Micromobility, encompassing low-speed transport modes such as e-scooters and bicycles, plays a pivotal role in enhancing urban mobility within the Intelligent Transportation System (ITS). For such an environment, a multitude of challenges arises, including optimizing system architecture and managing energy consumption efficiently. This paper presents an innovative approach aimed at improving micromobility integration within an Internet of Vehicles (IoV) ecosystem by leveraging Vehicular Adhoc Networks (VANETs) and IEEE 802.11p communication standard. To reduce energy consumption while maintaining costeffective infrastructure, we propose an energy-saving strategy that optimizes the operation by introducing mobile RSUs (mRSUs) with dynamic and adaptive states called (”ON/OFF”). Furthermore, we introduce a k-Nearest Neighbors (KNN) algorithm, leveraging Machine Learning (ML), to predict the optimal speed of these mRSUs, enhancing their efficiency and adaptability. The effectiveness of these approaches is rigorously evaluated based on the activation time of the mRSU, the total energy consumption metric, and using a meta-analysis method.

This paper introduces AHARP (Adaptive Hybrid Agent-based Routing Protocol), a novel routing protocol designed for the Internet of Vehicles (IoV). AHARP integrates reactive, proactive, and context-aware routing strategies with an enhanced dynamic clustering mechanism to address the challenges of high mobility, frequent topology changes, and resource constraints in vehicular networks. The protocol dynamically groups vehicles based on their geographic proximity, speed, and road type, ensuring efficient cluster formation and management. Extensive simulations demonstrate that AHARP significantly outperforms existing protocols in terms of packet delivery ratio (PDR), end-to-end delay, and location service efficiency. By reducing control message overhead, improving route discovery efficiency, and adapting to dynamic network conditions in real-time, AHARP provides a robust and scalable solution for high-mobility environments.

Shared electric micro-mobility, as an emerging mode of urban transportation, has been booming worldwide in recent years. Although it provides sustainable, eco-friendly, and cost-effective mobility, it also faces several challenges, particularly due to existing inefficient feet management strategies. These typically rely on fixed redistribution schedules that fail to adapt to highly dynamic user demand and overlook practical factors such as time-varying patterns and charging requirements. To address this problem, this paper presents a user-involved deep reinforcement learning framework for real-time feet management of shared electric micro-mobility, utilizing the Soft Actor-Critic network. It ensures system balancing through dynamic redistribution, taking into account charging constraints while reducing costs by aggregating discharged micro-vehicles. Our framework is demand-aware, incorporating predicted user demand through online learning using an LSTM network, thereby ensuring effective system balancing that meets user needs. Experimental results demonstrate the effectiveness of the proposed framework, showing robust performance for real-time applications.

Micromobility vehicles such as electric scooters and bikes represent an emerging technology that offers many benefits. To ensure the efficient integration of these vehicles into Cooperative Intelligent Transport Systems (C-ITS), it is necessary to meet the stringent requirements of V2X communication technologies, which demand ultra-reliable and low-latency to guarantee safety and seamless connectivity. V2X connections can be based on ETSI ITS-G5 or Cellular communication (C-V2X) incorporating fifth generation (5G) network. In this paper, we propose a hybrid communication architecture tailored for micromobility vehicles that leverages the benefit of each Radio Access Technology (RAT). We propose a novel decentralized RAT selection strategy based on Deep Reinforcement Learning (DRL), integrating a Double Deep Q-Network (DDQN) algorithm. The proposed approach enables each micromobility vehicle to dynamically select the optimal RAT combination that maximizes communication reliability and packet delivery ratio (PDR) while optimizing resource usage. Performance evaluations conducted under a real city scenario in Bordeaux, France, with 100 micromobility vehicles demonstrate that the proposed hybrid system achieves up to 99% PDR compared to static RAT selection strategies, which confirms the effectiveness of the DDQN-driven approach in enhancing communication performance for emerging micromobility-based V2X applications.

Individuals with reduced mobility rely on accurate information to navigate transit systems effectively. Proactive notifications about Train Schedule Variability (TSV) can significantly enhance their travel experience by providing timely alerts on potential delays. This paper introduces a three-layer Long Short-Term Memory (LSTM) model to predict TSV and notify reduced-mobility individuals through a Human-Machine Interface (HMI) system integrated into their wheelchairs. Our model estimates a Variability Index (VI) over 7, 15, and 30-day windows within a hierarchical architecture spanning edge, fog, and cloud layers. The edge layer delivers advance notifications, alerting users up to 7 days prior to anticipated delays. A Hierarchical Clustering Approach (HCA) is applied for robust data preparation, addressing missing values and identifying weekly patterns in a one-year dataset of train schedules from Frankfurt Station. This dataset, inclusive of seasonal and weather variability, undergoes additional processing with median filters and time-sliding windows to create data matrices. We evaluate the proposed three-layer LSTM model against multiple LSTM configurations and traditional time series forecasting models, including AutoRegressive Integrated Moving Average (ARIMA), Kalman Filtering, and an ARIMA-Kalman hybrid. Model performance is assessed using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Additionally, results demonstrate that the Variability Assignment Score (VAS) surpasses Mean Squared Error (MSE) in fault detection accuracy. Receiver Operating Characteristic (ROC) curve analysis, using Area Under the Curve (AUC) metrics, validates the superior performance of our model. Specifically, the VAS-based ROC curve for the three-layer LSTM model outperforms others, underscoring VAS as a more effective measure for variability detection. Further validation via the Diebold-Mariano (DM) test confirms the suitability of the three-layer LSTM model for this application.

