The increasing adoption of electric vehicles (EVs) presents new challenges in optimizing energy consumption, charging efficiency, and travel time. Dynamic Wireless Charging Lanes (DWCLs) offer a promising solution by allowing EVs to charge while driving, reducing dependency on static charging stations and minimizing downtime. However, optimizing the decision-making process for when and at what speed a vehicle should enter and exit a DWCL while maintaining an efficient driving strategy remains a complex problem. This work proposes a Deep Q-Network (DQN)-based reinforcement learning (RL) approach to dynamically control lane-switching and speed adjustments in a multi-lane road environment. The RL agent is designed to balance energy efficiency, travel time minimization, and charging maximization, while also considering real-world constraints such as road topology, accurate energy consumption modeling, and vehicle alignment with charging coils. By leveraging CARLA as a high-fidelity simulation environment, we implement and train a policy that autonomously decides lane-switching, speed adaptation, and charging strategies. The proposed framework not only improves the charging efficiency of EVs but also ensures smooth lane transitions while maintaining energy-optimal driving behavior. Experimental results show that the RL agent effectively learns the optimal use of DWCL, minimizing unnecessary lane switches and ensuring a highly efficient, and real-time deployable EV management system.

The rise of Connected Electric Vehicles (CEVs) is driving demand for intelligent, data-driven solutions in mobility and energy management. However, using data from each CEV independently fails to generalize to unseen scenarios, while sharing data with a centralized system raises privacy concerns. Federated Learning (FL) enables collaborative model training across distributed EV systems while preserving data privacy. Nevertheless, standard FL algorithms face challenges due to client heterogeneity, including non-IID (non-Independent and non-Identically Distributed) data, varying computational capabilities, and unstable communication links. We propose Match-Estimate (ME), a robust, flexible aggregation method designed for heterogeneous FL. As a drop-in replacement for strategies like Federated Averaging (FedAvg), ME extends our prior method, FedEstimate, to improve performance and training stability across diverse clients. MatchEstimate is a two-step server-side aggregator: (i) it aligns neurons across client models via layer-wise bipartite matching, and (ii) it trains a small regressor to map proxy shard-level updates to each client’s full update, which are then size-weighted and aggregated. This plug-in design mitigates non-IID drift and parameter misalignment. In a CEVs case study, ME significantly enhances their accuracy under realistic conditions, advancing privacy-preserving, decentralized intelligence for next-gen mobility.

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Cyber-attack detection is crucial for securing Industrial Internet of Things (IIoT) systems. This study introduces advanced deep learning methodologies to identify potential cyber-attacks effectively in IIoT devices. Three novel stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms. These architectures aggregate and enhance the results of individual deep learning models. Specifically, StackMean computes average predicted class probabilities, StackMax selects maximum predicted class probabilities for more aggressive predictions, and StackRF leverages a random forest to aggregate base models. Theoretical analysis suggests that the proposed stacked deep learning model can boost detection accuracy compared to standalone single deep learning models. Moreover, these stacked models offer increased robustness against adversarial attacks by reducing reliance on specific neural network structures. Additionally, the synthetic minority oversampling technique (SMOTE) algorithm is integrated to address class imbalance challenges in the training dataset. Performance validation is conducted using three publicly available datasets. The detection performance is evaluated using five statistical scores. The results consistently indicate the superiority of the proposed stacked deep learning models over existing techniques. The effectiveness of the SMOTE algorithm is demonstrated through its ability to expand decision regions and minimize false negative signals during attack predictions. In addition, a statistical test is employed to compare the accuracy of individual models with the stacked models, demonstrating that the stacked models exhibit improved accuracy. By combining cutting-edge stacked deep learning architectures with strategic data augmentation techniques, this research significantly contributes to the robustness of cyber-attack detection within IIoT systems.

Combining Autonomous Aerial Vehicles (AAVs) with public transportation systems offers a novel approach to enhancing urban last-mile delivery efficiency. However, current research lacks comprehensive solutions for optimizing AAV path planning that can adapt to real-time changes in public transportation schedules while avoiding dynamically changing high-interference zones, ensuring consistent drone connectivity, and accounting for limited drone battery life. This paper addresses these gaps by optimizing and balancing multiple objectives, such as ensuring early arrival, minimizing energy consumption, and reducing signal interference. To achieve these goals, we propose a Q-learning-based algorithm for real-time path optimization, ensuring path adaptability to random changes in interference zones and public transportation schedules, and compare its performance against multi-objective A* algorithm variants. Results show that Q-learning achieves superior early arrival times, energy efficiency, and reduced interference, with a nearly 100% success rate in package deliveries, compared to 60-80% for A*. These findings demonstrate the robustness and reliability of Q-learning in AAV-Aided Public Transportation systems, making it a valuable solution for real-world urban delivery challenges.

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

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

The rapid advancement in the Internet of Vehicles environments brings forth a significant increase in cybersecurity threats, with vehicles becoming prime targets for cyber-attacks. This paper presents a comprehensive study on the security vulnerabilities of electronic control units (ECUs), sensors, and communication links within the IoV framework. Utilizing the newly released CICIoV2024 dataset, we propose a deep neural network (DNN) model tailored for detecting a wide range of cyber-attacks, including DoS and spoofing attacks. Our model demonstrates remarkable performance in classifying benign and malicious activities, showing high accuracy, precision, recall, and F1-score across various attack scenarios. By applying rigorous preprocessing techniques and innovative model architecture, our research contributes significantly to the field of automotive cybersecurity, offering a robust solution for safeguarding the IoV ecosystem against emerging cyber threats.

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

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

Electric vehicles (EVs) have emerged as a promising solution for environmental preservation. However, a major hurdle remains: range anxiety caused by limited driving range and inaccurate energy consumption estimates. This is because energy use in EVs varies significantly based on environmental factors (like temperature) and driving conditions (like traffic congestion). To address this challenge, this paper leverages the recent trend of connected electric vehicles (CEVs). These vehicles are equipped with sensors and onboard computers that gather real-time data on various aspects that affect energy consumption. The paper then introduces a Tri-component data-driven machine-learning model that utilizes this data from connected EVs. The model focuses on optimizing route planning for energy efficiency by predicting energy consumption along different road segments. It considers various external factors like temperature, traffic congestion, and road incline to predict three key elements: velocity (how fast the vehicle will go), traction power (energy needed to move the vehicle), and auxiliary power (energy used by features like A/C and onboard computer). Tested in real-world scenarios, the model demonstrates a significant reduction in energy consumption estimation errors, with a remarkable 2.38% error rate for battery state-of-charge (SoC) and 0.52 kWh for energy consumption.

Summary Data aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

Current flow-based Network Intrusion Detection Systems (NIDSs) have the drawback of detecting attacks only once the flow has ended, resulting in potential delays in attack detection and increasing the risk of damage due to the infiltration of a greater number of malicious packets. Moreover, the delay provides attackers with an extended period of presence within the network, enabling them to execute subsequent attacks. To overcome this drawback, this work addresses the issue of early flow classification in NIDSs that incorporates a Deep Learning (DL) model. This model leverages Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), coupled with attention mechanisms. This strategic combination allows the system to harness the inherent sequential nature of packets within network flows, enhancing the efficiency of early flow classification. We conducted experiments on two up-to-date network intrusion datasets, namely CIC-IDS2017 and 5G-NIDD. Our findings demonstrate the effectiveness and accuracy of the proposed NIDS in classifying network flows. Additionally, our approach showcases its efficacy by promptly identifying and detecting attacks in their early stages without the need for flow termination. This results in a reduction in both the number of initial packets required for classification and the time needed for detection.

The increasing demand for fast and energy-efficient parcel deliveries driven by the growth of e-commerce has made drones an attractive option for urban freight. However, considering drones’ energy usage throughout the entire delivery process is essential when assessing their suitability. This research introduces an energy consumption model encompassing all flight stages—takeoff, flight, landing, hovering—simulating last-mile energy consumption within drone delivery-based systems. Additionally, it conducts a novel comparison between a system relying only on drones (Drone-only system) and a system combining drones with public transportation (Drone-APT system). The comparison considers factors such as consumer distribution, warehouse locations, and drone battery characteristics. The results, based on the real 3D structure of Barcelona, demonstrate that the Drone-APT system maintains consistent power consumption across different consumer distributions and warehouse locations. Conversely, the Drone-only system ensures consistent delivery times in these same scenarios. Furthermore, the Drone-APT improves energy efficiency, especially when direct drone delivery is limited by battery life. However, this efficiency improvement leads to increased delivery time due to the unpredictable timing of public transport vehicles. Striking a balance between drone energy consumption and delivery time is crucial, warranting further research to identify the most sustainable solution for last-mile delivery. This study offers valuable insights into the advantages and trade-offs associated with integrating drones and public transportation, laying the groundwork for future optimization efforts. Future work could incorporate intelligent technologies like machine learning to effectively address these trade-offs by dynamically optimizing drone paths based on energy efficiency and delivery time.

As part of the 5G, Connected and Automated Vehicles (CAVs) will benefit from Network Slicing (NS) in several tailored 5G- Vehicle-to-Everything (V2X) services running on the same physical infrastructure. However, the use of 5G- NS may also increase the risk of cyber-attacks that could compromise 5G-V2X network slices (5G-V2X-NSs) and cause significant harm to CAV's passengers. This risk is particularly high at cross-borders, where CAVs move from their Home Mobile Network Operator (H-MNO) to a Visited MNO (V-MNO), with similar 5G-V2X-NSs in place. Therefore, deploying security services to neutralize 5G- V2X NS threats in this scenario is mandatory. However, if H-MNO and V-MNO act independently, deploying these security services could be inefficient and may result in increased memory, processing, and network resource consumption. Thus, MNOs should collaborate to orchestrate their security services to neutralize 5G-V2X NS attacks and optimize their costs efficiently. In this context, this paper proposes a novel approach to enhance the security of 5G-V2X NS at cross-borders using Reinforcement Learning (RL) based security orchestration. Specifically, we trained and deployed an RL agent interacting with both H-MNO and V-MNO. The RL agent efficiently deploys security services to effectively remove threats, optimize resource utilization, and minimize the impact on 5G-V2X-NSs. The performance results show that the RL-based security orchestration neutralizes threats with an average success rate of almost 100%. Additionally, resource consumption is minimal at less than 8 %, and the acceptable impact on 5G- V2X - NSs is negligible, averaging less than 12 %.

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

As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs.

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs' privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

Drone-based delivery has gained popularity in recent years, and many different delivery systems and schemes have been proposed. One of the most promising scheme concepts is based on the collaboration between a drone and a public transportation network to expand the delivery range while conserving drone battery energy and reducing delivery costs. Path planning is the main problem with this design, as the public transportation network is stochastic and time-dependent. In this paper, an inspection time-adapted early arrival path problem is formulated, which seeks the path for a drone that ensures: (i) reaching the customer as soon as possible, (ii) adapting to random fluctuations in public transportation schedules, and (iii) taking into account the battery life of the drone. To achieve these requirements, a Q-learning-based planning method is proposed. The simulation results validate the effectiveness and feasibility of Q-learning on the planning path for parcel deliveries: at any departure instant, the arrival of the drones at the customer's location was guaranteed, i.e., the resulting path is 100% reliable. In addition, the convergence of the Q-Learning algorithm was reached after only 1000 learning epochs. Furthermore, the experimental results show that the Q-Learning solution can achieve a lower early arrival time and lower power consumption compared to a random algorithm.

