solved using non-dominated sorting genetic algorithm (NSGA-II).

IP Multimedia Subsystem (IMS) is the main solution for new generation networks providing multiple services proposed by third generation partnership project (3GPP). IMS architecture is based on several protocols guaranteeing connection and data transmission. Among these protocols, Session Initiation protocol (SIP), which is regarded as the main IMS signalisation protocol, hence considered a target for several attacks. To secure this protocol, 3GPP proposed an authentication and key agreement protocol called IMS-AKA. However, this protocol suffers from weaknesses in terms of complexity and user identity protection. In this work, we are particularly interested in IMS- AKA+ protocol, an improved version of IMS-AKA. This work aims to analyse performances of IMS-AKA+ against IMS-AKA using simulation and AVISPA specification. Results showed that IMS-AKA+ outperforms IMS-AKA resisting to many security attacks such as Spoofing attacks, SPIT and Dos-Synchronization. In addition to this, we propose an initial approach toward securing media plane by introducing a shared key at the end of IMS-AKA+ process using Secure Real Time protocol (SRTP).

With an increasing amount of connected cars and devices having more and more sensors, the development of smart architectures and algorithms to efficiently transport data is a major concern. The selection of relays to allow users to connect to Internet is an important aspect in networks with high mobility, particularly in low-population areas having poor network coverage. Furthermore, cellular connectivity can be expensive for users. The solution proposed in this paper uses a machine learning based classification algorithm to select the best relays amongst any user based on their mobility profile. Not only this solution can be used on its own to enhance network performance without requiring a dedicated architecture, but it can be coupled with other algorithms as well to increase performance even more. Simulation results will show the proposition is able to scale up to several hundreds of users simultaneously, it improves the delivery rate of packets by up to a factor 2, it increases connectivity, generates less signaling and yields a more stable topology compared to a random selection or the use of static relays.

Unmanned Aerial Ad-hoc Networks (UAANETs) pushed up UAVs' cooperative tasks. For efficient cooperation, a fitting mobility pattern must be adopted, ensuring simple, flexible, and easy-manageable coordination. Indeed, UAANET faces an inescapable challenge due to the limited energy constraint, which significantly affects the tasks' productivity and efficiency. Different from the literature, which only studied the impact of two classical factors affecting energy consumption, namely, communication protocols and computation, we highlight the impact of the temporal and spatial correlation involved in mobility models. Indeed, we consider a transitive relationship taking place between temporal and spatial correlation and energy consumption. Those correlation forms do affect the data loss ratio that, in turn, augments energy consumption due to the increased rate of data re-transmission, route re-discovery and maintenance, etc. On this basis, we assume the temporal and spatial correlation impact on energy consumption, which has been demonstrated and analyzed through numerical simulations.

Channel assignment remains an important issue in cellular networks due to the limited frequency resource. In this paper, we study the Minimum Span Channel Assignment Problem (MS-CAP) in hexagonal cellular networks with k-band buffering restriction, where channel interference does not extend beyond k cells. The objective of MS- CAP is to minimize the max- imum frequency index used for accepting all the demands while respecting the frequency separation constraint. We start from deriving the optimal channel assignment strategies for distance- 2 clique cellular networks with homogeneous call demands, where cells are within a distance of two cells from each other. By partitioning the network into distance-2 cliques, we then further propose a time efficient algorithm GPCAA to solve the MS- CAP in large cellular networks with heterogeneous demands. Our method has been evaluated on the well-known benchmark instance Philadelphia cellular network. Numerical results show that the GPCAA algorithm outperforms its counterparts in the literature in terms of solution optimality and computing time.

In this paper, we propose Mc-Track, a new secure data oriented Cloud based vehicular tracking system. We introduced in Mc-Track an adaptive approach which consists in selection of security level according to data kinds. The architecture of the Mc-Track is composed of three levels: the vehicular network, the Cloud service, and proxies called Tracking Authorities, in charge of performing Attribute Based Encryption (ABE). We provided selective encryption and adaptive security in the Tracking Authority (TA), using the machine learning classifier k-Nearest Neighbours (k-NN). We conducted experimental study to evaluate the efficiency of the proposed k-NN classifier in selective encryption and adaptive security. So we compared the accuracy of the predictions of k-NN classifier to the accuracy of predictions using Support Vector Machine (SVM) classifier. Experimental results, has shown that the k-NN classifier is more accurate than SVM classifier.

Sensor network design involves multiple steps including network sizing, hardware and software development, analysis/simulations, and data exploitation. Often, each step is handled by a specific tool. These tools are usually not interconnected. Therefore, descriptions are required for each tool. These many descriptions are costly in terms of development time and may lead to design errors. In order to solve these issues, we propose a new framework named Modesene to speed up sensor network development for different kind of applications. Modesene is designed to provide four main processes including multi-facet modeling, network simulations, deployment, and data exploitation. Modesene provides bridges with existing sensor network tools. Thus, a description in Modesene can be automatically transformed in each tool. Modesene can perform simulations and generate codes. We validate Modesene through a smart building use-case with different possible solutions. Based on Omnet++ simulator, Modesene can estimate node lifetime, costs, packet loss, etc. Subsequently, these results can be used to drive appropriate solutions. Then, the code generator provides source codes for sensor/gateway nodes, and for data exploitation. Compared to existing frameworks, Modesene supports all design steps, i.e, from network sizing to data exploitation.

In this paper, we present a new framework for protecting privacy on online social networks based on two main concepts: cloud computing and Attribute-Based Encryption system (ABE). The cloud computing is used to store outsourcing data by a third party. However, the issues of entrusting these third-party losing control over data arise. Thus, one does not know where data are stored. In the proposed framework we propose to use a distributed multi-authority ABE scheme, which provides flexible access to private data, and only users with the right keys can have access to it. The performance evaluation is conducted by simulations with different parameters including the number of attributes, encryption time and decryption time. The obtained results and security analysis show that our solution outperforms the classical solutions in terms of security and robustness.