A hybrid artificial intelligence (Hybrid AI) which represents a convergence of a classical (symbolic) AI with recent machine learning approaches has become a very quickly developing research axis. The combination of rule-based reasoning and statistical learning is required whenever the domain knowledge has to be incorporated in the decision system. In this work we present a system on the basis of Deep Neural Networks (DNNs) as object detectors, such as You Only Look Once version 8 (YOLOv8), transformers and logical rules which link objects and their context in the problem of rail line defect detection. Fusion of information is performed at the intermediate level - in the feature space, mixing sets of elements of this space delimited due to the object and context element detectors. Combination of objects and context elements is performed accordingly to the domain-defined rules, and fusion is ensured by a vision transformer. Experiments have been conducted on the domainrecorded dataset of rail defects. The proposed hybrid system outperforms base-line objects detection up to 0.28 of accuracy increase.

Micro-mobility, such as electric scooters and bicycles, has set a trend in recent years. This low-speed mode of transport, widespread and getting connected, represents a significant paradigm shift in contemporary transportation dynamics. To integrate these low-speed types of transport within the Intelligent Transport System (ITS), the paper proposes the use of the Vehicular Ad-Hoc Network (VANET) communication model within the Internet of Vehicles (IoV) using the IEEE 802.11p short-range communications standard. We propose a geofencing model that allows an optimal control of micromobility entities speeds in limited geographical areas and we simulate several use cases which can take place within our geofencing model. In addition, an energy saving approach is proposed given the presence of several roadside units (RSUs) in geofencing zones.

The user experience is an important aspect of micro-mobility fleet operations, and placing micro-vehicles in a suitable and optimized manner is a key element to enhancing user service. This paper aims to establish an effective methodology for optimizing shared micro-mobility rebalance operations through spatio-temporal prediction of user demand in dock-based systems. It is based on forecasting the occupancy levels of each station to avoid completely empty and jammed stations in the future, ensuring service availability throughout the day. Since the processed data is local, we employ edge computing, yielding a scalable solution that minimizes latency and enhances reliability, making it suitable for urban environments with fluctuating conditions. The results demonstrate significant improvements in service availability, validating the efficiency of our edge-adapted prediction model for dock-based micro-mobility fleets.

Toll management systems have a vital role in facilitating smooth and effective traffic flow at toll stations. To this end, numerous use cases have been explored to guide and detect vehicles at toll stations. This paper presents the results of a study conducted on a testbed as part of the European project InDiD. By utilizing more robust technologies, our work aims to enhance a specific use case explored within this project. Leveraging the resources of the APRR (Autoroute Paris-Rhin-Rhône enhanced) Cooperative Intelligent Transportation System (C-ITS) testbed, we did two experiments to assess the use of two technologies in improving vehicles orientation while approaching a toll station. The first one featured an upgraded vehicular communication network, incorporating both ITS-G5 and LTE-UU to improve the reliability of the vehicular communications. The subsequent one integrated computational power directly onto the vehicle (vMEC) to compare it with the use of an edge computing resource. The results show an improvement in reliability, a coverage extension, and a favorable latency tradeoff when using the vMEC. Additionally, we closely monitored the downlink resource consumption while employing hybrid communication.

Multiaccess edge computing (MEC) has emerged as a promising technology for time-sensitive and computation-intensive tasks. However, user mobility, particularly in vehicular networks, and limited coverage of Edge Server result in service interruptions and a decrease in Quality of Service (QoS). Service migration has the potential to effectively resolve this issue. In this paper, we investigate the problem of service migration in a MEC-enabled vehicular network to minimize the total service latency and migration cost. To this end, we formulate the service migration problem as a Markov decision process (MDP). We present novel contributions by providing optimal adaptive migration strategies which consider vehicle mobility, server load, and different service profiles. We solve the problem using the Double Deep Q-network algorithm (DDQN). Simulation results show that the proposed DDQN scheme achieves a better tradeoff between latency and migration cost compared with other approaches.

Cooperative intelligent transportation systems are now being widely investigated along with the emergence of vehicular communication. Services such as collective perception require a robust communication system with high throughput and reliability. However, a single communication technology is unlikely to support the required throughput, especially under mobility and coverage constraints. Thus, we propose in this paper a hybrid vehicular communication architecture that leverages multiple Radio Access Technologies (RATs) to enhance the communication throughput. We developed a Deep Reinforcement Learning (DRL) algorithm to select the optimal hybrid transmission strategy according to the channel quality parameters. We assess the effectiveness of our hybrid transmission strategy by a simulation scenario that shows about 20% throughput enhancement and a 10% reduction of channel busy ratio.