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

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

Cooperative collision avoidance between inland waterway ships is among the envisioned services on the Internet of Ships. Such a service aims to support safe navigation while optimizing ships' trajectories. However, to deploy it, timely and accurate prediction of ships' positioning with real-time reactions is needed to anticipate collisions. In such a context, ships positions are usually predicted using advanced Machine Learning (ML) techniques. Traditionally, ML schemes require that the data be processed in a centralized way, e.g., a cloud data center managed by a third party. However, these schemes are not suitable for the collisions avoidance service due to the inaccessibility of ships' positioning data by this third party, and allowing connected ships to get access to sensitive information. Therefore, in this paper, we design a new cooperative collision avoidance system for inland ships, while ensuring data security and privacy. Our system is based on deep federated learning to collaboratively build a model of ship positioning prediction, while avoiding sharing their private data. In addition, it is deployed at multi-access edge computing (MEC) level to provide low-latency communication to ensure fast responses during collision detection. Furthermore, it relies on Blockchain and smart contracts to ensure trust and valid communications between ships and MEC nodes. We evaluate the proposed system using a generated dataset representing ships mobility in France. The results, which demonstrate the accuracy of our prediction model, prove the effectiveness of our cooperative collision avoidance system in ensuring timely and reliable communications and avoiding collisions between ships.

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches.

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes; each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way, but also to enable low latency intrusion detection. Moreover, we build our FL-based IDS on top of well-know CIC-IDS2018 dataset, that includes the main network attacks. Noting that, we first perform a feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing the network resource consumption.

New Radio Vehicle-to-Everything (NR-V2X) distributed communication mode utilizes a semi-persistent scheduling (SPS) scheme, in which a reserved radio resource is used for a certain duration. However, attackers can exploit the predictability of SPS’s resource assignment to cause packet dropping, by selecting already reserved resources. In this paper, we first develop a feedback-based attack detection strategy then devise the optimal evasion policy based on a fuzzy inference system that dynamically adapts the resource reservation time. Simulation results show the effectiveness of our scheme in greatly reducing packet dropping-based attacks, and also in improving the packet reception ratio within the network.

With the rapid growth of inland shipping and the increased number of inland ships required to convey the freight, the ultimate goal is to design an intelligent shipping system to make inland shipping safer and more efficient. Cooperative ships safety systems are an emerging approach to supporting reliable collision detection. Two critical requirements of cooperative safety applications are position accuracy and ultra-low communication latency. Therefore, this paper proposes a new collision detection system for inland ships based on Federated Deep Learning, which is expected to provide a robust positioning prediction model. In addition, it guarantees collaborative learning among all ships while preserving ships' privacy. Furthermore, our safety system is deployed at Multi-access Edge Computing (MEC) nodes to ensure low latency communication and guarantee real-time reaction to avoid collisions between ships. Extensive simulation results show the system's accuracy and, hence, the efficiency of the collision detection system to ensure timely and trusted communications and avoid collisions between ships.

Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks.

Inland vessels often have to cross numerous locks before reaching their final destination, which leads to a significant delay and sometimes represents as much as half of the total travel time. The delay affects shipment costs and can affect other parts of the transport chain, adversely impacting this transportation mode’s growth. Therefore, this work presents a two-level solution to ensure a shorter waiting time at locks and improve inland waterway transport. On the one hand, the first level focuses on making infrastructural modifications by proposing an efficient Lock Automation Decision Making (Lock-ADM) method. The problem modeling consists of using a three-stage algorithm. Firstly, we calculate the optimal number of locks while minimizing the investment costs using the exact solver, CPLEX. Secondly, we measure the importance of locks in the network, and finally, we select the best locks to automate using the Genetic Algorithm (GA) metaheuristic. Based on real data, we achieved an average reduction of 33.7% in overall lock waiting time at a low cost. On the other hand, the second level proposes a Dynamic Lock Scheduling (Lock-DS) to efficiently manage vessels scheduling at locks by minimizing their waiting time and optimizing their speed. We achieve an average reduction of 69.9% in vessel waiting time and a reduction of 48.03% in total fuel consumption compared to existing scheduling methods. Automating the most important locks with Lock-ADM and managing their crossing with Lock-DS ensure shorter vessels’ waiting time and represent a significant first step towards the automation of inland navigation.

For autonomous vehicles (AVs), an intelligent awareness system is a fundamental task that provides crucial information on the driving environment. The main techniques for self-driving systems include solving tasks like self-localization, parsing the driving road, and understanding objects, enabling the system to reason and act. However, compared to other AVs, like cars, such robust awareness systems are not yet developed for inland autonomous vessels. Therefore, this paper proposes a system to accurately delimit safe navigation areas by simultaneously locating and mapping the environment. First, we construct the first open-source dataset; the InlandAutoDetect dataset comprises 3,377 images comprehensively labeled for object detection in a fluvial domain with almost 30,000 objects annotated. Second, we analyze and compare the results of nine deep learning perception models adapted to the inland environment in accuracy and run-time speed. The best one, namely Retinanet, is selected, and its different variants are tested by modifying the feature extractor block. Then, we propose the most suitable configuration for inland navigation. Finally, the selected model results are integrated into the complete system to delimit the safe sailing area. The achieved accuracy rate is above 88%, and the run-time speed rate is 0.8 seconds per image. Hence, the performance evaluations show the proposed system’s robustness and effectiveness to give accurate results and fulfill real-time operation requirements.

Vehicular Fog Computing (VFC) is a paradigm of vehicular networks that has a set of advantages such as agility, efficiency, and reduced latency. The VFC is vulnerable to a variety of attacks, and existing security measures in traditional networks are not necessarily applicable to VFC. Among these attacks, we can find the Sybil attack that allows a vehicle to create multiple identities to perform malicious operations. In this paper, we propose a blockchain-based mechanism to detect Sybil attacks in VFC networks. The detection process consists of two levels; the first one is targeted toward the verification of the vehicle’s position by the FN using the Received Signal Strength Indicator (RSSI) technique. The FN delivers a position proof, if its position is valid, and stores it in the blockchain. At this point, the set of the obtained position proofs constitutes a trajectory. The second level is projected toward a comparison between the trajectories of the vehicles reporting an event. Two trajectories that pass through the same FNs at the same time, will be considered as Sybil trajectories. The objective of these two-level detections is to identify the Sybil attack in several attack scenarios performed by a powerful adversary. Our analysis shows that existing proposals cannot deal with such an adversary. Moreover, simulation results show the efficiency of our proposal in terms of communication, computation, and detection rate. Indeed, our system can reach a detection rate of 98% when the malicious vehicle generates several aliases simultaneously and sends position requests to the FN for each generated pseudonym.

A promising potential of Unmanned Aerial Vehicles (UAV) in 5G networks is to act as Aerial Base Stations (ABSs) that dynamically extend terrestrial base stations coverage without overloading the infrastructure. However, coverage extension faces crucial challenges such as user mobility and determining the best coordinates for new base station deployment. In this paper, we address this problem based on the prediction of users' spatial distribution that allows Aerial base stations (ABS) to adjust their position accordingly. We first analyze the performance of two machine learning schemes (Long Short Term Memory (LSTM)-based encoder-decoder and self-attention-based Transformer) for user mobility prediction based on a real DataSet. Then, we use these schemes to enhance the ABS deployment algorithm. Numerical results reveal significant gains when applying the proposed mobility prediction models over traditional deployment algorithms. In four hours of the day, both the Transformer and LSTM based models show, respectively, more than 31% and 22% gain in coverage rates compared to regular deployment schemes.

The secure multidimensional data aggregation (MDA) has been widely investigated in smart grid for smart cities. However, previous proposals use heavy computation operations either to encrypt or to decrypt the multidimensional data. Moreover, previous fault-tolerant mechanisms lead to an important computation cost, and also a high communication cost when considering a separate identification phase. In this article, we propose an efficient and secure MDA scheme, named ESMA. Unlike existing schemes, the multidimensional data in ESMA are structured and encrypted into a single Paillier ciphertext and thereafter, the data are efficiently decrypted. For privacy preserving, the Paillier cryptosystem is adopted in a fog computing-based architecture, and to achieve efficient authentication, the batch verification technique is applied. Besides, ESMA is fault tolerant, i.e., even if some of the smart meters fail to send their data, the final aggregation result will not be affected. Furthermore, ESMA can be adapted to respond to other queries than the summation of data. The performance analysis demonstrates the cost efficiency of ESMA both in computation and communication and the scalability as well. For instance, with a 16-bits size for each data type and 500 reporting smart meters, 40 data types can be supported in a single Paillier ciphertext. ESMA also resists various security attacks and preserves the user's privacy.

Mobile devices have usually limited capabilities in terms of computation power, battery lifetime, storage size and available bandwidth. Thus, to address these limitations and to continue supporting the ever-increasing application requirements, service providers use powerful servers in order to offer services through the cloud. However, due to latency and QoS limitations, cloud computing still does not solve all the problems of newly emerging mobile applications demands. Thus, a more recent development is to push the storage and processing capabilities to the edge of access network closer to end users, which introduce the new concept of fog computing. Fog computing is a decentralized computation framework which essentially extends cloud computing resources and services to the edge of access network [2].

Internet of Things (IoT) is becoming an emerging paradigm to provide pervasive connectivity where “anything“ can be connected “anywhere” at “anytime” via massive deployment of physical objects like sensors, controllers, and actuators. However, the open nature of wireless communications and the energy constraint of the IoT devices impose strong security concerns. In this context, traditional cryptographic techniques may not be suitable in such a resource-constrained network. To address this problem, an effective security solution that ensures a trade-off between security effectiveness and energy efficiency is required. In this paper, we exploit cooperative transmission between sensor nodes in IoT for e-Health application, as a promising technique to enhance the physical layer security of wireless communications in terms of secrecy capacity while considering the resource-impoverished devices. Specifically, we propose a dynamic and cooperative virtual multiple-input and multiple-output (MIMO) configuration approach based on game theory to preserve the confidentiality of the transmitted messages with high energy savings. For this purpose, we model the physical layer security cooperation problem as a non-transferable coalition formation game. The set of cooperative devices form a virtual dynamically-configured MIMO network that is able to securely and efficiently transmit data to the destination. Simulation results show that the proposed game-based virtual MIMO configuration approach can improve the average secrecy capacity per device as well as the network lifetime compared to non-cooperative transmission.