IEEE 802.15.4 beacon-enabled mode offers to Wireless Body Area Networks the ability to deliver their periodic traffic in a contention-free way. The Guaranteed Time Slot (GTS) mode helps to avoid collisions and provides a deterministic transmission pattern. The main disadvantage of contention-free communication is being an easy target for selective jamming. In this paper, we propose a new optimized version of GTS attacks that conserve the adversary's resources and enable prolonging the lifetime of the attack, thus, increasing its negative impact on the network. In addition, a solution that mitigates the harm of this detrimental attack is provided. To evaluate the performance of the solution, extensive network simulation is conducted. The obtained results show that the proposed solution is efficient in terms of packet delivery ratio, energy consumption and delay.

The amount of data represented by the Internet of Things (IoT) is continually growing and is expected to reach more than one third of the total amount of Internet traffic by 2020. Wireless sensors pose a great challenge because of the varying radio conditions and the limited energy and computational capabilities they have. This is why we have developed an Objective Function (OF) that uses fuzzy logic to dynamically adapt to variable environments in wireless networks. Simulation results will show that the proposed algorithm increases performance compared to other solutions by up to 15% in terms of throughput and by up to 14% in terms of Packet Delivery Ratio (PDR) without compromising energy consumption.

In this paper, we propose a multi-access edge based vehicular fog architecture. It is based on the integration of fog computing (FC) and multi-access edge computing (MEC) with the vehicular cloud. The aim of this architecture is the exploitation of unused vehicular resources and infrastructure equipment to bring the cloud at the proximity of users. The vehicular cloud serves as fog nodes that receive and process the users' requests. The control of the vehicular cloud and further processing are ensured by a MEC server. We describe the modules of the proposed architecture and the interaction between them in both cases static and mobile vehicular cloud. In order to optimize the access to the MEC server and to the conventional cloud, we suggest a fuzzy-based gateways' selection approach. It allows the selection of a set of vehicles that act as relays between the fog nodes (i.e. vehicles) and the cloud.

In this paper, we focus on the dissemination and data transportation in vehicular networks while reducing the communication cost in terms of overhead, energy and cellular traffic. We consider hybrid communication environment vehicles- to-vehicles (V2V), and Vehicles-to-Infrastructure (V2I). This environment is possible through the vehicular heterogeneous networks where vehicles are equipped with both cellular and non- cellular communication interfaces. In order to connect vehicles to Internet with a minimum solicitation of cellular infrastructure, we propose a new hybrid and programmable architecture based on Connected Dominating Sets (CDS), and Software Defined Vehicular Networks (SDVN) approaches. On the one hand, the SDVN paradigm is introduced to make the architecture flexible and programmable. On the other hand, the CDS is used to select dominating nodes that will form the the virtual backbone (VB). The VB is composed by a subset of selected parked vehicles where their role is to connect other vehicles to Internet and to reduce the end-to-end delay. Only vehicles in this backbone use the cellular link and act not only as relayed nodes but also as fog devices which are intermediate nodes between cloud server and end-users. In order to create and to manage this VB, we propose a new algorithm named BadZak based on CDS approach. The BadZak is based not only on the connectivity degree of parked vehicles, but also on their geographic position, and the zone of interest (ZI) as strategy to select dominating nodes. The ZI concept depends on the application and needs to include nodes of the VB covering a large area. We implemented two versions of BadZak algorithm one based on glutton and other optimal approaches, named BadZak-1 and BadZak-2 respectively. These algorithms are compared with well-known one name Chang Greedy algorithm in different simulation scenarios and different parameters: transmission range, nodes' density, connectivity degree, and the point of interest number.

Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes are motivating a transformation of the conventional ways of generating electricity. Globally, there is a desire to rely more on renewable energy resources (RERs) for electricity generation. RERs reduce greenhouse gas emissions and may have economic benefits, e.g., through applying demand side management with dynamic pricing so as to shift loads from fossil fuel-based generators to RERs. The electricity grid is presently evolving toward an intelligent grid, the so-called smart grid (SG). One of the major goals of the future SG is to move toward 100% electricity generation from RERs, i.e., toward a 100% renewable grid. However, the disparate, intermittent, and typically widely geographically distributed nature of RERs complicates the integration of RERs into the SG. Moreover, individual RERs have generally lower capacity than conventional fossil fuel-based plants, and these RERs are based on a wide spectrum of different technologies. In this article, we give an overview of recent efforts that aim to integrate RERs into the SG. We outline the integration of RERs into the SG along with their supporting communication networks. We also discuss ongoing projects that seek to integrate RERs into the SG around the globe. Finally, we outline future research directions on integrating RERs into the SG.

Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices such as sensors, robots, unmanned aerial vehicles (UAV) and cars, are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impact the network performance; namely data loss, large handover delay and application functionality failures. In this paper, we propose a new energy efficient and mobility aware routing protocol named EC-MRPL based on the well-known Routing Protocol for Low power and Lossy Networks (RPL standard). Unlike RPL which is designed for low resources networks with basically static devices, the proposed protocol enables to better conserve the energy and sustain the connectivity of mobile nodes. EC-MRPL integrates an enhanced mobility detection method and a novel point of attachment prediction and replacement strategy aware of the resources constraints. As such, EC-MRPL overcomes and mitigates problems caused by mobility. Obtained simulation results using Cooja/Contiki show that EC-MRPL outperforms both the RPL and the MRPL protocols in terms of handover delay, data loss rate, signaling cost and energy consumption.

In this paper, we investigate the throughput analysis for the MIMOMAX protocol, a cross-layer design based on multiple transmit and receive antennas (MIMO) capabilities. We propose an analytical model based on a three-dimensional Markov chain. This model includes two additional parameters than the conventional model: a) non-saturated traffic situation, and b) beam strength at the receiver. Each beam has its own backoff parameters that can change dynamically depending on the Signal-to-Noise-Ratio (SNR) values. We discuss a theoretical study of the interactions between the transmission probability, the beam strength and the network throughput. We show how to achieve the maximum network throughput by computing the transmission probability from the saturation point. Numerical and simulation results validate our model.