Recent advancements in autonomous vehicle perception haveexposed limitations of onboard sensors such as radar, lidar, and cameras, which road obstacles and adverse weather conditions can impede. Connected and Autonomous Vehicles (CAVs) are leveraging wireless communications to share perception information through a process called Cooperative Perception (CP), aiming to provide a more comprehensive understanding of their environment. However, this can result in excessive redundant and useless information in the network, as the same road objects may be detected and exchanged simultaneously by multiple CAVs. This not only consumes more network resources but also may overload the communication channel, reducing the delivery of perception information to CAVs and ultimately decreasing the overall CP awareness in the network. This paper introduces MCORM, a multi-agent learning method based on the advantage actor-critic algorithm to maximize object usefulness and reduce redundancy in the network. Our evaluations demonstrate that through this method, CAVs learn optimal CP message content selection policies that maximize usefulness. Further more, our proposal proves to be more effective in mitigating object redundancy and improving network reliability in comparison to existing approaches.

With an increasing number of vehicles equipped with ACC (Adaptive Cruise Control), the impact of such vehicles on the traffic flow becomes significant. Especially in the zones where traffic safety is a concern. Here, we can mention roads where traffic is characterized by its "stop and go" particularity. One of the widely used and well-known algorithms to serve as the basis for a control algorithm of a real-world ACC system is the IDM (Intelligent Driver Model). This contribution analyzes the IDM, a CFM (Car-Following Model) governed by a system of differential equations. The model, in recent years, has been intensively studied for capturing traffic phenomena properly and making driver braking behavior safer. Despite the intensive analyses, to our knowledge, a rigorous study of comfortable braking, when an emergency occurs, has rarely been performed. Especially when vehicles' densities and speeds are in constant variation and when, in most situations, urgent braking is necessary. In this work, we modify the classic IDM to introduce the O-IDM (Optimized-Intelligent Driver Model). We increase safety distances between vehicles to prevent accident risks without permitting vehicles' velocities to become negative or to diverge to -∞ in finite time. We personalize IDM parameters for each type of vehicle and we aim to make the transition of the traffic flow from "free flow" to "stop and go" traffic more comfortable. We compare the obtained O-IDM results to the Krauss-FM (Krauss-Following Model) and to the classic IDM to prove the efficiency of our approach.

Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high reliability, low end-to-end (E2E) latency, and high throughput. However, present V2X communication technologies such as ITS-G5 and C-V2X (Cellular V2X) cannot satisfy these requirements alone. In this paper, we propose an intelligent, scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs) to meet the needs of these applications. Then, we propose a communication mode selection algorithm based on Deep Reinforcement Learning (DRL) to maximize the network's reliability while limiting resource consumption. Finally, we assess our work using the platooning scenario that requires high reliability. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, it improves the efficiency of the redundant communication mode by 20% regarding resource consumption.

The use of Cooperative Perception (CP) enables Connected and Autonomous Vehicles (CAVs) to exchange objects perceived from onboard sensors (e.g., radars, lidars, and cameras) with other CAVs via CP messages (CPMs) through Vehicle-to-Vehicle (V2V) communication technologies. However, the same objects in the driving environment may simultaneously appear in the line of sight of multiple CAVs. Consequently, this leads to much irrelevant and redundant information being exchanged in the V2V network. This overloads the communication channel and reduces the CPM delivery to CAVs, thereby decreasing CP awareness. To address this issue, we mathematically formulate CP information usefulness as a maximization problem in a multi-CAV environment and introduce a distributed multi-agent deep reinforcement learning approach based on the double deep Q-learning algorithm to solve it. This approach allows each CAV to learn an optimal CPM content selection policy that maximizes the usefulness of surrounding CAVs as much as possible to reduce redundancy in the V2V network. Simulation results highlight that the proposal effectively mitigates object redundancy and improves network reliability, ensuring increased awareness at short and medium distances of less than 200 m compared to state-of-the-art approaches.

Emerging V2X applications, like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) require Ultra-Reliable Low Latency Communications (URLLC). Unfortunately, none of the existing V2X communication technologies, such as ETSI ITS-G5 or C-V2X (Cellular V2X including 5G NR), can satisfy these requirements independently. In this paper, we propose a scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs). To this purpose, we propose a novel ITS station protocol stack and a decentralized RAT selection strategy that uses Deep Reinforcement Learning (DRL). The proposed approach employs a double deep Q-learning (DDQN) algorithm that allows each vehicle to determine the optimal RAT combination to meet the specific needs of the V2X application while limiting resource consumption and channel load. Furthermore, we assess the ability of our architecture to offer reliable and high throughput communication in two different scenarios with varying traffic flow densities. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, the selection strategy exhibits about a 20% improvement in throughput and a 10% reduction in the channel busy ratio (CBR).