This research work presents conceptual considerations and quantitative evaluations into how integrating computation offloading to edge computing servers would offer a paradigm shift for an effective deployment of autonomous drones. The specific mission that has been considered is collaborative autonomous navigation and mapping in a 3D environment of a small drone network. Specifically, in order to achieve this mission, each drone is required to compute a low latency, highly compute intensive task in a timely manner. The proposed model decides for each task, while considering the impact on performance and mission requirements, whether to (i) compute locally, (ii) offload to the edge server, or (iii) to the ground station. Extensive simulation work was performed to assess the effectiveness of the proposed scheme compared to other models.

Software-defined networking (SDN) is considered as one of the main enabler technologies of 5G that is expected to propel the penetration of vehicular networks. The rapid development of wireless technology has generated an avalanche demand for bandwidth-intensive applications (e.g., video-on-demand, streaming video, etc.) causing an exponential increase in mobile data traffic. Moreover, the use of edge caching technique enhances network resource utilization and reduce backhaul traffic. Many incentive mechanisms have been developed to encourage caching actors to enhance the caching process. In this paper, we propose an SDN based incentive caching mechanism for a 5G-enabled vehicular network. Our caching strategy consists of a small base station (SBS) that encourages mobile vehicles equipped with embarked caches to store and share its popular contents using vehicle to vehicle (V2V) communication. SBS aims to offload the cellular core links and reduce traffic congestion, where cache-enabled vehicles compete to earn more SBS reward. The interaction between the SBS and the cache-enabled vehicles is formulated using a Stackelberg game with a non-cooperative sub-game to model the conflict between cache-enabled vehicles. The SBS acts first as a leader by announcing the number of popular contents that it wants to cache and the cache-enabled vehicles respond after by the optimal number of contents they accept to cache and the corresponding caching price. Two optimization problems are investigated and the Stackelberg equilibrium is derived. The simulation results demonstrated the efficiency of our game theoretical based incentive V2V caching strategy.

The rapid growth of communication networking, ubiquitous sensing, and signal processing has spurred the emergence of the Internet of Things (IoT) era. As a novel cutting-edge technology, the IoT enables a plethora of smart-devices equipped with diverse computing, sensing, and actuation capabilities to be connected to the Internet. Thus, it promises to provide a revolutionary and fully connected “smart” world while greatly developing economies and enhancing the quality of life. IoT is indeed an emergent global phenomenon, where real-time remote access to data and applications opens new unprecedented opportunities for ubiquitous monitoring and managing. In such dynamic, interconnected, and heterogeneous environment where the context conditions (location, time, situation sensitivity, etc.) are continuously and frequently changing, context-aware and adaptive solutions for data access are required to respond to the applications' needs. Nevertheless, until now, no schemes provide concrete context-aware access control mechanisms in IoT. In this article, we design a novel context-aware attribute-based access control (CAABAC) that considers the dynamic context changes. The proposed approach incorporates the contextual information with the ciphertext-policy attribute-based encryption (CP-ABE) to guarantee adaptive contextual access to data. The extensive analysis and simulations prove both the effectiveness and efficiency of the proposed scheme. Specifically, context-aware and adaptive remote access is enabled while outperforming other benchmarked schemes in terms of storage, communication, and computational cost.

Protecting the location privacy is one of the main challenges in vehicular ad-hoc networks (VANETs). Although, standardization bodies, such as IEEE and ETSI, have adopted a pseudonym-based scheme as a solution for this problem, an efficient pseudonym changing and management is still an open issue. In this paper, we propose PRIVANET, a complete and efficient pseudonym changing and management framework. The PRIVANET has a hierarchical structure and considers the vehicular geographic area as a grid. Each cell of this grid contains one or many logical zones, called vehicular location privacy zones (VLPZs). These zones can easily be deployed over the widespread roadside infrastructures (RIs), such as gas stations, to provide a secure changing and management of pseudonyms. The proposed framework consists of different building blocks: 1) an effective VLPZ-based pseudonym changing strategy; 2) a reputation-based mechanism to motivate selfish vehicles to enter VLPZs; 3) an adapted user-centric privacy model; 4) a secure hybrid mechanism for the distribution of pseudonyms sets and CRLs; 5) a method to generate the IP and MAC addresses from the pseudonym; 6) a stochastic model to estimate the number of VLPZs required at a given cell; and 7) a mathematical model for an optimal placement of the VLPZs over RIs to reduce the transportation cost of vehicles in terms of time. An extensive simulation study using a realistic map and with real traffic mobility measurements is carried out to evaluate and validate the performance of the PRIVANET. The simulation results demonstrate the effectiveness of the proposed framework.

Rapid growth of Internet of Things (IoT) devices dealing with sensitive data has led to the emergence of new access control technologies in order to maintain this data safe from unauthorized use. In particular, a dynamic IoT environment, characterized by a high signaling overhead caused by subscribers' mobility, presents a significant concern to ensure secure data distribution to legitimate subscribers. Hence, for such dynamic environments, group key management (GKM) represents the fundamental mechanism for managing the dissemination of keys for access control and secure data distribution. However, existing access control schemes based on GKM and dedicated to IoT are mainly based on centralized models, which fail to address the scalability challenge introduced by the massive scale of IoT devices and the increased number of subscribers. Besides, none of the existing GKM schemes supports the independence of the members in the same group. They focus only on dependent symmetric group keys per subgroup communication, which is inefficient for subscribers with a highly dynamic behavior. To deal with these challenges, we introduce a novel Decentralized Lightweight Group Key Management architecture for Access Control in the IoT environment (DLGKM-AC). Based on a hierarchical architecture, composed of one Key Distribution Center (KDC) and several Sub Key Distribution Centers (SKDCs), the proposed scheme enhances the management of subscribers' groups and alleviate the rekeying overhead on the KDC. Moreover, a new master token management protocol for managing keys dissemination across a group of subscribers is introduced. This protocol reduces storage, computation, and communication overheads during join/leave events. The proposed approach accommodates a scalable IoT architecture, which mitigates the single point of failure by reducing the load caused by rekeying at the core network. DLGKM-AC guarantees secure group communication by preventing collusion attacks and ensuring backward/forward secrecy. Simulation results and analysis of the proposed scheme show considerable resource gain in terms of storage, computation, and communication overheads.

Many smartphone-based traffic safety applications have been proposed in literatures. These applications demand very high position sampling to safeguard vulnerable road users. European Telecommunication Standards Institute (ETSI) has defined time interval between Cooperative Awareness Messages for collision risk warning to be between Is and 0.1s. This implies that geographical awareness information has to be sampled between the frequencies 1Hz and 10Hz inclusive. However, an investigation we made depicts that current smartphones can't support such high rate location sampling. Even though they meet the aforementioned sampling requirements, high rate sampling of position data is an energy hungry process. In light of this, we have proposed an energy efficient position prediction method that fuses GPS and Inertial Navigation Systems (INS) sensors data to estimate pedestrians' positions at high rate. INS based dead reckoning is performed to extrapolate positions from last known location when GPS reading is unavailable and when GPS fix is realized the reading is used to correct dead reckoning parameters in addition to serving as a location fix. The proposed solution is compared to a position prediction method that relies solely on GPS data on two selected pedestrian trajectories. The result demonstrates that fusing GPS and INS position data has an average improvement of 30% and 61.4% in error in distance and direction respectively. The proposed position prediction algorithm is also applied to sensors data that are obtained by relaxing sampling rates with the objective of sparing smartphone's energy. In this regard, first energy efficiency of different position sampling rates of GPS and INS sensors are evaluated and then the algorithm is applied to the sampling frequencies that are proven to husband energy. The outcome of the evaluation is that the battery life of smartphones can be doubled by compromising accuracy of estimated distance and direction by only 11.5% on average.

The rapid growth of Internet of Things (IoT) technology offers huge opportunities and also brings many new challenges related to the authentication in (IoT) devices. Using passwords or pre-defined keys have drawbacks that limit their use for different (IoT) applications like smart hotel and smart office. In fact, they didn't provide temporary access to data in such reservation systems. Thus, authenticating users basing on password mechanism is not feasible. In this paper, we propose a new Token-Based Lightweight User Authentication (TBL UA) for (IoT) devices, which is based on token technique in order to enhance the robustness of authentication. Security analysis shows the security strength of the proposed scheme such as token security, Perfect Forward Secrecy (PFS), etc. In addition, the presented performance analysis shows that it is a strong competitor among existing ones for user authentication in (IoT) environments.

Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data set openly accessible for the community working on this area. The other contribution of this research is the adaptation and the configuration of deep learning techniques used in other domains such as maritime and road domain to fluvial domain for autonomous vessels in which high accuracy and fast processing are vital. Experiments demonstrated that detecting objects in such environment is plausible in near real time with the selected algorithms.

The rapid technological advancements in wireless communications, ubiquitous sensing and mobile networking have paved the way for the emergence of the Internet of Things (IoT) era, where “anything” can be connected “anywhere” at “anytime”. However, the flourish of IoT still faces various security and privacy preserving challenges that need to be addressed. In such pervasive and heterogeneous environment where the context conditions dynamically and frequently change, efficient and context-aware mechanisms are required to meet the users' changing needs. Therefore, it seems crucial to design an adaptive access control scheme in order to remotely control smart things while considering the dynamic context changes. In this paper, we propose a Context-Aware Attribute-Based Access Control (CAABAC) approach that incorporates the contextual information with the Ciphertext-Policy Attribute-based Encryption (CP-ABE) to ensure data security and provide an adaptive contextual privacy. From a security perspective, the proposed scheme satisfies the security requirements such as confidentiality, context-aware privacy, and resilience against key escrow problem. Performance analysis proves the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of storage, communication and computational cost.

Using Internet of Things (IoT) in the health domain is one of the most promising approaches which offer a ubiquitous healthcare where sensors are used, in real time, for constant monitoring of patient's symptoms and needs wherever he is. Wireless body area network (WBAN) is a highly suitable communication tool for the medical IoT devices. However, the conception of WBAN applications is still a challenging job that should take into consideration many technical requirements such as network lifetime, security level, network throughput and data criticality and prioritization. As a consequence, a trade-off between security effectiveness, energy efficiency, and QoS requirements can be perceived as a major performance objective. In this paper, we propose a stochastic game to balance the tradeoff between network performance and security level while taking into account the context dynamics. Simulation results show that the proposed approach can achieve an acceptable security level and is more efficient than benchmark algorithms in terms of network lifetime and throughput.

Greenhouse gas emissions are an emerging issue that poses a serious environmental problem and threat the entire world. Electricity production process is considered among the principal CO2emitters since it relies heavily on non-renewable sources like coal and natural gas. However, switching to green electricity generation as hydro or wind is not yet evident because they represent intermittent sources that are highly weather-dependent and can meet now at maximum 14% of electricity generation needs. Moreover, green electricity is often more expensive than non-green ones. As governments and utilities worldwide seek to reduce carbon footprints and conserve energy, they are interested to use further wireless Internet of Things (IoT) technologies to transform traditional energy infrastructure into interconnected smart grid. Smart meters are an essential element and usually the first milestone in smart grid implementations. In this context, we propose an optimization model embedded in these IoT devices (i.e. smart meters) to intelligently schedule energy consumption from renewable & non-renewable electricity providers and an energy storage system (battery) to meet smart buildings electricity requirements. The proposed optimization model takes into consideration several constraints, namely the availability as well as the electricity price of each source. The goal of this model is to find the best proportion of using each source in order to reduce CO2emissions but also taking into account the minimization of consumer's billing cost. Simulation results prove the efficiency of our model either with or without using carbon emissions tax to penalize using non-green energies.