In this paper, we focus on network control functionalities and resources management in Internet of Things (IoT) using Software Defined Network (SDN) and Fog computing paradigms. We propose a new hybrid and programmable architecture to make an appropriate decision related to the control process (centralized or semi-distribute) according to different heterogeneous parameters. The centralized scenario is a classical approach of SDN. However, the semi-distributed scenario consists in introducing different level of control: principal controller, secondary controllers, and local controllers nodes. In order to adapt the architecture to network topology, we use Connected Dominating Sets (CDS) algorithm to select a relevant subset of nodes able to act as local controllers. The selection strategy is proposed using fuzzy set approach based on different network parameters: connectivity degree, link quality, and the distance in terms of hop number from the gateway. The obtained results show that the selection strategy gives good results in terms of local controller selection.

Vehicular cloud computing is a new paradigm that integrates VANET and cloud computing. It refers to the vehicles' use of outside resources: they access to the conventional cloud or cooperate with other vehicles by sharing their resources (i.e. formation of a temporary vehicular cloud). Both these two ways induce different costs such as expensive cellular links in the case of access to the conventional cloud and intermittent links in the case of vehicles' cooperation. In this paper, we focus on the proactive decision made by the vehicle (i.e. choose the access to the vehicular cloud or to the cloud computing), where this decision depends on many parameters hardly to control. In other words, our goal is to determine the best technology to access the cloud (i.e wifi/LTE). We propose to model the evolution of the vehicles' strategy as an evolutionary game. To avoid the utilization of a centralized controller, we propose a distributed Q-Learning based Vehicular Cloud Access algorithm (QL-VCA) that allows each vehicle to select the way of access independently. The simulation results show that the proposed Q-learning algorithm provides users with better throughput and lower packet loss rate and delay compared to the other scenarios (i.e. using only the LTE or the wifi).

The papers in this special section focus on the deployment of information and communication technology (ICT) in smart grids as it relates to renewable energy resource management. The successful integration of renewable energy into the power grid is expected to reduce the dependence of the grid on the fossil fuels. The potential renewable energy resources include light, wind, vibration, heat, biofuel, biomass, and tides. It is envisaged that the use of renewable energy will reduce the use of traditional energy resources, such as nuclear, oil, and gas, in the future and this trend will continue in order to reduce the emission of greenhouse gases. The abundance of these renewable distributed energy resources (DERs) at the consumer side may help to develop distributed renewable energy generation at a large scale. The DERs will likely be an integral part of the future electric grid, i.e., the smart grid [4]–[6]. A prominent feature of the smart grid is that it allows for two-way communication between the utility and its customers through ICTs.

Data transportation and routing in Internet of Things (IoT) is a challenging issue where massive data collection and gathering are predictable. The Routing Protocol for Low- power and Lossy Networks (RPL) is one of the best candidates to ensure routing in 6LoWPAN networks. However, RPL is vulnerable to a number of attacks related to exchanged control messages. In this paper, we propose a new secure routing protocol based on RPL referred to as Secure-RPL (SRPL). The main aim of SRPL is to prevent misbehaving nodes from maliciously changing control message values such as the rank of a node that may disturb a network by creating a fake topology. We introduce the concept of rank threshold along with hash chain authentication technique to deal with internal attacks like sinkhole, black hole, selective forwarding attacks etc. Simulation results show that SRPL is robust and resistant to this kind of attacks based on malicious manipulation of RPL metrics.

The introduction of machine-to-machine (M2M) communications to long term evolution and advanced (LTE-A) cellular networks can significantly degrade the performance of existing human-to-human (H2H) communications. In this paper, we consider a shared channel resource allocation in an H2H/M2M coexistence scenario. We first formulate the resource sharing problem between M2M and H2H communications as a bipartite graph (BG). In addition, we propose a power control scheme for the concurrently transmitting M2M nodes to mitigate the H2H performance degradation following a probability that is set based on a proportional integrative derivative (PID) controller reflecting the interference level. The impact of M2M radio resource allocation on the performance of conventional scheduling algorithms optimally designed for H2H communications in terms of data rate and fairness is evaluated. Simulation results are encouraging and our proposed scheme succeeds in reducing the impact of M2M communications on H2H services in terms of data rate and fairness.

In this paper we address the impact of the security cost in terms of energy consumption, processing time, and traffic load on quality of services (QoS) in Wireless Sensor Networks (WSNs). Offering security services (authentication, confidentiality, and integrity) and QoS (throughput, delay, and reliability) guarantee in WSNs is still challenging issue. The security and QoS are opposite parameters, and then security services must be dynamically and optimally adapted to QoS and network constraints (e.g. energy efficiency). Therefore, designing such solution that optimizes multiple conflicting objectives is computationally intractable. We propose a new solution based on multi-objective optimization using genetic algorithm (NSGA-II) for security, QoS, and energy efficiency in WSNs. Resource constraints as well as QoS requirements are respected through use of optimal security level based on evolutionary strategy. The obtained simulation results illustrate that the energy efficiency and the security level optimization is reached with different set of optimal security settings adapted to the QoS and the energy requirements.

Internet of Things (IoT) is a world wide network of interconnected objects. IoT capable objects will be interconnected through wired and wireless communication technologies. However, cost-effectiveness issues and accessibility to remote users make wireless communication as a feasible solution. A majority of possibilities have been proposed but many of these suffer from vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase spectrum. Thus trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. Additionally, ubiquitous objects with cognitive capabilities will be able to make intelligent decisions to achieve interference-free and on-demand services. The main goal of this paper is to discuss how CR technology can be helpful for the IoT paradigm. More precisely, in this paper, we highlight CR functionalities, specially spectrum sensing in conjunction with cloud services to serve as self-reconfigurable IoT solutions for a number of applications.