The automotive market is undergoing revolutionary technological transformations, leading to today’s "Cooperative Intelligent Transport System" or C-ITS. This latter raises challenging demands in terms of coverage and connectivity to ensure ultra-low latency and ultra-high reliability under high mobility and density conditions. This work focuses on the C-ITS standardized by ETSI, and whose access network relies on the ITS-G5 technology to achieve V2X services. This paper proposes an ITS-G5 RAN slicing architecture that integrates new modules via the ITS protocol stack to create multiple RAN slices with different priorities and provide secure access to a given slice while performing traffic shaping and policing as well as traffic prioritization and isolation between different slices.

Cooperative Perception (CP) allows Connected and Autonomous Vehicles (CAVs) to enhance their Environmental Awareness (EA) by sharing locally perceived objects through CP messages (CPMs). European Telecommunications Standards Institute (ETSI) has recently defined a set of CPM generation rules to achieve a trade-off between EA and Channel Busy Ratio (CBR) despite massive perception data. Nonetheless, these rules still lack the consideration of information usefulness, resulting in a considerable volume of useless information transmitted in the CP network. This limitation could increase CBR and thus decrease EA due to the loss of CPMs in the network. This paper introduces CloudAC-IU, a cloud-based deep reinforcement learning approach to lean CAVs to maximize perception information usefulness in the network. Simulation results highlight that the CloudAC-IU enhances EA by decreasing CBR and increasing CPM reception for CAVs compared to state-of-the-art works.

Vehicular applications in smart cities, such as assisted and autonomous driving, require sophisticated data processing, low latency, and high throughput data transmission. Edge Computing is a leading approach designed to meet those application requirements. By deploying Edge servers at the network's edge, close to the vehicles, such applications can be successfully delivered while adhering to low-latency and high-throughput requirements. However, optimal placement of Edge servers is challenging since it necessitates a trade-off between quality of service and deployment cost. Latency can be reduced by placing as many Edge servers as feasible close to the vehicles, however, this results in significant deployment costs. This work addresses the problem of optimal Edge server placement. It solves this problem using integer linear programming, considering the relation between delay and cost, as well as the capacity of Edge servers in realistic road traffic scenarios. The proposed generic methodology is designed to reduce the cost of deploying Edge servers by combining the achievement of the desired latency threshold with workload balancing between Edge servers. We evaluate the efficiency of the proposed solution mathematically and through simulations based on open data from real vehicles traffic on roadways of Bordeaux, France. The obtained results demonstrate that our solution outperforms existing Edge server placement approaches, especially on workload balancing.

Vehicle density and channel utilization can vary significantly over short time intervals in complex and dynamic environments such as vehicular networks. In such situations, the European ITS-G5 technology proposed Decentralized Congestion Control (DCC) techniques, such as the reactive Transmit Rate Control (TRC) and the adaptive Dual α algorithms, to prevent performance degradation. This paper proposes a proactive DCC technique that uses a Recurrent Neural Network (RNN), namely the Long Short-Term Memory (LSTM), to optimize the channel performance. We specifically use smoothed Channel Busy Ratio (CBR) forecasting time series calculated using an LSTM agent and provided as inputs to DCC algorithms to achieve faster channel load convergence and, as a result, improve network stability, channel load limitation, and resource allocation fairness. According to the simulation results, the proposed proactive DCC algorithms outperform existing algorithms in terms of reduction in average channel load and faster channel convergence.

Modern vehicles contain already connected devices allowing them to interact with the road infrastructure and with other vehicles. The goal of this interaction is to share information and to improve road safety, traffic efficiency and driving comfort. The resulting system, called cooperative intelligent transport system (C-ITS), involves vehicles, infrastructure, and traffic management. Vehicles and infrastructure in development use a combination of cameras, sensors, GPS, radar, LiDAR, and on-board computers. These technologies work together to map the vehicle's position and its proximity to everything around it. Because of their reliance on these sorts of technologies, which are easily accessible to tampering, a driving system in this environment is susceptible to cyberattacks if an attacker can discover a weakness in a certain type of vehicle, infrastructure or in a company's electronic system. Since this environment is a critical system, these attacks can lead to disasters that eventually cost lives. After reviewing the state-of-the-art of C-ITS, examples of road safety using digital technologies will be presented, and the focus will deal with cybersecurity threats for connected vehicles. Such threats may concern the inside components of the vehicle or the communications between vehicles. Main challenges and future research directions will be discussed.

Summary Due to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.

Summary As sensor nodes are power constrained, saving energy and prolonging network lifetime have been given the greatest priority in the design of routing protocols in wireless sensor networks (WSNs). In this regard, duty‐cycling is broadly utilized as an underlying MAC (Medium Access Control)‐based protocol for routing solutions. In this regard, a cross‐layer approach, integrating MAC and routing protocols, is required to achieve a trade‐off between energy efficiency and communication reliability, through adapting nodes duty‐cycle to routing decisions. Besides, with the proliferation of WSNs applications in various domains, achieving energy efficiency should not be performed while ignoring the diverse quality of service (QoS) demands of the considered applications to ensure their well functioning. In this context, we propose a context‐aware routing framework under duty‐cycled networks, a generic framework that allows to support heterogeneous applications and traffic patterns. An evaluation of the framework is also proposed in this paper. Results prove the effectiveness of context‐awareness and the cross‐layer interaction between the different modules of the framework to guarantee desired QoS while reducing energy consumption.