Internet of Things (IoT) based pervasive healthcare systems have revolutionized the healthcare industry while enabling remote patient monitoring to improve patient care delivery and provide a highly reliable ubiquitous healthcare monitoring. In this context, Virtual Private Network (VPN) is a promising network layer technology that is used for a secure, reliable and remote access to patient health information. It is an overlay network that creates secure and dynamic tunnels between various devices across a public network such as the Internet. However, it is vulnerable to several attacks such as spoofing, snipping, and hacking as the data is transmitted through the Internet connection. In addition, due to the IoT device's energy constraints and the application requirements, the main purpose is to adaptively select the most appropriate encryption and authentication algorithms to secure the communication tunnel between these resource constrained devices and the remote user. Therefore, adaptive risk-aware secure tunnel negotiation is perceived as a major performance objective. In this paper, we propose a Stackelberg game for the security requirements negotiation between the communication peers in order to ensure a trade-off between security effectiveness and network performance while considering the dynamic context changes. Simulation results prove that the proposed approach can reduce the cost of security policy implementation in terms of performance degradation. For instance, the latency is improved by around 25% compared to static security policy.

As mobile applications deliver increasingly complex functionalities, the demands for even more intensive computation would quickly transcend energy capability of mobile devices. On one hand and in an attempt to address such issues, fog computing paradigm is introduced to mitigate the limited energy and computation resources available within constrained mobile devices, by moving computation resources closer to their users at the edge of the access network. On another hand, most of electric vehicles (EVs), with increasing computation, storage and energy capabilities, spend more than 90% of time on parking lots. In this paper, we conceive the basic idea of using the underutilized computation resources of parked EVs as fog nodes in order to provide on-demand computation at the vicinity of the access network. The proposed Vehicular Fog Computing (VFC) architecture aggregates the abundant unused resources of parked vehicles, and uses it to serve mobile users' demands. The resource allocation problem is formulated as a Markov Decision Process (MDP) and dynamic programming is used to solve the underling decision problem. Extensive simulation results show the effectiveness of the proposed approach by improving the global reward value by 51% and scoring an energy gain of 66% compared to two other models.

Industrial Partners (IPs) with Mobile Network Operators (MNOs) are extending the mobile network infrastructure with Small Cells (SCs) in order to meet the growing mobile traffic demand. Due to the increasing number of telecommunication market competitors and the scarcity of radio resources, static sharing schemes are no more efficient. New dynamic schemes should be considered to meet both user expectations and economic success. In a crowd networking context, we propose in this work a dynamic radio resource scheme based on combinatorial double auctions. The participants in these auctions are the MNOs considered as buyers and the IPs, providers of SCs, considered as sellers. The commodity is a bundle of radio resources geographically distributed in several sites. This market place is regulated by an auctioneer responsible of pricing process and market clearing. The auctions are launched periodically to increase the elasticity. Theoretical analysis shows that this model satisfies the most important properties of auctions namely, strong balanced budget, individual rationality, incentive compatibility and economic efficiency. The proposed auction model is also simulated to evaluate the social welfare, the utilization percentage of the supplied resources and the service rate of the buyers demand. Results show that this model ensures a positive utility values for both sides with a service rate that exceeds 50\% and an utilization rate that reaches 50\% for some IPs.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

Many resource-constrained applications make use of data aggregation in order to prolong the network lifetime. However, the resource-constrained devices are usually deployed in unattended environments in which providing security is of paramount importance. This leads to various attacks that can occur during data aggregation process. Internal attacks such as selective forwarding represent the most dangerous ones since they cannot be detected by existing cryptography- based protocols proposed to secure data aggregation. In this work, we propose a two-levels verification, in which data is verified using cryptography and intrusion detection techniques. Indeed, a lightweight homomorphic encryption is combined with a game-theory based technique to efficiently secure data aggregation. Our analysis and results show the applicability of the system for aggregation-based resource-constrained applications, especially those considering sensitive information (e.g. health monitoring, military) where the time-efficient detection is crucial.

The deployment of small cells is one of the technical solutions to meet the challenge of data traffic rise. It reduces the cost of radio access networks. The efficiency of this solution depends on the success of cell planning to mitigate the interference. This work aims to optimize the incremental cell planning scheme that considers a preliminary macro-cell network infrastructure and expands it with new small cells to enhance the coverage and the capacity. In order to reduce the cost of installing new small cells, we consider a crowd networking business model where the Mobile Network Operator retrieves sites to deploy small cells above. We formulate the small cells placement problem as an Integer Linear Programming. It aims to maximize the coverage, ensure the required capacity and mitigate the cross-tier interference. Since the problem is NP-hard with large number of sites, we propose an heuristic to select the optimal sites locations around each macro cell. We tested the proposed heuristic with different simulation scenarios. The proposed Sequential Deployment scenario is better in coverage uniformity among dense and non dense zones whilst the Dense Zones First ensures more optimized selected sites number with better capacity in the dense zones in terms of users. The last scenario proves that higher candidate sites optimize the number of small cells in the non dense areas without compromising the coverage.

Vehicle to vehicle wireless communication is regarded as a key component for improving safety on the road. There has been increasing interest in addressing the scalability issue with a large literature that has been proposed to mitigate packet congestion problem. LIMERIC is a well-known congestion control protocol which was adopted by the current ETSI standardization bodies to be applied in the future deployment of VANETs. In this study, the objective is to evaluate the application reliability of using the density information in this problem. In this paper, we compare LIMERIC to LIMERIC-D to provide an answer to the question whether density estimation can improve the performance of the congestion control strategies regarding application reliability. The simulation results shows the effectiveness of the use of the density information to improve the performance of congestion control protocols in VANETs.

High penetration rate of Smartphones and their increased capabilities to sense, compute, store and communicate have made the devices vital components of intelligent transportation systems. However, their GPS positions accuracy remains insufficient for a lot of location-based applications especially traffic safety ones. In this paper, we developed a new algorithm which is able to improve smartphones GPS accuracy for vulnerable road users' traffic safety. It is a two-stage algorithm: in the first stage GPS readings obtained from smartphones are passed through Kalman filter to smooth deviated reading. Then an adaptive online time warping based map matching is applied to map the improved new locations to corresponding road segments. The incremental alignment is made in real-time based on two similarity metrics - distance and direction difference. Different online time warping variants are formulated and compared with the naïve dynamic time warping algorithm in terms of accuracy and response time. We tested them on GPS trajectories collected from smartphones and reference points extracted from road network data. Test results show that while the proposed algorithms have comparable accuracy with existing algorithms, they largely outperform in terms of response time. For the dataset used, average ratios of correct matches are 91.4% and 93.2% for newly proposed algorithms and for existing algorithm respectively. Negligible accuracy inferiority of the new algorithms is compensated by large improvement on response times which are meliorated from seconds to instant responses.

With the expansion of the Internet of Things (IoT) era and the tremendous progress of wireless communications, Wireless Body Area Networks (WBANs) have been introduced as a promising technology for remote health monitoring. However, the heterogeneity of IoT technologies, the open nature of wireless networks, and the existence of resource-constrained devices are the main challenges to design burdensome security protocols for WBANs. In this context, scalability, reliability, interoperability, and security requirements need risk-aware and adaptive security solutions that take into account the dynamic context changes. Therefore, a trade-off between risk mitigation actions and network performance can be perceived as a major performance objective. The main purpose of this work is to propose an adaptive risk management framework that considers the WBAN context changes to dynamically select the most appropriate security countermeasure and assess its implementation cost in terms of performance degradation. Furthermore, a case study is discussed to show the impact of adaptive risk assessment on network performance.

Wireless body area networks (WBANs) are perceived as an emerging key technology for the next generation ubiquitous healthcare systems. However, the openness and mobility of wireless sensor technologies make the sensor-controller communication vulnerable to be eavesdropped and linked to the sensors in transmission of patient's information. Furthermore, in such resource-constrained environment, authenticating sensor nodes anonymously with the controller node while considering their limited capabilities is a paramount security requirement. In this paper, we propose a lightweight and adaptive anonymous authentication and key agreement scheme for the two-tier WBAN. The proposed protocol enables an anonymous mutual authentication and a session key establishment between the controller node and the body sensor nodes while taking into account the dynamic context changes. From a security perspective, we demonstrate that the proposed key agreement scheme achieves the desired security properties such as anonymity, unlinkability, perfect forward secrecy, etc. Performance analysis proves that the proposed protocol outperforms benchmark schemes in terms of communication and computational overhead.

Vehicular Cloud Computing (VCC) is envisioned as a promising approach to increase computation capabilities of vehicle devices for emerging resource-hungry mobile applications. In this paper, we introduce the new concept of Vehicular Fog Computing (VFC). The Fog Computing (FC) paradigm evolved and is employed to enhance the quality of cloud computing services by extending it to the edge of the network using one or more collaborative end-user clients or near-user edge devices. The VFC is similar to the VCC concept but uses vehicles resources located at the edge of the network in order to serve only local on-demand mobile applications. The aim of this paper is to resolve the problem of admission control for applications with different QoS requirements and dynamic vehicle resources. In order to model the problem, a potential theoretical game approach is designed and a new scheduling algorithm is proposed. Moreover, we developed the decentralized decision making problem among vehicles device resources as a decentralized game. We evaluate all game properties and show that the game always admits Nash equilibrium. Simulation results demonstrate that the proposed approach can achieve efficient QoS and scale well as the system size increases.

Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the most lethal cyber-attacks that can target an UAV network, namely, false information dissemination, GPS spoofing, jamming, and black hole and gray hole attacks. Extensive simulations confirm that the proposed scheme performs well in terms of attack detection even with a large number of UAVs and attackers since it exhibits a high detection rate, a low number of false positives, and prompt detection with a low communication overhead.

Small drones are currently emerging as versatile nascent technology that can be used in exploration and surveillance missions. However, most of the underlying applications require very often complex and time-consuming calculations. Although, the limited resources available onboard the small drones, their mobility, the computation delays and energy consumption make the operation of these applications very challenging. Nevertheless, computation-offloading solutions provide feasible resolves to mitigate the issues facing these constrained devices. In this context, we address in this paper the problem of offloading highly intensive computation tasks, performed by a fleet of small drones, in order to improve the energy overhead and decrease the execution delay. We adopt a theoretical methodology based on a sequential game where three different types of players (drone, base station and edge server) carry out the heavy computation tasks. Compared to literature, as far as we know, we are the first to consider a computation- offloading problem with three different devices. Each player has a set of possible strategies, depending on the previous actions that the other players might undertake in a sequential game. Furthermore, we prove the existence of a Nash Equilibrium and design an offloading algorithm that converges to this optimal point. Extensive simulations gave promising results where the sequential game based model outperforms comparable approaches in terms of global utility, which pledges the best possible tradeoff between energy consumption and achievable delay.