In this paper, we focus on the resource allocation based on scheduling algorithms. Unlike the existing solutions, we introduce the concept of Proportional-Integral-Derivative (PID) controller in the scheduling algorithm called PID-Scheduler in order to reach the system stability. The stability is considered in terms of throughput and delay which meet the nodes needs. In order to allocate the resources to the mobile nodes while meeting their needs in terms of throughput and delay, the proposed PID-Scheduler is not only based on the network parameters obtained in the present time, but also from the past and those which may be obtained in the future. The PID-Scheduler reacts immediately to deviation from the QoS required by the mobile. Moreover, it remembers and integrates the deviation history and corrects slowly. The prediction of the future deviation is taken into account in order to make a fast correction. The simulation results show that the proposed solution PID-Scheduler gives better results than the well-known scheduling algorithms like MaxSNR and WFO.

Device-to-device (D2D) communications, one of the major component of the evolving 5G networks, is showing promising advantages on supporting machine-to-machine (M2M) communications. In this paper, we consider the design of efficient transmission strategy selection algorithm for M2M communications underlaying cellular networks. First, a group of machine type-devices (MTDs) is matched with a particular user equipment (UE). MTDs belonging to the same group can access the same spectrum within its matched UE while the latter quality of service (QoS) is maintained. Next, we propose an efficient evolutionary game based transmission strategy selection algorithm for M2M communications using D2D mode. Specifically, MTDs switch opportunistically from a non-cooperative strategy to a cooperative strategy. Initially, we consider a non-cooperative scenario due to the selfish behavior of devices. In case the latter QoS is not satisfied, MTDs switch to a cooperative game. In a cooperative game, we propose two alternative power control schemes: a fixed mixed-strategy power control scheme where each MTD willing to play cooperatively selects the power strategy from a discrete level of powers and an adaptive mixed-strategy power control scheme. The latter technique enables to set efficiently the discrete power levels using a fuzzy logic and a proportional-integral-derivative (PID) controllers aiming to assure the desired QoS of UEs while maximizing the efficiency of M2M communications. Simulation results show that the evolutionary game based transmission strategy selection algorithm avoids significant degradation of traditional human-to-human (H2H) services in terms of throughput and fairness compared to a single non-cooperative game strategy. Besides, the adaptive mixed-strategy power control scheme outperforms the fixed mixed-strategy power control scheme by saving the battery life of MTDs while guaranteeing the latter QoS.

The aim of this work is to investigate main techniques and technologies enabling user's mobility in wearable health monitoring systems. For this, design requirements for key enabling mechanisms are pointed out, and a number of conceptual and technological recommendations are presented. The whole is schematized and presented into the form of a design framework taking in consideration patient context constraints. This work aspires to bring a further contribution for the conception and possibly the evaluation of health monitoring systems with full support of mobility offering freedom to users while enhancing their life quality.

Traditional cellular networks have been considered the most promising candidates to support machine to machine (M2M) communication mainly due to their ubiquitous coverage. Optimally designed to support human to human (H2H) communication, an innovative access to radio resources is required to accommodate M2M unique features such as the massive number of machine type devices (MTDs) as well as the limited data transmission session. In this paper, we consider a simultaneous access to the spectrum in an M2M/H2H coexistence scenario. Taking the advantage of the new device to device (D2D) communication paradigm, enabled in long term evolution- advanced (LTE-A), we propose to combine M2M and D2D owing to the MTD low transmit power and thus enabling efficiently resource sharing. First, we formulate the resource sharing problem as a maximization of the sum-rate, problem for which the optimal solution has been proved to be non deterministic polynomial time hard (NP-Hard). We next model the problem as a novel interference-aware bipartite graph to overcome the computational complexity of the optimal solution. We propose two alternative algorithms, one centralized and one semi-distributed to perform the M2M resource allocation. The computational complexity of both introduced algorithms is of polynomial complexity. Simulation results show that the semi-distributed M2M resource allocation algorithm achieves quite good performance in terms of network aggregate sum-rate with markedly lower communication overhead compared to the centralized one.

Streaming over multimedia WSN (MWSN) in urban environment is challenging due to many issues among which spectrum scarcity and high radio interference. Such conditions make it difficult to ensure high bandwidth, low transmission delay and low packet losses required for real time multimedia streaming applications. In this paper, we propose COMUS a COgnitive radio solution for MUltimedia streaming over wireless Sensor networks which uses both cognitive radio technology and clustering mechanism to enhance spectrum and energy efficiency. In COMUS we consider clustering the MWSN nodes into different clusters to ensure low energy consumption. Furthermore, based on the nodes geographical position and the actual and the forecasted channel availability, we aim to ensure stable clusters forming. The multimedia streaming from a particular source node to the sink node, require a physical channel selection to perform the corresponding routing task. Thus, in COMUS we propose an efficient channel selection to prevent frequent channel switching which considers the PU (Primary User) activity forecasts. Our simulation results show that COMUS outperforms the two existing pioneering mechanisms called SEARCH and SCEEM and this in terms of providing higher video quality (PSNR and frame rate), lower end-to-end transmission delay and lower frame loss ratio under varied spectrum conditions.

In Single-Input Single-Output (SISO) wireless multi-hop networks, when two or more stations attempt to transmit at the same time in the same vicinity area, a collision will occur. In this case, the collided packets are discarded and then retransmitted later. In order to deal with collision and to maximize the system's throughput, we propose a new cross-layer design based on multiple transmit and receive antennas (MIMO) capabilities, called MIMOMAX. Transmit filter, receive filter and channel state information (CSI) are exchanged between the PHY and MAC layers to optimize the scheduling of simultaneous transmissions on the same channel. The performance evaluation is conducted using OPNET simulation tool. The results obtained through extensive simulations are compared with those of IEEE 802.11b/g/n single-hop and multi-hop wireless networks. We show that MIMOMAX is less efficient (low throughput and long delays) for single-hop networks. This is due to an under-utilization of channel Degrees of Freedom (DoFs). However, it always performs better (high throughput and low packet delay) than the 802.11b/g/n wireless multi-hop networks: up to 30% and 87% as improvements in network throughput compared to the IEEE 802.11n and IEEE 802.11g networks respectively. In addition, we illustrate that MIMOAX is robust and resists to the early network saturation problem.