Summary The specific characteristics of vehicular ad hoc networks, such as high speed of nodes and frequent topology changes, impose challenges for the routing process. In addition, the advent of the Internet of Vehicles (IoV) concept along with the autonomous and connected cars contribute to the proliferation of new innovative applications with different quality of service requirements, rising new challenging issues for data transfer. In this article, we review the different taxonomies of vehicular routing protocols, while giving an insight into the design of geographical protocols. Then, we outline several optimization techniques and paradigms used to enhance the routing process. Moreover, in order to promote the deployment of robust IoV routing protocols at large scale, we provide some directions for future research work.

Summary Wireless sensor networks (WSNs) are composed of sensors exchanging the information that they collect from the environment. The use of a scheduler offers an efficient solution to eliminate information redundancy and possible collisions in this network. A scheduler is responsible for choosing the sensors to exchange information at each step of the algorithm's execution. This article presents a WSN handshake algorithm (WSN‐HS) scheduling the communications between every two sensors safely in an exclusive mode. Our WSN‐HS is energy‐efficient. It tries to elaborate communications between sensors with the minimum of messages and thus with the minimum of energy consumption. In addition, our algorithm is fault‐tolerant to sensors' disappearance. Hence, when a sensor runs out of energy, the other sensors in the network will not be blocked and they will continue executing the distributed algorithm. We compare and evaluate our algorithm with another handshake algorithm. The results of the simulation done with two examples of distributed algorithms show that our algorithm significantly minimizes the energy consumption in the WSN. Moreover, in this article, we detail the analysis that emphasizes the efficiency of our WSN‐HS algorithm.

Cooperative Intelligent Transport Systems (C-ITS) are built based on vehicular communication technologies with real-time information exchange to improve overall traffic management such as road safety, efficiency, and comfort. Road safety depends on several factors that should be considered, among which the reliability of the used in-vehicle awareness systems. Therefore, communication technologies are rapidly evolving, and New Radio Access Technologies (RATs) are emerging to provide enhanced performances in terms of reliability, coverage, and throughput. C-ITS can benefit from these new RATs enhancements to allow new use cases and applications and then prevent additional road accidents. In this context, the IEEE 802.11bd and cellular vehicle-to-everything (C-V2X) technologies such as LTE-V2X and NR V2X are being developed. This advance is fundamentally reshaping the C-ITS landscape as both technologies (IEEE 802.11bd and LTE-V2X) are competing. In this paper, we analyze how these technologies help increase road safety by making communication more reliable. Furthermore, we will compare their performance using our IEEE 802.11bd implementation in OMNeT++ in terms of packet reception ratio. Finally, we will forecast the number of avoided serious injuries on the European roads.

Cooperative Intelligent Transport Systems (C-ITS) are becoming indispensable in improving road safety and traffic management. ITS-G5 is the European standard for vehicle to everything (V2X) communications. With advanced V2X applications and use cases, the need for enhanced communication performance and broader perception of the road becomes crucial, particularly in congested areas, where the communication performance decreases significantly. Network slicing is a new network paradigm for next-generation networks. It has proved useful to guarantee the quality-of-service requirements of various applications. This paper presents an end-to-end network slicing for the ITS-G5 vehicular communications where slices' resources are orchestrated on all the ITS infrastructure, including the ITS-G5 radio access network. The proposed slicing mechanism aims to provide enhanced end-to-end (E2E) latency for specific traffic types and user groups. We demonstrate the slicing mechanism's effectiveness on two different priority groups carried in two different slices (i.e., lower-priority slice and higher-priority slice). Simulation results show a significant improvement of the E2E latency for users of the higher-priority slice.

Cooperative Intelligent Transportation Systems (C-ITS) are being developed to enable information exchange among vehicles and between vehicles and the roadside infrastructure. C-ITS are regarded as a base technology to reduce road accidents and improve traffic efficiency sustainably. Driving in unsignalized intersections is a significant problem for drivers due to many accidents registered on such roads. In this paper, we aim to solve this problem by proposing an Intersection Assistance System (IAS) that helps C-ITS drivers crossing the intersection more safely. Our solution, namely ESIAS (Edge-based Safety Intersection Assistance System), uses an edge-computing architecture to process efficiently the data offloaded by sensors (cameras/lidars) installed on road intersections. Our solution's performance is evaluated through different simulation scenarios by calculating the number of avoided accidents in each scenario. The results show that ESIAS can effectively reduce 80% of potential accidents.

Broadcasting emergency messages has been widely recognized as an important area of research. A crucial issue, in this context, is to ensure the correctness of broadcasting protocols using a formal method to prevent errors before their implementation. In this paper, we propose a new protocol for broadcasting emergency messages in order to inform persons in case of an unexpected situation occurrence. Our protocol consists in applying a clustering mechanism before proceeding with the broadcasting phase. This mechanism is one of the most efficient techniques used in a large-scale network. We formally verify the proposed protocol using the Event-B formal method which supports an incremental development based on the refinement technique.