Nowadays, smart meters play a primordial role in the Smart Grid (SG) in order to optimize electricity production, distribution as well as consumption. By exchanging more frequent information between the smart meter and Utility Provider (UP), SG will operate more efficiently with less loss. However, this frequent exchange poses a serious privacy issue which can invade individual privacy since the UP is able to collect a massive amount of smart meter-related data not necessary for billing purposes. In this paper, we propose an efficient sustainable model that uses a local energy to enhance the consumer privacy and reduce his electricity cost at the same time. Moreover, the proposed model is lightweight but also reliable to deal with the low computation capacity of the smart meter. Analytic results prove the efficiency of our model.

Heterogeneous networks (HetNets) which include macrocells with short range small cells proved a better coverage and higher user data rates compared to classical networks. Using the same spectrum as the macrocells, small cells would allow increased spatial reuse of bandwidth. In industrialized countries, the deployment of new small cells by another actor (tier) to cover the outage improves the service with a lower cost. In this paper, we investigate cell association issue in heterogeneous networks composed of small and macrocells operating in the same spectrum. In contrast to the related work, we consider that the small cells are a cellular network belonging to another tier. Hence, there is competitiveness between the two tiers in order to selfishly maximize the gain while respecting the User Equipment (UE) Quality of Service (QoS) requirements. We propose a model based on game theory in order to get the best distribution of user equipments among small and macro base stations.

The management of radio congestion in the control channel is one of the active research areas in Vehicular Ad-hoc Networks (VANETs). Many congestion control protocols have already been proposed to ensure an optimal management of the radio control channel. LIMERIC is a well-known congestion control protocol which was adopted by the current ETSI standardization process to be applied in the future deployment of VANETs. This protocol uses a mathematical equation to adjust the beaconing rate for each vehicle based on the measured channel load and a targeted channel load. However, efficiently managing all the available bandwidth using LIMERIC is yet to be achieved and still an open challenge. To address this issue, we propose a new approach that enhances LIMERIC protocol so that the available bandwidth would be used efficiently. Our aim is to bring the measured channel load as close as possible to the targeted level of channel load. Our method combines LIMERIC with a novel local density estimation approach called Segment based Local Density Estimation (SLDE). The performance evaluation shows that our approach uses the bandwidth efficiently and allows higher beaconing rate with a fair division of the available bandwidth.

Due to the limitations of mobile devices in terms of processing power and battery lifetime, cloud based solutions offer an attractive approach to answer these shortcomings. Since offloading intensive computation tasks to an edge/cloud server would achieve impressive performances, computation offloading paradigm has attracted the focus of many research groups in the last few years. This paper considers the problem of computation offloading while achieving a tradeoff between execution time and energy consumption. The proposed solution is intended for a fleet of small drones that are required to achieve highly intensive computation tasks. Drones need to detect, identify and classify objects or situations. Thus, they are brought to deal with intensive tasks such as pattern recognition and video preprocessing. The latter implement very complex calculations and typically require dedicated and powerful processors, which would definitely accentuate the dilemma between energy and delay. We adopted a game theory model where the players are all the drones in the network with three possible strategies. We defined the cost function to be minimized as a combination of energy overhead and delay. The simulation results are very promising and the achieved performances outperformed their counterparts in terms of average system wide cost and scalability.

The wireless industry is preparing the fifth generation wireless systems with several requirements regarding capacity and coverage area. A promising solution to meet this challenge is a densification of the network via a large scale deployment of small cells from different sizes. The advantage here is that small cells are easy to deploy and cost-efficient compared to macro base stations. However, the challenge is to ensure a reliable backhaul links. Mesh routers backhauling is one of the candidate solutions here, since it is easy to deploy, doesn't use wired connections between locations and cost-efficient. Nevertheless, the issue is to deploy routers in the suitable locations that provide the convenient and cheaper power sources. In this paper, we address the problem of mesh routers placement to ensure small cells backhauling. We propose a novel approach that aims to minimize the number of mesh routers and optimize their placement among locations offering the possibility to use solar panels. We first model the problem as an Integer Linear Program (ILP) and since it is hard to find the optimal solution with a large number of mesh routers and small cells, we propose also an heuristic approach called GPMR (Green Placement for Mesh Routers). We study the performance of both, the ILP model and the proposed heuristic GPMR, through simulations scenarios. Results show that GPMR provided performances close to those provided by ILP when the number of small cells is less than 10.We also show that the results provided by our heuristic GPMR follow an evolution close to the one observed in the optimal solutions when the number of small cells is high (more than 20).Thus, our heuristic approach can be used to obtain a near-optimal performance for ultra-dense networks.

The Internet of vehicles allows connecting vehicles to the Internet to make all data from vehicles available for applications aimed towards improving safety and comfort for passengers. Density is one of the most important sensed data to gather. This information is mainly obtained through periodic messages broadcast by the neighbouring vehicles. However, the availability of this information depends on the Internet. A low penetration rate of Internet of vehicles, or the loss of Internet connection, can significantly affect the accuracy of the sensed density. Moreover, the reception rate of the periodic messages seriously drops at short distances caused by the broadcast storm problem in high‐density scenarios. To address this problem, using inter‐vehicular communications, we propose a segment‐based approach for enhancing the accuracy of the local density estimation. This approach provides a highly accurate estimation with low overhead over the maximum vehicles transmission range to all the vehicles. The proposed approach is extensively evaluated analytically and by simulation. Performance evaluation results show that our approach SLDE allows about 3% of mean error ratio with low overhead over the maximum transmission range.

Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection. In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection.

Protecting the location privacy is still one of the main challenges in Vehicular Ad-hoc Networks(VANETs). Although, standardization bodies such as IEEE and ETSI have adopted the pseudonymous scheme as a solution to this problem, an efficient pseudonym changing and management is still an open issue. In this paper, we propose a complete and efficient pseudonym management and changing scheme based on Vehicular Location Privacy Zone (VLPZ). We define VLPZ as a roadside infrastructure designed to pseudonyms management and changing. This scheme considers that the vehicular geographic area is partitioned as a grid, where each cell contains one or many VLPZs. The location privacy protection level provided by the scheme depends on the VLPZ capacity and the number of vehicles that are inside it at the same time. For this reason, we also propose a reputation mechanism to stimulate vehicles to enter to the VLPZ, and finally evaluate the performances of the proposed scheme using Veins Framework based on OMNet++ network simulator and SUMO mobility. Simulation results demonstrate the effectiveness of the proposed scheme.

Security issues in unmanned aerial vehicle (UAV) networks attract the attention of both industry and research community. This is due to the large number of attacks that can target such networks with a goal for instance to jam the communication, disturb the network operation, inject wrong data, etc. In this paper, we propose and implement a cyber security system to protect the UAVs against the most dangerous threats: cyber-attacks that target the data integrity and network availability. Our system is based on a cyber detection mechanism to promptly detect these attacks as soon as they unfold. Minimizing false positives and false negatives rates is a major issue since classifying a legitimate node as an intruder and vice versa may compromises the efficiency of the security system . Thereby, to address this issue, a threat estimation model based on Belief approach is proposed. Simulation results show that our security system exhibits a high accuracy detection compared to cyber detection system proposed in current literature.

Enabling vehicles to connect to the best available communication medium in a heterogeneous network is of high importance to preserve good quality of service (QoS), however a hard task to handle. The ISO-CALM (Communications Access for Land Mobiles) offers the possibility to manage multiple communication interfaces with a promising architecture, but without any specific implementation. Several techniques were proposed to handle the network choice, however, they didn't stick to an open standard and in some cases many important metrics are not considered. We consider in this paper to develop a fuzzy-based framework to select the best communication medium in a heterogeneous vehicular network. The proposed mechanism is based on fuzzy inference systems and considers several features that affect the decision process, which are available from the network such as received signal strength (RSSI), network density, vehicle speed and service cost. The use of fuzzy-logic is motivated by its capability to treat linguistic parameters in a lightweight manner which makes it able to deal with `ping-pong' problem by reducing the number of vertical handoffs and easy to integrate in real resources-constrained equipments.

In the Internet of Things (IoT), resources' constrained tiny sensors and devices could be connected to unreliable and untrusted networks. Nevertheless, securing IoT technology is mandatory, due to the relevant data handled by these devices. Intrusion Detection System (IDS) is the most efficient technique to detect the attackers with a high accuracy when cryptography is broken. This is achieved by combining the advantages of anomaly and signature detection, which are high detection and low false positive rates, respectively. To achieve a high detection rate, the anomaly detection technique relies on a learning algorithm to model the normal behavior of a node and when a new attack pattern (often known as signature) is detected, it will be modeled with a set of rules. This latter is used by the signature detection technique for attack confirmation. However, the activation of anomaly detection for low-resource IoT devices could generate a high-energy consumption, specifically when this technique is activated all the time. Using game theory and with the help of Nash equilibrium, anomaly detection is activated only when a new attack's signature is expected to occur. This will make a balance between accuracy detection and energy consumption. Simulation results show that the proposed anomaly detection approach requires a low energy consumption to detect the attacks with high accuracy (i.e. high detection and low false positive rates).

Vehicular environments are vulnerable to attacks because of the continuous interactions between vehicles despite authentication techniques deployed by communication standards. In fact, an authenticated node with a certificate could initiate an attack while complying with implemented protocols if it has malicious intentions and benefit from this always on connection to threaten the network accuracy. Several mechanisms to counter these attacks were proposed but none of them is able to anticipate the behavior of nodes. In the present work, we target this problem by proposing a preventive mechanism able to predict the behavior of vehicles and prevent from attacks. We use Kalman filter to predict the future behavior of vehicles and classify them into three categories (white, gray and black) based on their expected trustworthiness. The main concern of this work is to prevent from the denial of service (DoS) attack. Results, given by the implementation of the proposed mechanism over an intersection-based routing protocol using ns3 simulator, prove its accuracy regarding the detection rate and a good impact on packets delivery ratio and end-to-end delay.

Our main focus through the present paper is on developing an original distributed mobility model for autonomous fleet of interconnected UAVs (Unmanned Aerial Vehicles) performing an area exploration mission. The UAVs, equipped with wireless ad-hoc capabilities, are required to optimally explore an area while maintaining connectivity with their neighboring UAVs and the base station. Because energy is a scarce resource, especially for UAVs, its wise management is quite beneficial for the network lifetime and mission success. Hence, the proposed mobility model, compared to other models in the literature, is the first to ever include the remaining energy level as decision criterion combined with area coverage and network connectivity. Based on these criterions and the information received from its neighbors, each UAV determines, using the information received from its neighbors, its next movement to be undertaken. The performances of the proposed approach are compared with those achieved through a randomized approach and a forces-based approach. Simulation results, using NS3, show that it outperforms the two other models in terms of coverage and connectivity.