Multichannel operation has been suggested for vehicular networks in order to support simultaneously safety and non-safety applications. Without awareness of lower layers, safety messages may be generated out of the control channel interval that leads to synchronous contention at the beginning of control channel interval (CCHI). The jamming attacks at this period are difficult to detect as they can be confused with collisions in contention. In this work, we proposed a detection method that can distinguish between packet lost due to normal collisions and due to jamming attacks. An analytical model is also introduced and validated in order to study the efficiency and the precision of the proposed detection method. Obtained numerical results from analytical model and network simulation show that our detection method has a high detection probability under reactive jamming attacks.

Abstract In this paper, we focus on the coexistence between security and quality of services (QoS) guarantee in wireless sensor networks. The idea is to integrate the security services: authentication, integrity, confidentiality, and non‐repudiation with QoS guarantee. In literature, the major proposed solutions consider QoS and security separately. In addition, it has been shown that it is difficult to offer the end‐users multiple levels of security while offering a high level of QoS, because security and QoS are opposite parameters. To tackle this main issue, we propose a new model based on proportional integral derivative controller in order to dynamically select the security level adapted to QoS requirements while considering the energy consumption. The stability parameters in terms of security level and QoS are taken into account by using the principle of proportional integral derivative which is based on the past parameters (history), the current system state (present), and the predictable system change (future). In order to evaluate the proposed model, we consider the case of routing protocol, and we introduce this model into the well‐known ad‐hoc on‐demand distance vector (AODV) protocol called QwS‐AODV protocol. Unlike the classical secure AODV, the QwS‐AODV protocol aims at adding or removing security services dynamically according to the network performance. Moreover, the validation and the evaluation of the proposed solution are carried out by simulation. The performance evaluation is presented with different network parameters such as throughput, delay, energy consumption, and security services. In addition, the comparison with a classical secure AODV is carried out, and the results obtained with the simulation illustrate that the security is always ensured with lower costs in terms of QoS and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.

Many applications of Wireless Sensor Networks (WSN) require to achieve the positions of the sensor nodes within a given time bound. In this paper we study the relative and physical localizability of WSN in a given time bound. We propose a new distributed and time bounded localization algorithm based on Multidimensional Scaling (MDS) method in WSN called D-MDS localization time algorithm. We compare the proposed algorithm to the existing algorithm based on the well-known Trilateration method. The simulation results show that the proposed algorithm outperforms the existing approach based on Trilateration method in terms of the number of localized nodes in the network and the number of anchors required to physically localize the sensors. The D-MDS localization time algorithm localizes a large number of nodes for a low node degree in a time bound. Moreover it is able to physically localize the network with a low number of anchors compared with the algorithm based on Trilateration method.

In this work, we propose a new Predictive-based Mobile Target Tracking Algorithm for Wireless Multimedia Sensor Networks called PMT2. Resource management being a critical feature of this kind of networks, the main aim of PMT2is to handle the trade-off between the accuracy of the tracking and the energy conservation. Prediction approach seems to be the best candidate to reach this objective. For this purpose, we introduce an enhanced version of the Extended Kalman Filter combined with a change detection mechanism named CuSum for Cumulative Summary. We also propose a deployment strategy to improve the efficiency of the tracking algorithm. Using simulations, we show the performances of the proposed coupled mechanism in the trajectory prediction and in the reactivity to abrupt direction changes. Moreover, we perform a comparative study between PMT2and existing works: 1) BASIC where all the Cameras Sensors are always in active mode; 2) OCNS for Optimal Camera Node Selection, a cluster-based solution with a probabilistic sensor selection; 3) PTA, another predictive solution based on standard Kalman Filter. The obtained results illustrate that PMT2improves the quality of tracking by up to 35% compared to existing works, while reducing energy consumption by up to 55%.

Nowadays, the channel selection is a challenging task in cognitive radio because of the high radio activity and the preemptive priority of the licensed user, called primary user (PU). In this paper, we propose a DIStributed channel Selection mechanism for efficient content dissemination in COgnitive RaDio ad-hoc networks (DISCORD). DISCORD selects the most appropriate channel for content dissemination based on the PU channel occupancy and the importance of cognitive radio neighbors in the network. Indeed, a sender peer in DISCORD forecasts the channel primary user activity by the mean of the actual and statistical estimation of the channels occupancy and selects the most stable one. Moreover, it makes use of a new Social Networks Analysis (SNA) inspired metric to select the appropriate neighbors for high content dissemination in the network. The simulation results using NS2 shows that Discord presents highest performance in terms of interferences, PU priority respect and content delivery ratio in multi-hop CRNs comparing to four related protocols.