As Internet of Things (IoT) is widely spread and is becoming heterogeneous, a growing number of connected devices are the focus of security threats. Hence, a standardized security strategy seems required. OneM2M [1] is a global standard initiative designed to satisfy the need for a common horizontal platform for the multi-industry M2M/IoT applications. In this paper, we propose an Intrusion Detection and Prevention System (IDPS), for the Service Layer introduced by the oneM2M standard. To our knowledge, it is the first generic IDPS for the oneM2M Service Layer based on Edge Machine Leaning (ML). We will detail, in this work, the strategy of the oneM2M-IDPS. Moreover, we investigate the performance of ML algorithms on the oneM2M generated dataset to choose the best ones for our IDPS. Since we are in the context of tiny devices (IoT), we pay attention in our experiments to the features dimension reduction in ML and thus, to the size of trained models.

Cut-in into vehicle platoons is a dangerous driving maneuver that affects the safety and efficiency of platooning vehicles. An accurate prediction of such maneuver enables the platooning system to take safety measures that ensure the platoon safety and integrity. The contribution of this paper consists of an evaluation of a set of supervised machine learning algorithms for cut-in maneuver recognition, for eventual use in platooning systems. The models were trained and tested on a large-scale publicly available driving dataset, from which cut-in events were extracted. The results show that tree-based classifiers such as Gradient Booting Machine can recognize the cut-in maneuvers with an Fl-score of 98%. An experiment to investigate the model performance with advanced prediction times shows that up to 80.5% of the cut-ins were correctly predicted 1 second before the lane crossing time.

Summary In wireless sensor networks (WSN), Intrusion Detection through sensor monitoring enables the detection of intricate security attacks such as denial of service and routing attacks. However, the monitoring process, which observes sensor behaviour, can induce a large amount of energy and bandwidth overhead, thus reducing the system lifetime. Furthermore, monitors can be attacked and their observations corrupted, which breaks the system security. Therefore, our goal is the design of a reliable and energy‐efficient monitoring system that resists internal security attacks. We present DAMS (Distributed and Adaptable Monitoring System) that extends LEACH protocol with a trust‐based and energy‐efficient clustering protocols for both cluster heads and monitors election. Compared to other LEACH extensions, the obtained topology reduces monitoring energy overhead, improves the system lifetime, and increases the packet delivery ratio.

Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of “N” active instances called replicas while redundancy provides “P” standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service Function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.

Vehicular ad-hoc networks (VANETs) technology has become an active research area over the last few years. It has a huge potential to improve traffic efficiency, road safety as well as comfort to both passengers and drivers. In this context, one of the most difficult challenges is to ensure that protocols used in VANETs operate properly as expected and do not cause any inconsistencies. In this paper, we follow the guidelines of systematic literature reviews (SLR) to provide a comparison of the existing approaches formally verifying the correctness of VANETs. We introduce a taxonomy of the proposed solutions and we discuss their goals, limits, verification techniques, etc. We conclude the paper with some research challenges of VANETs that still need to be addressed. So, throughout this present paper, we provide information for researchers and developers to understand the contributions and challenges of the existing studies to pave the way for improving their solution.

Broadcasting official and emergency information is seen as a principle role for controlling crisis situations. In fact, it has a great importance to ensure the communication between network nodes (vehicles, passengers, etc.). This paper presents a survey that examines the existing studies of broadcasting protocols. Our survey follows the guidelines of systematic literature reviews (SLR). It provides a comparison of the existing approaches based on some criteria such as communication technologies and simulation tools. Finally, we highlight some recommendations and possible future researches which need further investigations.

Internet of Vehicles (IoV) constitutes an important part of the Smart Cities concept, relying on different technologies and including heterogeneous cars types, which raises challenges to ensure and preserve road safety. Throughout this paper, we tackled the data collection and transmission problems related to the safety in the terrestrial domain by identifying the car-types and exposing the different communication data levels and means used in the quest of safety realization.

Wireless Body Area Networks are nowadays attracting both academic and industrial worlds. Combining collected data related to patient context with original health measurement can enhance the general health state monitoring and help to better understand the patient disease evolution. Daily activity is one of the important features that may influence the patient health state. Thus, recognizing the user activity can be a useful way for improving quality of health services. Relying on supervised learning, we study the feasibility of extracting and classifying the human activities from channel gain measures, which is an important feature that characterizes the WBAN channel links.

Distributed algorithms have become a rapidly growing field of research due to the advances of the network technologies. However, they are very difficult to implement correctly because they must meet many requirements. In this paper, we follow the guidelines of systematic literature reviews to provide a survey of the existing works ensuring the formal verification of distributed algorithms in static and dynamic networks. Then, we develop a taxonomy of these solutions based on some criteria. Also, a discussion on each criterion is shown with a focus on constraints, requirements and challenges. Finally, we identify some recommendations and open research areas which can motivate the development of more efficient solutions. So, throughout this present paper, we provide information for researchers and developers to understand the contributions and challenges of the existing solutions to pave the way for enhancing their reliability.