In this paper, we propose an efficient and lightweight attack detection mechanism for a smart grid Neighborhood Area Network (NAN) that combine between distributed and centralized intrusion detection. A NAN includes the customers' appliances, smart meters and collectors. The smart meters measure the power consumption of each appliance and the collectors aggregate the measures and forward them to the control center for analysis. Intrusion Detection System (IDS) agents, proposed in our framework, run in a distributed fashion at smart meters level and in a centralized fashion at collector and control center nodes. A combination between a rule-based detection and a learning algorithm for training and classification is proposed to detect intruders that want to either inject false measurements or exhaust the energy of the grid, or inject Denial of Service (DoS) attacks. Simulation results confirm that our intruder detection framework outperforms the current cyber detection mechanisms since these sophisticated attacks are detected with efficient energy consumption.

Over the last few years, a lot of applications have been developed for Vehicular Ad Hoc NETworks (VANETs) to exchange information between vehicles. However, VANET is basically a Delay Tolerant Network (DTN) characterized by intermittent connectivity, long delays and message losses especially in low density regions [1]. Thus, VANET requires the use of an infrastructure such as Roadside Units (RSUs) that permits to enhance the network connectivity. Nevertheless, due to their deployment cost, RSUs need to be optimally deployed. Hence, the main objective of this work is to provide an optimized RSUs placement for delay-sensitive applications in vehicular networks that improves the end-to-end application delay and reduces the deployment cost. In this paper, we first mathematically model the placement problem as an optimization problem. Then, we propose our novel solution called ODEL. ODEL is a two-steps technique that places RSUs only in useful locations and allows both vehicle-to-vehicle and vehicle-to-infrastructure communication: (i) the first step is comprehensive study that looks for the RSUs candidates locations based on connectivity information, and (ii) the second step uses genetic algorithm and Dijkstra algorithm to reduce the number of RSUs based on the deliverance time requirement and the deployment cost. We show the effectiveness of our solution for different scenarios in terms of applications delay (reduced by up to 84%) and algorithm efficiency (computation performance reduced by up to 79% and deployment cost reduced at least by up to 23%).

In the last few years, significant efforts have been devoted to developing intelligent and sustainable transportation to address pollution problems and fuel shortages. Transportation agencies in various countries, along with several standardization organizations, have proposed different types of energy sources (such as hydrogen, biodiesel, electric, and hybrid technologies) as alternatives to fossil fuel to achieve a more ecofriendly and sustainable environment. However, to achieve this goal, there are significant challenges that still need to be addressed. We present a survey on sustainable transportation systems that aim to reduce pollution and greenhouse gas emissions. We describe the architectural components of a future sustainable means of transportation, and we review current solutions, projects, and standardization efforts related to green transportation with particular focus on electric vehicles. We also highlight the main issues that still need to be addressed to achieve a green transportation management system. To address these issues, we present an integrated architecture for sustainable transportation management systems.

Data routing has gained great intention since the appearance of Vehicular Networks (VANETs). However, in the presence of attackers, reliable and trustworthy operations in such networks become impossible without securing routing protocols. In this paper, we target to study and design a secure routing protocol S-GyTAR for vehicular environments. Several kinds of routing techniques are proposed in the literature and could be classified into topology-based or position-based strategies. Position-based is the most investigated strategy in vehicular networks due to the unique characteristics of such networks. For this reason, this work is based on the well-known intersection-based routing protocol GyTAR, which exploits the greedy forwarding technique to relay data. In fact, we benefit from GyTAR's characteristics and reshape it to introduce a new distributed trust management strategy to secure routing. We design a cluster-based mechanism to monitor nodes and a reputation-based schema to evaluate the vehicles and classify them. We evaluate our proposal using NS3 simulator. Simulation results show high performances regarding the detection rate of malicious nodes and overhead with an amelioration of the end-to-end communication delay in the presence of malicious vehicles.

In this paper, we design and implement a new intrusion detection and prevention schema for vehicular networks. It has the ability to detect and predict with a high accuracy a future malicious behavior of an attacker. This is unlike the current detection schémas, where there is no prevention technique since they aim to detect only current attackers that occur in the network. We used game theory concept to predict the future behavior of the monitored vehicle and categorize it into the appropriate list (White, White & Gray, Gray, and Revocation_Black) according to its predicted attack severity. In this paper, our aim is to prevent from the most dangerous attack that targets a vehicular network, which is false alert's generation attack. Simulation results show that our intrusion detection and prevention schema exhibits a high detection rate and generates a low false positive rate. In addition, it requires a low overhead to achieve a high-level security.

In this paper, we design and implement a new Intrusion Detection Framework for Vehicular Networks (IDFV). These networks are vulnerable to various security attacks due to the lack of centralized infrastructure. The aim of our framework is then to secure them against the most dangerous routing attacks that have a high severity damage such as selective forwarding, black hole, wormhole, packets duplication and resource exhaustion attacks that can target such networks. IDFV relies on a set of detection and eviction techniques to detect, in a short delay, malicious vehicles with a high accuracy and eject them. Furthermore, IDFV applies a robust reputation schema to evaluate vehicles' trust level. We analyze the performances of our framework using NS-3. Simulation results show that IDFV exhibits a high level of security i.e. high detection rate, low false positive rate and a fast attacks' detection compared to detection frameworks proposed in current literature.

On the one hand, intrusion detection systems (IDSs) have shown their ability to detect internal and external attacks with a high accuracy. On the other hand, cryptography techniques have proved their ability to assure the privacy of communication, i.e. data confidentiality. In this paper, we propose to develop and implement a lightweight Security Framework for Wireless Sensor Networks (WSNs) that combines the advantages of both cryptography and IDS techniques to assure the privacy of communication and detect the most dangerous attacks such as spoofed, altered or replayed routing information, man-in-the-middle, and denial-of-service attacks. However, the execution of both techniques at each node generates high overhead and energy consumption, even if we consider a cluster-based WSN. To economize nodes' energy, the cryptography operation is launched during a certain period determined mathematically and only within the cluster where a malicious node is detected. The proposed framework is evaluated analytically and implemented under TOSSIM simulator. According to our simulation results, our framework outperforms other security frameworks proposed in the literature in terms of detection rate, false positive rate, energy consumption and average efficiency.

Among recent advances in wireless communication technologies' field, Vehicular Ad-hoc Networks (VANETs) have drawn the attention of both academic and industry researchers due to their potential applications including driving safety, entertainment, emergency applications, and content sharing. VANET networks are characterized by their high mobile topology changes. Clustering is one of the control schemes used to make this global topology less dynamic. It allows the formation of dynamic virtual backbone used to organize the medium access, to support quality of service and to simplify routing. Mainly, nodes are organized into clusters with at least one cluster head (CH) node that is responsible for the coordination tasks of its cluster. In this sight, our paper introduces a clustering mechanism based for connectivity maintenance in VANET. The proposed solution is experimentally evaluated using NS2 simulator.

The first step in a surveillance system is to create a representation of the environment. Background subtraction is widely used algorithm to define a part of an image that most time remains stationary in a video. In surveillance tasks, this model helps to recognize those outlier objects in an area under monitoring. Set up a background model on moving platforms (intelligent cars, UAVs, etc.) is a challenging task due camera motion when images are acquired. In this paper, we propose a method to support instabilities caused by aerial images fusing spatial and temporal information about image motion. We used frame difference as first approximation, then age of pixels is estimated. This latter gives us an invariability level of a pixel over time. Gradient direction of ages and an adaptive weight are used to reduce impact from camera motion on background modelling. We tested our proposed method simulating several conditions that impair aerial image acquisition such as intentional and unintentional camera motion. Experimental results show improved performance compared to algorithms GMM and KDE.

Vehicular ad hoc networks (VANETs) are wireless networks that provide high-rate data communication among moving vehicles and between the vehicles and the road-side units. VANETs are considered as the main wireless communication platforms for the intelligent transportation systems (ITS). Service-oriented vehicular networks are special categories for VANETs that support diverse infrastructure-based commercial infotainment services including, for instance, Internet access, real-time traffic monitoring and management, video streaming. Security is a fundamental issue for these service networks due to the relevant business information handled in these networks. In this paper, we design and implement an efficient and light-weight intrusion detection mechanism, called efficient and light-weight intrusion detection mechanism for vehicular network (ELIDV) that aims to protect the network against three kinds of attacks: denial of service (DoS), integrity target, and false alert's generation. ELIDV is based on a set of rules that detects malicious vehicles promptly and with high accuracy. We present the performance analysis of our detection mechanism using NS-3 simulator. Our simulation results show that ELIDV exhibits a high-level security in terms of highly accurate detection rate (detection rate more than 97%), low false positive rate (close to 1%), and exhibits a lower overhead compared to contemporary frameworks.

ABSTRACT In the last few years, the technological evolution in the field of wireless sensor networks was impressive, which made them extremely useful in various applications (military, commercial, etc.). In such applications, it is essential to protect the network from malicious attacks. This presents a demand for providing security mechanisms in these vulnerable networks. In this paper, we design a new framework for intrusion detection in cluster‐based wireless sensor networks. Our detection framework is composed of different protocols that run at different levels. The first protocol is a specification‐based detection protocol that runs at intrusion detection system (IDS) agents (low level). The second one is a binary classification detection protocol that runs at cluster head (CH) node (medium level). In addition, a reputation protocol is used at each CH to evaluate the trustworthiness level of its IDSs agents. Each CH monitors its CH neighbors on the basis of a specification detection protocol with the help of a vote mechanism applied at the base station (high level). We evaluated the performances of our framework in the presence of four well‐known attacks: hello flood, selective forwarding, black hole, and wormhole attacks. We evaluated specifically the detection rate, false positive rate, energy consumption, and efficiency. Simulation results show that our detection framework exhibits high detection rate (almost 100%), low number of false positives, less time to detect the attack, and less energy consumption. Our intrusion detection framework outperforms other schemes proposed in the literature in terms of detection, false positive rate, and energy consumption. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Vehicular networks are drawing the attention of both research community and automotive industry because they provide intelligent transportation systems as well as drivers and passengers’ assistant services. However, the industrialization of such networks faces a number of challenges, in particular, the high cost of the infrastructure to deploy. To overcome this problem, an effective solution is to rely on cooperative vehicle‐to‐vehicle communication to minimize the deployed infrastructure. Because a large number of cooperative vehicle‐to‐vehicle applications are broadcasting by nature, we proposed an efficient dissemination protocol: Road‐Oriented Dissemination (ROD). ROD consists in two modules: (i) Optimized Distance Defer Transfer Module and (ii) Store‐and‐Forward Module. We compare our protocol with other dissemination protocols and analyze its performance by simulations, on‐road tests and analytically. Performance study shows interesting results of ROD compared with the other existing solutions. ROD is able to provide a low end‐to‐end delay, high delivery ratios, and a minimum bandwidth usage because only a limited number of vehicles are involved in the broadcast scheme. Copyright © 2011 John Wiley & Sons, Ltd.