ABSTRACT In this paper, we propose a distributed and dynamic public key infrastructure for vehicular ad hoc networks. We aim to achieve the fundamental security requirements, particularly the authentication, the confidentiality, and a reliable vehicle‐to‐vehicle data exchange. To make the certification authority (CA) reachable by all vehicles, we distribute its role among a set of dynamically elected vehicles. The election of dynamic CAs is based on a clustering algorithm where the cluster heads will be CAs in their clusters. The cluster heads are elected following two criteria: security and mobility. Due to the important role of the CA in each cluster and to protect it from DOS attacks, we introduce a VANETs dynamic demilitarized zone for vehicular ad hoc networks. Its role is to handle the certification requests sent to the CA from unknown vehicles, and hence, it avoids compromising it. Additionally, we detail the certificates management in the proposed public key infrastructure, and we propose a mechanism to provide anonymous vehicle‐to‐vehicle communications using pseudonyms. To study the feasibility of our distributed architecture and particularly the clustering algorithm, we propose a probabilistic model considering the speed of vehicles and taking into account the safety distance between vehicles. We carried out a set of simulations to evaluate the performance of the proposed clustering algorithm in both urban and highway environments. Hence, we study the effects of the transmission range, the speed of vehicles, and the number of trusted vehicles in the network on the stability and the efficiency of the overall proposed architecture. We also study some delays characterizing the certificates management. Our simulation results show that the security of the proposed architecture closely depends on the number of trusted vehicles in the network, and the stability depends on the mobility of vehicles on the road and on the total number of trusted vehicles. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we address the issue of the presence of malicious and selfish nodes in Vehicular Ad Hoc Networks (VANETs). Malicious nodes spread false and forged messages, while selfish nodes only cooperate for their own interest. To deal with this, we propose DTM2, a Distributed Trust Model inspired by Spence's Job Market model from Economics. In our model, a sender node transmits a signal with its message. This signal represents a guarantee of the truthfulness of the message for the potential receivers. In order to use the signal, the sender node has to pay a cost, which depends on the value of the signal and its own behavior. Therefore, the worse the behavior of the sender node, the more expensive the signal cost. This model deters the sender nodes from acting as malicious nodes. Similarly, cooperation of the sender nodes is rewarded proportionally to the signal's value. We validated DTM2via extensive simulation in an urban scenario. We show that our approach is able to detect and evict gradually all malicious nodes in a network composed of 25%, and 50% of them. Moreover, our solution greatly decreases the ratio of corrupted and false data sent through a network to levels as low as 0%, and it increases the participation ratio of selfish nodes by 20%.

In this work, we propose a new framework able to ensure the Quality-of-Services (QoS) with different security levels in Wireless Sensor Networks (WSNs). We propose a new model based on the PID (Proportional Integral Derivative) controller in order to dynamically select the security level adapted to QoS requirement while considering the energy consumption. The stability parameters in terms of security level and QoS are taken into account by using the principle of PID which is based on the past variation parameters (history), the current system state (present) and predictable system change (future). We introduce the proposed model in AODV routing protocol called QwS-AODV protocol. Unlike the classical Secure AODV, QwS-AODV protocol aims at adding or removing security services dynamically according to the network performance. Moreover, the validation and the evaluation of the proposed solution is carried out by the simulation. The performance evaluation is presented with different network parameters such as: throughput, delay, energy consumption and security services (authentication, confidentiality, Integrity). In addition, the comparison with a classical Secure AODV is done, and the results obtained from the simulation illustrate that the security is always ensured with lower costs in terms of QoS and energy consumption.

In this paper we propose a trust model based on a Markov chain in order to formalize the trust metric variation and its stability in the context of Vehicular Ad hoc Networks (VANETs). The proposed model takes into account not only the dynamic trust metric variation according to the vehicles behaviors, but also the constraints related to the monitoring process. In our model each vehicle can act as monitor and update the trust metric of its neighbors according to their behavior in the network. In addition, our model can be customized through different parameters like the trust interval and the number of transitions needed to reach the highest trust level. This flexibility enables to adapt the model according to the application context. The performance evaluation of the proposed model is presented with different parameters and two types of disruptive vehicles are taken into account: malicious and selfish. The obtained results show the resistance, the robustness and the incentive of the proposed model against the fluctuations of the vehicles behaviors.

In this paper we propose a new framework able to ensure security, multimedia quality and energy efficiency in Multimedia Wireless Sensor Networks (MWSNs). The energy is an important and limited resource, which has a direct impact on the lifetime of nodes in MWSNs. In addition, the characteristics of MWSNs like the limited bandwidth and non-deterministic channel access have a significant impact on the QoS of multimedia traffic. We propose the Efficient Dynamic Selective Encryption Framework (EDES) in order to reduce the energy consumption and increase the QoS while ensuring a secure multimedia traffic. EDES proposes three security levels (high, medium and low) and the selection of each level depends on the energy and QoS parameters. Moreover, the cross-layer approach is selected for EDES to take into account the different parameters at physical, MAC and upper layers. The capacity metric is proposed to evaluate the possibility to increase or decrease the security level. The simulation results illustrate the importance of the security level adaptation according to the QoS and the energy parameters. EDES increases the lifetime duration of nodes by almost 40% compared to static encryption.

Mobile Ad-hoc Networks (MANETs) are based on a fundamental aspect, which is the cooperative parameter. This parameter may compromise the networks. The selfish misbehaving nodes can seriously affect the network performance. Moreover, the existing mechanisms based on the monitoring process to detect the misbehaving nodes are not efficient and suffer from an important false alarm rate. These weaknesses are mainly due to the interferences and the costs of the monitoring process. In MANET based on SISO (Single-Input Single-Output) technology, the interferences at the monitor node compromise the observation and the accuracy of the cooperation report. That is why in this paper, we focus on the MIMO (Multi-Input and Multi-Output) technology to overcome these drawbacks and to significantly improve the monitoring process. We propose a new MAC protocol called MIMODog-SPACE-MAC based on the well-known SPACE-MAC protocol. It allows the monitor node to avoid the collision during the monitoring process by adjusting the antennas weights in order to nullify the signal coming from other nodes than the monitored one. Therefore, the proposed solution contributes to significantly enhance the accuracy of the monitoring process. We show that for a MIMO network with randomly located nodes n, each equipped with M antennas, the achievable number of monitor nodes is Θ(M/√(n ln n)). Indeed, theoretical results show that by using MIMODog-SPACE-MAC, the network can have a constant improvement M on an asymptotic number of monitor nodes compared to SISO 802.11 DCF MAC.

We plan to design and implement software defined intelligent antenna switching capability to wireless sensor nodes based on link quality metric, such as Received Signal Strength Indicator (RSSI). In this paper, as a first step, we discuss the preliminary results of our newly designed radio module (Inverted-F Antenna) for 2.4 GHz bandwidth wireless sensor networks. In this perspective, we consider the TelosB motes and compare the performance of the built-in TelosB antenna with our proposed antenna. Experimental results confirm the effectiveness of the proposed radio module i.e., 5% to 12% gain over the built-in radio module of the TelosB motes.