The aim of this work is to propose a distributed algorithm, encoded by the local computations model, for computing maximal cliques in dynamic networks.This model provides an abstraction which simplifies the design and the proof of distributed algorithms.To guarantee the correctness of our algorithm, we use the Event-B formal method, which supports a refinement based incremental development using the RODIN platform.

Wireless Sensor Networks (WSNs) are widely used in critical applications (health-care, transport, volcanic eruption monitoring, etc.). Any design error in WSN algorithms can be harmful for the human's life. Therefore, we should be sure that WSN algorithms work correctly from the very first design stages. In this paper, we propose a new approach combining local computations models and refinement to prove correctness of distributed algorithms for WSNs. We use the formal method Event-B to apply refinement. In fact, local computations models provide abstract description of computations which can be easily specified with Event-B. We illustrate our approach by an example of distributed algorithm for WSN.

Dynamic networks are characterized by frequent topology changes due to the unpredictable appearance and disappearance of mobile devices and/or communication links. In this paper, we propose a correct-by-construction approach for proving distributed algorithms in a forest of spanning trees. Our approach consists in two phases. The first one aims to control the dynamic structure of the network by triggering a maintenance operation when the forest is altered. To do so, we develop a formal pattern using the Event-B method which is based on an existing model for building and maintaining a spanning forest in dynamic networks. The second phase of our approach deals with distributed algorithms which can be applied to spanning trees. We illustrate our pattern through an example of a leader election algorithm. The proof statistics show that our solution can save efforts on specifying as well as proving the correctness of distributed algorithms in a forest of spanning trees.

Network Function Virtualization (NFV) is a promising technology that is receiving significant attention in both academia and the industry. NFV paradigm proposes to decouple Network Functions (NFs) from dedicated hardware equipment, offering a better sharing of physical resources and providing more flexibility to network operators. However, in such environment, efficient management mechanisms are crucial to address the problem of Placement and Chaining of Virtual Network Functions (PC-VNF). In this paper, we introduce a PC-VNF model based on a flexible resource allocation approach that takes into account service requirements in terms of latency, in addition to traditional connectivity and resource utilization. This is particularly important for emerging 5G services such as ultrareliable, low latency and massive machine type communications. The end-to-end performance needs to meet the user expectations as well as service requirements to provide the desired QoS/QoE. Our main goal is to determine the optimal VNF placement minimizing resource consumption while providing specific latency (i.e., end-to-end delay) and avoiding violation of Service Level Agreements (SLA) by constraining allocated resources to a given VNF to reach its required performance. Results show that our approach achieves the required latency with better resources utilization compared to the classical approaches, with a reduction of up to 40% of resource consumption and a higher rate of accepted requests by recovering 15 to 60 % of the rejected requests.

In wireless sensor networks, Intrusion Detection through sensor monitoring allows locating intricate security attacks like denial of service and routing attacks. Nevertheless, the monitoring process observing sensors behavior can induce an important energy and bandwidth overhead. Furthermore, the monitors can be attacked and their observations would be corrupted. In this paper, we propose an adaptable monitoring system that optimizes sensor resources and resists monitor security attacks. The idea is to select periodically monitors with high reputation and high resilient energy. A theoretical evaluation and simulation results demonstrates the promising efficiency of the proposed approach.

As the energy efficiency remains a key issue in wireless sensor networks, duty-cycled mechanisms acquired much interest due to their ability to reduce energy consumption by allowing sensor nodes to switch to the sleeping state whenever possible. The challenging task is to authorize a sensor node to adopt a duty-cycle mode without inflicting any negative impact on the performance of the network. A context-aware paradigm allows sensors to adapt their functional behavior according to the context in order to enhance network performances. In this context, the authors propose an enhanced version the Efficient Context-Aware Multi-hop Broadcasting (E-ECAB) protocol, which combines the advantages of context awareness by considering a multi criteria and duty-cycle technique in order to optimize resources usage and satisfy the application requirements. Simulation results show that E-ECAB achieves a significant improvement in term of throughput and end-to-end delay without sacrificing energy efficiency.

Broadcast authentication has been widely investigated in the context of wireless sensor networks, Internet, RFIDs, and other scenarios. With the emergence of the Internet of Things that allows to connect different wireless technologies to provide services, broadcast authentication is crucial. Broadcast authentication aims to confirm that the sender of the message is the pretended source. In this direction, different state of the art proposals address this problem either by reducing the communication and overhead burden of security solutions, or by reducing the impact of attacks that aim to jeopordize the effectiveness of the service. In this paper, we propose an improved authentication scheme that is efficient for resource constrained devices. In particular, we shed the light into the security vulnerabilities of lightweight authentication mechanisms and their inability to tackle memory DoS attacks. Hence, we propose an improved scheme derived from the streamlined μTESLA, referred to as X-μTESLA. We demonstrate through our analytical and simulation results that X-μTESLA reduces communication overhead and yields a better performance in terms of energy consumption, memory overhead, and authentication delay than its previous counterparts.