In a recent work [1], we proposed LTE4V2X, a novel framework for a centralized vehicular network organization based on 4G LTE network. We demonstrated the efficiency of our framework for an FCD (Floating Car Data) application. Such applications are based on data collected from vehicles (localization, speed, direction, etc.) in order to feed a traffic management server. In the continuity of this work, this new paper presents two extensions of LTE4V2X. The first one is the multi-hop extension, which uses multi-hop communications to deal with areas where there is no LTE coverage (e.g. tunnels). The second extension deals with the adaptation of LTE4V2X framework for a dissemination application that aims to disseminate a specific message in a given geographical area. We analyze the performances of LTE4V2X using NS-3 simulation environment and a realistic highway mobility model. The results show that the multi-hop extension leads to an improvement of LTE4V2X performances, for applications based on both data collection and data dissemination.

Wireless sensor networks (WSNs) have a huge potential to be used in critical situations like military and commercial applications. However, these applications are required often to be deployed in hostile environments, where nodes and communication are attractive targets to attackers. This makes WSNs vulnerable to a variety of potential attacks. Due to their characteristics, conventional security mechanisms are not applicable. In this context, we propose an intrusion detection framework for a cluster-based WSN (CWSN) that aims to combine the advantage of anomaly and signature detection which are high detection rate and low false positive, respectively.

Wireless sensor networks (WSNs) have a huge potential to be used in critical situations like military and commercial applications. However, these applications are required often to be deployed in hostile environments, where nodes and communication are attractive targets to attackers. This makes WSNs vulnerable to a variety of potential attacks. Due to their characteristics, conventional security mechanisms are not applicable. In this context, we propose an intrusion detection framework for a cluster-based WSN (CWSN) that aims to combine the advantage of anomaly and signature detection which are high detection rate and low false positive, respectively.

In this paper, we present a protocol to collect data within a vehicular ad hoc network (VANET). In spite of the intrinsic dynamic of such network, our protocol simultaneously offers three relevant properties: (1) It allows any vehicle to collect data beyond its direct neighborhood (i.e., vehicles within direct communication range) using vehicle-to-vehicle communications only (i.e., the infrastructure is not required); (2) It tolerates possible network partitions; (3) It works on demand and stops when the data collection is achieved. To the best of our knowledge, this is the first collect protocol having these three characteristics. All that is chiefly obtained thanks to a specific tool, namely Operator ant, borrowed from the self-stabilization area which confers to our algorithm the nice property to recover by itself from topology changes. In addition to a theoretical proof of correctness, our protocol has been implemented and tested through the Airplug Software Distribution: Road and lab experiments are presented and discussed.

Mobile ad hoc networks (MANETs) face a number of challenges, in particular due to dynamic network topology and large variable number of mobile nodes. To overcome these problems an effective solution is to define a self-organizing architecture that facilitates the network management task and permits to deploy wide panoply of services. One method to organize such networks is to define a virtual backbone that covers all the nodes in the network. In this paper we propose new self-organization architecture based on an optimized CDS construction. In this approach, the neighbors select CDS nodes intelligibly. In the selection processes, a new weight parameter depending on energy, link quality and connectivity is introduced in order to increase the CDS lifetime and hence the network performances. To study the benefits of this new architecture, we propose a new routing protocol called "PROC" (Proactive Routing based on Optimized CDS). Simulation results, using NS2, show that PROC minimizes the overhead and the energy consumption compared to the well-known OLSR routing protocol.

In wireless sensor networks several constraints decrease communications performances. In fact, channel randomness and energy restrictions make classical routing protocols inefficient. Therefore, the design of new routing protocols that cope with these constraints become mandatory. The main objective of this paper is to present a multi-objective routing algorithm RBCR that computes routing path based on the energy consumption and channel qualities. Additionally, the channel qualities are evaluated based on the presence of relay nodes. Compared to AODV and AODV associated to a cooperative MAC protocol, RBCR provides better performances in term of delivery ratio, power consumption and traffic load.

Vehicular networks face a number of new challenges, particularly due to the extremely dynamic network topology and the large variable number of mobile nodes. To overcome these problems, an effective solution is to organize the network in a way which will facilitate the management tasks and permit to deploy a wide panoply of applications such as urban sensing applications. This paper presents LTE4V2X, a novel framework for a centralized vehicular network organization using LTE. It takes advantage of a centralized architecture around the eNodeB in order to optimize the clusters management and provide better performances. We studied its performances against a decentralized organization protocol for a well known urban sensing application, FCD application. We analyze the performances of LTE4V2X using NS-3 simulation environment and a realistic urban mobility model. We show that it permits performance improvement by lowering the overhead induced by control messages, reducing the FCD packet losses, as well as enhancing the goodput.

Over the last decade cooperative communication in wireless sensor networks (WSN) received much attention. A lot of works have been done to propose a MAC layer that supports cooperative communication. However the impact of the association of a cooperative communication technique with a low power listening scheme was not studied in the literature. In this paper we propose CL-MAC, a Cooperative Low power mac protocol for WSNs. CL-MAC implements jointly Low Power Listening and cooperative communication. More precisely, we propose two variants of this protocol: a proactive version CL-MAC(P) and a reactive version CL-MAC(R). In order to evaluate the performances of the two proposed CL-MAC variants, we compare its to those of X-MAC. Simulation results proved that our protocol is able to enhance the use of the channel and to reach promising energy preservation especially in dense networks.

The proliferation of low power networks like Wireless Sensor Networks (WSN) rose up new challenges. Power conservation and channel quality become the most important parameters. Obviously, hop count based routing protocols are no more adapted to such networks having power limitations and channel problems. Several alternatives were suggested to cope with these constraints. In MAC layer for example, cooperative protocols were designed to enhance the channel use: the neighbor nodes help the source to retransmit its packets. However, if the path proposed by the routing protocol contains poor channels, the cooperative communications will not save all the packets. Therefore, the design of new routing protocol becomes compulsory. In this paper we propose ECAR, a routing protocol that optimizes two objectives at the same time: energy and Channel State Information (CSI). Compared to AODV, ECAR provides considerable enhancements in delivery ratio, end-to-end delay and power consumption.

Intelligent Transport Systems envision many applications relying only on vehicle-to-vehicle communications. Depending on the application (road safety, driver information, infotainment...), the requirements are different in terms of throughput, delay and loss rate. This paper explores the performances issues of a convoy of vehicles on the road, in order to estimate the capacity of such linear vehicular network. Among other results, we show that, while the loss rate is important, it is possible to rely on the vehicular network to relay informations issued from on-board sensors.

This paper presents a video demonstration of a platform for data dissemination for vehicular Networks developed by France Telecom R&D. The aim of this platform is to disseminate infotainment information (e.g. advertisement applications, etc.) in a predefined zone. This platform is based on an optimized dissemination protocol called ROD (Road Oriented Dissemination).

In this paper, we present the results from a detailed energy measurement study of different TCP variants when used in Mobile Ad hoc Network environments. More precisely, we focus on the node-level cost of the TCP protocol; also know as the computational energy cost. In fact, the computational energy consumption is the most important part of TCP energy consumption. This is already proven in previous work and our results confirm this fact. Sometimes, the computational energy cost is three times that of the communication energy cost. The studied TCP variants, in this work, are TCP New-Reno, Vegas, SACK, and Westwood. In our analysis, we draw a breakdown of the energy cost of the main congestion control algorithm (i.e. slow start, fast retransmit/fast recovery, and congestion avoidance) used by these TCP variants. The computational energy cost is studied using a hybrid approach, simulation/emulation, using the SEDLANE emulation tool. This study takes into consideration different data packet loss models (congestion, link loss, wireless signal loss, interference) within such environments when different ad-hoc routing protocols (reactive and proactive) are used. The performed study gives a set of results that are of high interest for future improvements of TCP in MANETs. Among the obtained results, we show that the computational energy cost of TCP varies according to the type of data packet loss model it comes through: network congestion, interference, link loss, or signal loss. The results demonstrate that the link loss scenario is the most severe situation for TCP connections to face. In addition to that, we show that the Fast Retransmit/Fast Recovery phase has much less energy cost than both Slow Start and Congestion Avoidance phases, due to the fact that it sends more TCP data bytes in a shorter period of time. Finally, the computational energy cost is quantified and compared to the TCP end to end performance for each TCP variant showing the link between both.

Cooperative relaying methods can improve wireless links, but introduce overhead due to relay selection and resource reservation compared to non-cooperative transmission. In order to be competitive, a cooperative relaying protocol must avoid or compensate for this overhead. In this paper, we present a MAC protocol for relay selection and cooperative communication as an extension to CSMA/CA which addresses resource reservation, relay selection, and cooperative transmission while keeping the overhead in terms of time and energy low. We discuss the efficiency of this protocol for packet error rate, throughput, and message delay in a multi-hop network. Simulation results show that the protocol performs similar and without noticeable overhead compared to standard CSMA/CA for good SNR while it is able to significantly improve throughput and reliability at larger distances.

Vehicular sensor networks (VSNs) are an emerging paradigm in vehicular networks. This new technology uses different kind of sensing devices available in new vehicles, to gather information about the driver's environment (speed, acceleration, temperature, seats occupations, etc.) in order to provide a safer, more efficient and more comfortable driving experience. In this paper, we focus on a particular VSN architecture, where the ad hoc network is operated by a telecommunication/service provider (WiMax access point, 2.5/3G base station) to combine non-valuable individual sensed data and extract from them effective feedbacks about the situation of the road in a geographical area (traffic density, unusual traffic behavior, etc.). In operated VSNs, providers tend to reduce the traffic load on their network, using unlicensed spectrum communication medium (IEEE 802.11p, for example). To do so, we propose CGP (Clustered Gathering Protocol), a cross layer protocol based on hierarchical and geographical data collection, aggregation and dissemination mechanisms. We analyze the performances of CGP using a simulation environment and realistic mobility models. We demonstrate the feasibility of such solution and show that CGP offers the operator precious information without overloading his network.

We address the drawbacks of cooperative relaying regarding its spectral inefficiency and its channel over reservation compared to direct transmissions. We propose to exploit routing information in cooperative relaying to reduce these disadvantages. In this paper we present a complete system design of multi-hop-aware cooperative relaying and investigate different relay selection policies for it. Performance tests indicate that it significantly outperforms hop-by-hop cooperative relaying in terms of throughput.

Supporting future large-scale vehicular networks is expected to require a combination of fixed roadside infrastructure and mobile in-vehicle technologies. The need for an infrastructure, however, considerably decreases the deployment area of VANET applications. In this paper, we propose a self-organizing mechanism to emulate a geo-localized virtual infrastructure (GVI). This latter is emulated by a bounded-size subset of vehicles currently populating the geographic region where the virtual infrastructure is to be deployed. An analytical model is proposed to study this mechanism. More precisely, this model is proposed to study the GVI in the frame of its main use: data dissemination in VANETs. Despite being simple, the proposed model can accurately predict the system performance such as the probability that a vehicle is informed, and the average number of duplicate messages received by a vehicle, and allows a careful investigation of the impact of vehicular traffic properties and system parameters on performance criteria. Analytical and simulation results show that the proposed GVI mechanism can periodically disseminate the data within an intersection area, efficiently utilize the limited bandwidth and ensure high delivery ratio.