In this paper we propose an analytical model to detect and remove malicious nodes while taking into account MAC IEEE 802.15.4 beacon-enabled technology. The proposed solution called muDog enables to monitor nodes activities with a minimal energy consumption in order to detect the suspicious behavior particularly the non-cooperative nodes in the routing process. Moreover, we analyze the cost of the monitoring mechanism in terms of energy consumption and the quality of detection by the evaluation of the monitor's observation. The impact of nodes density, packets' size, network traffic load: saturated/unsaturated cases and distance between monitor and monitored nodes are taken into account in our evaluation. The obtained results illustrate that muDog is more efficient than Watchdog whatever the parameter is.

In this paper, we propose a newly distributed protocol called ADCD to manage information harvesting, and distribution in Vehicular Sensor Networks (VSN). The concept of ADCD is based on the characterization of sensed information (i.e. its importance, location and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages for which they have no interests. Thanks to this adaptive broadcasting strategy, ADCD limits the generated overhead avoiding network congestions as well as long latency to deliver the harvested information, which are the main limitations of other existing protocols. Moreover, it is designed to be flexible regarding the use of roadside units or not, which is not the case in other schemes in the literature. To reach its objectives, ADCD operations are divided into three steps: (i) classification of data and the identification of their target area of diffusion, (ii) data-centric election of the set of broadcasters to avoid broadcasting redundancy, and (iii) iterative process for data dispatching in a targeted area. Performance evaluation shows that the ADCD protocol allows for mitigating the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared to the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density.

The diversity of research topics in Wireless Sensor Networks (WSNs) is attracting more and more researchers from different fields. The common point of all proposed solutions and protocols for WSNs is the evaluation usually done with network simulators. In this paper, we focus on the results relevance of wireless sensor network simulators in different scenarios based on indoor and outdoor environments. We propose a comparative study between 3 usual simulators (NS2, OPNET and QualNet) while using as reference a real testbed based on recent Imote2 sensors. The simulators give different results even in similar environments. NS2 and Qualnet give results close to those of the experimentation in the case of an indoor environment, but in the outdoor environment Opnet gives results closer to the reality. In addition, the impact of different MAC protocols (B-MAC and TKN15.4 MAC) which observe or do not observe the IEEE 802.15.4 standard is illustrated by real experimentations. They show that TKN15.4 protocol gives a better throughput than B-MAC.

In this paper, we propose a new scheduler able to extend the wireless coverage by using an incentive approach for potential mobile relaying nodes. Indeed, the cost of cooperation can be expensive in terms of QoS and energy consumption which do not motivate the nodes to cooperate. Our incentive approach rewards the cooperative nodes. The percentage of cooperation is considered in the QoS management in order to incite the border nodes to cooperate and then to extend the wireless area. Moreover, the monitoring mechanism is proposed to correctly evaluate the cooperation rate of each node. The results show that not only the proposed solution allows the border nodes to cooperate without the negative impact but also enhance the QoS parameters.

ABSTRACT In this paper, we propose a newly distributed protocol called Advanced Diffusion of Classified Data (ADCD) to manage information harvesting and distribution in vehicular sensor networks. ADCD aims at reducing the generated overhead, avoiding network congestions as well as long latency to deliver the harvested information. The concept of ADCD is based on the characterization of sensed information (i.e., based on its importance, location, and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages in which they have no interest. Also, we propose in this paper a new probabilistic model for ADCD based on Markov chain. This one aims to optimally tune the parameters of ADCD, such as the optimal number of broadcaster nodes. The analytical and simulation results based on different metrics, such as the overhead, the delivery ratio, the probability of a complete transmission, and the minimal number of hops, are presented. These results illustrate that ADCD allows mitigating the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared with the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we propose a comparative study between results generated by a real 802.11 testbed in different outdoor and indoor environments and 3 usual network simulators (NS2, QualNet and OPNET). The goal of this study is to evaluate the relevance of the low layers recently implemented in these simulators. The motivation of this paper is to provide a guide to researchers to choose and parameterize a simulator according to a selected context. The study shows that the simulation results can be rather close to the experimental results. However, they are very dependent on the tuning of the physical layer parameters and the selected propagation models.