In this paper, we present the results of a detailed measurement study of the computational energy cost of different TCP variants when used in mobile ad hoc networks. The studied TCP variants are TCP New-Reno, Vegas, SACK, and Westwood. We used a hybrid approach using simulation and emulation, through SEDLANE emulation tool. This study investigates different packet loss models (congestion, link loss, signal loss, interference) with different ad hoc routing protocols (reactive and proactive). The study demonstrates that the fast retransmit/fast recovery phase has a mush lower energy cost than both slow start and congestion avoidance phases, sending more TCP data bytes in a shorter period of time.

The concept of cooperative relaying promises gains in robustness and energy-efficiency in wireless networks. Although protocols for cooperative relay selection were proposed recently, their analysis was made without consideration of the energy required for receiving. Such an analysis is unfair, as relaying requires more receptions than direct source-destination transmission. We consider this lack of analysis and propose two refinements of cooperative relaying. Using ldquorelay selection on demand,rdquo relays are only selected if required by the destination. Using ldquoearly retreat,rdquo each potential relay assesses the channel state and decides whether to participate in the relay selection process or not. Simulation results show that these enhancements reduce the overall energy consumption significantly.

Vehicular networks require accurate channel state information (CSI) to decode the received signal. Such knowledge is usually obtained via a training sequence. However in vehicular networks, the channel coherence time is very small due to the high speeds of the nodes, therefore the channel estimate from the training is likely to become inaccurate as the decoding proceeds. Using shorter packets can improve the performance at the cost of increased overhead. In this paper we introduce a novel channel tracking algorithm for VBLAST in vehicular networks with relatively little change in the overhead. The algorithm uses first order Kalman filters therefore it has less complexity than available tracking algorithms. The algorithm uses the detected symbols and received signal after the interference cancellation and detection processes of the VBLAST decoder to improve the channel estimation. Simulation results show considerable improvement in mean square error (MSE) and BER when using this algorithm compared to channel estimation by training only with small increase in hardware complexity.

Majority of characterization studies on Vehicular Networks are based on simulations that assume little network errors and consistent signal strength. Existing VANET experiments, on the other hand, focus mostly on single-hop communication. In this paper, we present the results of an extensive measurement campaign involving up to six vehicles and evaluating the performance of IEEE 802.11 in different vehicular communication scenarios: vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V). We concentrate our evaluation on multi-hop communication in these two scenarios. We found that distance and line of sight communication are the two main factors affecting the multi-hop inter-vehicle communication. The experimental results confirm also the feasibility of using ad hoc networks to extend the transmission range of the infrastructure and the connection time for cars in motion.

The fundamental component for the success of VANET (vehicular ad hoc networks) applications is routing since it must efficiently handle rapid topology changes and a fragmented network. Current MANET (mobile ad hoc networks) routing protocols fail to fully address these specific needs especially in a city environments (nodes distribution, constrained but high mobility patterns, signal transmissions blocked by obstacles, etc.). In our current work, we propose an inter-vehicle ad-hoc routing protocol called GyTAR (improved greedy traffic aware routing protocol) suitable for city environments. GyTAR consists of two modules: (i) dynamic selection of the junctions through which a packet must pass to reach its destination, and (ii) an improved greedy strategy used to forward packets between two junctions. In this paper, we give detailed description of our approach and present its added value compared to other existing vehicular routing protocols. Simulation results show significant performance improvement in terms of packet delivery ratio, end-to-end delay, and routing overhead.

Ad hoc networks have gained place in the research area recently. There are many researches regarding different topics related to such networks, such as routing, media access control, security, scalability, and many others. There are two usual methods to test and evaluate ad hoc networks performance: simulations and real test-beds. A network emulator is a tradeoff between pure simulations and real test-beds. Here, we propose a multi-hop wireless ad hoc network emulator that uses the advantages of network simulator (NS-2) and traffic shaping tool (DummyNet), that we called SEDLANE [simple emulation of delay and loss for ad hoc networks environment]. SEDLANE is a network emulator that is based on TCP behaviour characteristics. Using SEDLANE, we can emulate a whole multihop ad hoc network through the data packet loss and round trip time (RTT) values over the TCP connection. SEDLANE helps testing and evaluating ad hoc network protocols using very simple and inexpensive test-bed configuration. The results confirm that; introducing SEDLANE within a simple configuration network (possibly 2 nodes) gives exactly the same results as those obtained when simulating such networks.

A key component of intelligent transportation systems (ITS) is the provision of adequate network infrastructure to support vehicular communication. In this paper we present the results of an extensive measurement campaign evaluating the performance of IEEE 802.11 in different vehicular communication scenarios: vehicle-to-vehicle (V2V) and infrastructure- to-vehicle (I2V). We concentrate our evaluation on multi-hop communication in these two scenarios. We found that distance and line of sight communication are the two main factors affecting the network communication. The experimental results confirm also the feasibility of using ad hoc networks to extend the transmission range of the infrastructure and the connection time for cars in motion.

In this paper we propose two algorithms for efficient data-centric storage in wireless sensor networks without the support of any location information system. These algorithms are intended to be applied in environments with large number of sensors where the scalability of the network has great issue. During the first algorithm, each sensor obtains a unique temporary address according to its current relative location in the network. The second algorithm is used to route data from one sensor to another, this routing algorithm only depends on the sensor's neighborhood, i.e. in order to implement the routing table each sensor needs only to exchange local information with its first hop neighbors. The forwarding process used in this algorithm resembles the one found in Pastry peer-to-peer protocol.

Test-bed based research is an important aspect in mobile ad hoc networks (MANETs) and especially in vehicular ad hoc networks (VANETs). However because of the high associated cost, we do not see as many high quality experiment studies as simulation or analysis ones. In this paper, we present extensive experimental measurements of vehicle-to-vehicle communication on several typical scenarios in order to study the critical factors that affect multimedia applications over an IEEE 802.11 VANET. Unlike [4], we proof that in addition to short message transfer, the deployment of multimedia applications is also feasible. We found that network performances are clearly different according to the environment (city, highway, etc.), the distance between vehicles and the vehicles speed.

Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic information processing. This has widespread applications in the context of vehicular networks. The existing centralized approaches for traffic estimation are characterized by longer response times. They are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and infrastructure-free mechanism for road density estimation. The proposed solution is adaptive and scalable and targets city traffic environments. The approach is based on the distributed exchange and maintenance of traffic information between vehicles traversing the routes. The performance analysis of the proposed mechanism shows the accuracy of the algorithm for different traffic densities. It also gives insights into the promptness of information delivery in the mechanism based on delay analysis at road intersections. This promptness is a necessary condition to various applications requiring reliable decision making based on road traffic awareness.

In this paper we study a self organizing network architecture, party. Party is a new routing protocol intended to be applied in environments with large number of nodes where the scalability of the routing protocol plays an important role. Party's routing is unique and only depends on the current node's neighborhood. Routing tables are created on the basis of the first hop neighborhood only. We will show the protocol performance with a large number of nodes in the network, and compare it to the legacy ad hoc routing protocols. Results show a large improvement in terms of overhead and throughput.

TCP was mainly developed to be implemented within wired networks where the main cause for packet loss is network congestion. Conversely, in wireless ad hoc networks there are many other reasons to lose packets (such as fading, interference, multi-path routing, etc.). Reacting to these various loss types, TCP triggers its congestion control algorithm (i.e. considering all these losses as due to congestion). This, in some cases, might be an aggressive reaction leading to network performance degradation. On the other hand, since ad hoc nodes are battery operated, they need to be energy conserving so that battery life is maximized. In our work we analyzed the effect of TCP variants' congestion control algorithms on TCP performance (energy consumption and average goodput) in ad hoc networks. The study takes into consideration the different loss types that may occur in ad hoc environment; aiming to find the best adapted TCP variant for such networks. We intend to use the results of our current work to set design guidelines for specific TCP enhancements suited for ad hoc networks.

The client-server model is a commonly used model for distributed application programming. Most of group-collaboration applications, such as network gaming, rely on this model. We call a group-collaboration application an application where any participating entity can centralize the shared information and play the role of server. In wired networks, the choice of the participating entity playing the server role has a limited impact on the performances of the application, the station or the network. However, in mobile ad hoc networks (MANETs) an inadequate server choice can have a side effect on the network-, the application- or the wireless station-performances. The objective of our current work is to propose a novel and complete framework for server election and maintenance in ad hoc networks. This framework, called P-SEAN for policy-based server election in ad hoc networks, uses two concepts in order to perform the server election process: the serving ability degree and the situational server-election policy. Hence, the proposed framework implements the different situations for server election and maintenance in ad hoc networks as policy-rules (situational server-election policy). This election and maintenance are mainly based on factors such as connectivity, processing power, RAM capacity, remaining battery life, etc. These factors define the serving ability degree of each ad hoc node. The motivation behind using the policy-based networking paradigm is to render our framework extensible to incorporate additional application-specific criteria. In addition to these two main concepts, we also propose a complete architecture for a P-SEAN-enabled service and a lightweight protocol for server election and maintenance exchanges

TCP was mainly developed to be deployed within wired networks. Recently, many researches have studied its performance within mobile ad hoc networks (MANETs). These researches found that TCP performance are highly influenced by the characteristics of such networks. This is due to TCP's reliability mechanism and its inability to discriminate the packet loss cause. Indeed, unlike wired networks, where packet loss is mainly caused by network congestion, in MANETs we could have many other reasons to lose a data packet. However, TCP considers that all packet losses are due to network congestion. This enforces TCP to be aggressive in front of certain types of loss. Packet losses in MANETs can be either related to wireless communication environment (i.e. the effect of fading, interference, multipath routing, etc.) or to the dynamic nature of such networks (i.e. link failures, network partitioning). This latter could be due to the node mobility or to the node battery depletion. This could lead to frequent route re-computation within the network. In this work, we intend to study the effect of ad hoc routing protocols on TCP performance (energy consumption and average goodput) within MANETs. We consider studying different types of ad hoc routing protocols having different characteristics: reactive vs. proactive, distance vector vs. link state, and source routing. Our study results show that; DSDV as a proactive distance vector routing protocol leads to most accepted TCP performance results and this is confirmed at different mobility levels

Ad hoc wireless networks are power constrained since nodes operate with limited battery energy. Thus, energy consumption is crucial in the design of new ad hoc routing protocols. To design such protocols, we have to look away from the traditional minimum, hop routing schemes. In this, paper, we propose three extensions to the state-of-the-art shortest-cost routing algorithm, AODV. The discovery mechanism in these extensions (LEAR-AODV, PAR-AODV, and LPR-AODV) uses energy consumption as a routing metric. They reduce the energy consumption of the nodes by routing packets to their destination using energy-optimal routes. We show that these algorithms improve the network survivability by maintaining the network connectivity. They carry out this objective with low overhead and without affecting the other wireless network protocol layers.