Abstract In this paper, we propose a new architecture based on an efficient trust model and secure distributed clustering algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of certificates in each cluster. We use the combination of a fully self‐organized security for trust models like pretty good privacy (PGP) adapted toad hoctechnology and the clustering algorithm which is based on the use of trust and mobility metrics, in order to select the clusterhead and to establish a public key infrastructure (PKI) in each cluster for authentication and exchange of data. Furthermore, we present a new approach: the dynamic demilitarized zone (DDMZ) to protect the CA in each cluster. The principal idea ofDDMZconsists in selecting the dispensable nodes, also called registration authorities (RAs); these nodes must be confident and located at one‐hope from theCA. Their roles are to receive, filter and treat the requests from any unknown node to theCA. With this approach, we can avoid the single point of failure in each cluster. Moreover, we propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of theDDMZagainst different attacks. In addition, we evaluate the performance of the proposedSDCAand we estimate the robustness and the availability ofDDMZthrough the simulations. The effects of direct connectivity and transmission range on the stability and security of the network are analyzed. The simulation's results confirm that the proposed architecture is scalable, secure, and more resistant against attacks. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The intrusion detection system (IDS) for mobilead hocnetworks (MANET) consists in monitoring the nodes' behavior, in order to detect the malicious activity of nodes. Many existing solutions deal with the problem at each layer separately. But new kinds of misbehavior attacks are cross‐layer attacks. And such smart misbehaviors cannot be detected at the level of one layer. In this paper, we propose a new cross‐layer approach based on physical, MAC, and routing layers for a monitoring mechanism. A new analytical model is proposed to illustrate the parameters' effect on these different layers. The impact of the signal to noise ratio (SNR) and the distance between monitor and monitored nodes are clearly introduced. Moreover, the difference between the carrier sense, the interference range, and the transmission range is taken into account in our model. The proposed model improves the evaluation of the nodes' cooperation and reduces the risk of having any false positive rate. The analytical study and simulation results illustrate our purpose. In addition, with the simulations' results, we illustrate the impact of the distance between monitor and monitored nodes on the monitoring mechanism. Finally, we show that our cross‐layer mechanism has a lower false positive rate than the classical Watchdog mechanism in different network's parameters such as the nodes' density, the speed mobility, and the different traffic loads. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we focus on the medium access control (MAC), particularly the IEEE 802.11 and we deal with some hidden vulnerabilities based on the control packets CTS (clear to send) and ACK (acknowledgment). Through these vulnerabilities, we show two new smart attacks which were not dealt by the solutions proposed recently like the attack based on the RTS (request to send) packet vulnerability. The malicious node can exploit these vulnerabilities on the MAC protocol, in order to corrupt the monitoring and routing processes. Furthermore, we demonstrate the attacks through algorithms and we show how vulnerabilities can be exploited and how these attacks can be implemented by the attacker. The impact of these attacks is presented through simulation and implementation. Simulation and exprimental results show the impact of the attacks on the network. In addition, the experimental results demonstrate the feasibility of these real attacks and their exploitation. These experimentations allow us to confirm the simulation's results. Furthermore, in order to prevent these attacks, the solutions based on control packet authentication are presented. We propose two kinds of the solution one is cryptography independent and the other one is cryptography dependent. The evaluation and analysis of these solutions are investigated by analytic and simulations analysis. The simulations' results of the proposed solution show that the attacks are prevented and the negative impacts are significantly reduced. In addition, the security cost of the proposed solutions are investigated. Hence, the security costs are insignificant in comparison with the negative impact of these attacks. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we propose an anonymous protocol to secure nodes which have important roles in the network. We focus in the clustering approach to secure the mobile ad hoc networks (MANETs). In each cluster, a confident node is selected to ensure the certification authority (CA) roles; however, the cluster security depends in the security of the CA node. Therefore, we present an anonymous dynamic demilitarized zone (ADDMZ) to protect the CA node identity and to avoid the single point of failure in the cluster. ADDMZ is formed by a set of confident nodes which have a high trust level between them and their goal is to filter the communication between the cluster member node and the CA node. Moreover, we draw one's inspiration from military defence mechanisms such as: camouflage and identity change mechanisms. We present protocol to realize these mechanisms by using the identity based cryptographic from bilinear maps. The security analysis is proposed to discuss the proposed protocols.

Although IEEE 802.11 provides several transmission rates, a suitable rate adaptation taking into account the relative fairness among all competitive stations, according to the underlying channel quality remains a challenge in Mobile Ad hoc Networks (MANETs). The absence of any fixed infrastructure and any centralized control makes the existing solutions for WLANs like CARA (collision-aware rate adaptation) [4] not appropriate for MANETs. In this paper, we propose a new analytical model with a suitable approach to ensure a relative fairness among all competitive nodes of a particular channel. Our model deals with the channel quality while respecting the nodes, based on transmission successes and failures in a mobility context. Finally, each node calculates its own probability to access the channel in a distributed manner. We evaluate the performance of our scheme with others in the context of MANET via extensive and detailed simulations. The performance differentials are analysed using varying network load and transmission range. The simulation results illustrate that our proposed approach ensures a better tradeoff between fairness and throughput.

To avoid the single point of failure for the certificate authority (CA) in MANET, a decentralized solution is proposed where nodes are grouped into different clusters. Each cluster should contain at least two confident nodes. One is known as CA and the another as register authority RA. The Dynamic Demilitarized Zone (DDMZ) is proposed as a solution for protecting the CA node against potential attacks. It is formed from one or more RA node. The problems of such a model are: (1) Clusters with one confident node, CA, cannot be created and thus clusters' sizes are increased which negatively affect clusters' services and stability. (2) Clusters with high density of RA can cause channel collision at the CA. (3) Clusters' lifetime are reduced since RA monitors are always launched (i.e., resource consumption). In this paper, we propose a model based on mechanism design that will allow clusters with single trusted node (CA) to be created. Our mechanism will motivate nodes that does not belong to the confident community to participate by giving them incentives in the form of trust, which can be used for cluster's services. To achieve this goal, a RA selection algorithm is proposed that selects nodes based on a predefined selection criteria function. Finally, empirical results are provided to support our solutions.

The monitoring process consists in evaluating the behaviour of nodes in networks in order to detect if the monitored nodes well-behave or misbehave. Many existing solutions deal the problem at each layer separately. Actually new kinds of misbehaviour attacks are cross-layer. So, such smart misbehaviours cannot be detected at the level of one layer. In this paper, we propose a new cross-layer approach based on physical, MAC and routing layers for a monitoring mechanism. An analytical model is proposed to illustrate the parameters' effect on these different layers. The impact of the signal to noise rate (SNR), the distance between monitor and monitored nodes are clearly introduced. Moreover, the difference between the carrier sense, the interference and the transmission ranges is taken into account in our model. The simulations' results show the effectiveness of the proposed analytical model, we reach until 90% of observation's correction in some cases.

Providing a security solution for mobile ad-hoc networks (MANETs) is not an easy task. This is due to the unique characteristics of MANETs, such as the lack of a pre- existent infrastructure, the dynamic topology of the network, the non-existence of a control authority and the constraints of device resources. In this paper, we introduce the monitoring and cluster manager modules to improve our distributed hierarchical architecture. Moreover, we study the concept of dynamic demilitarized zone (DDMZ) defined in our hierarchical architecture to avoid a single point of failure in MANETs. The DDMZ is formed by the dispensable nodes which belong to the confident community. The confident community is formed by sets of confident nodes which have high trust levels and collaborate with each other to ensure secure services. We propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of DDMZ against different attacks. Furthermore, we estimate the robustness and the availability of DDMZ and we also analyze the effects of direct connectivity and transmission range on the stability and security of the network.