The wide proliferation of wireless sensor networks in various domains has lead to the emergence of numerous applications with different requirements to satisfy for the well functioning. It is imperative to develop different approaches and communication protocols to meet these diverse and specific requirements while considering the most critical constraint, network lifetime. Adopting duty cycling is an efficient way to conserve energy, by allowing nodes to turn off their radio whenever possible. With these intermittent sleeping periods, it is hard to meet short delay delivery for real time traffic in duty cycled networks, since involved nodes in forwarding are not always awake. For delay intolerant applications, providing an always-on path without scarifying energy can be an effective solution. In this context, we propose a node-disjoint multipath routing algorithm, operating under duty cycled environment, to construct n complementary paths from the source towards the sink. To support urgent data transfer from sources of critical traffic, our protocol aims to offer always at least one available path while others are down. Simulation results show good performances in term of energy consumption and the number of exchanged packets control with a relative small increase in paths establishment time.

Distributed algorithms are designed to run on interconnected autonomous computing entities for achieving a common task: each entity executes asynchronously the same code and interacts locally with its immediate neighbours. It is widely agreed that the lack of knowledge of the global state makes termination detection one of the most important and complex problems in distributed computing. By relying on refinement, we prove that an algorithm computing a spanning tree with Local Termination Detection (each entity is able to determine only its own termination condition), can be reused and adapted in order to compute the same algorithm with Global Termination Detection (at least one entity is aware that the entire computation is achieved in the network). The main idea relies upon specifying a combination of a well known algorithm namely SSP and the spanning tree algorithm, following a top/down approach. This paper is a starting point towards a general framework for enhancing termination detection property of distributed algorithms and reusing their proofs.

Context-awareness has gained an increasing popularity in ubiquitous computing environments as it allows automatic adaptation of protocols behavior according to context changes. Nowadays, wireless sensor networks (WSNs), deployed as a main appliance to gather contextual information, can benefit from their own context data. In such scarce resource networks, the concept of context-awareness can be exploited either to optimize resource usage or to enhance the functional behavior of operating protocols. In this paper, we investigate the benefits brought by the concept of context-awareness when exploited in duty-cycled networks, since WSNs rely mainly on power saving mode in order to prolong the lifetime of sensor nodes. We focus mainly on the broadcast operation required for data collection and routing task. To quantify these improvements, we propose a comparative study relying on our ECAB protocol, using context-awareness jointly with a duty cycle mechanism to assure an efficient multi-hop broadcasting. To this end, we compare ECAB against protocols operating without context-awareness. Results show the effectiveness of this concept to improve network performances related to energy consumption, latency and packet delivery.

In Wireless Ad Hoc Networks, synchronization is a prerequisite to many basic operations such as frequency hopping, power saving, free contention channel access, clustering and security. Clock synchronization requires the availability of a common time reference for all mobile nodes. However, a synchronization algorithm cannot converge quickly to a stable point without considering the effects of collisions and channel interference inherent to wireless environments. In this paper, we propose a Fast Synchronization Protocol with Collision Handling for multi hop wireless ad hoc networks. Performance evaluation results show the ability of our algorithm to improve the convergence time and to assure the network wide synchronization accuracy even in highly dynamic scenarios.

Proving the correctness of distributed algorithms in dynamic networks is a hard task due to the time complexity and the highly dynamic behavior. In the literature, the existing solutions lack a consensus about their developments and their proofs. Moreover, the proofs which have been presented are done manually. In this paper, we propose a reuse based approach for specifying and proving distributed algorithms in dynamic networks. It consists in developing a formal pattern using Event-B method, based on refinement techniques. The proposed pattern allows to handle topological events in dynamic networks and to characterize the concept of time. Our solution relies on evolving graphs as a powerful model to record the evolution of a network topology. To illustrate it, we present an example of a distributed counting algorithm. The proof statistics related to the development of the pattern and the algorithm show the efficiency of our solution.

One of the most important issues in dynamic networks is to prove the correctness of distributed algorithms. This issue has been widely studied in the literature. Nevertheless, we note a lack of consensus about the development and proof of these algorithms. Moreover, the proofs which have been presented are usually done manually. In this paper, we introduce a formal pattern based on evolving graphs which allows to record the dynamic behavior of a network topology. To specify the proposed pattern, we use the Event-B formal method which supports a refinement-based incremental development using RODIN platform.

In a dynamic network topological events can occur at any time, and no stable periods can be assumed. To make designing distributed algorithms easier, we model these latter with a local computation model. The implementation of a local computation model using message passing communication model has given rise to various problems. Among these we can mention the use of a great amount of communication and computation resources. In order to solve these problems, we propose another implementation of rewriting systems using mobile agents. We present then, using local computations, a framework for describing distributed algorithms for mobile agents in a dynamic network. We make use of the high level encoding of these algorithms as transition rules. The main advantage of this uniform and formal approach is the proof correctness of distributed algorithms. We illustrate this approach by giving an example of distributed computation of a hierarchical spanning tree by mobile agents in a dynamic network.

The Internet of Things (IoT) is formed by smart objects and services to interact in real time, and that are deployed in various applications such as home monitoring, healthcare, and smart cities. However, security concerns should not be overlooked since an adversary could exploit the vulnerabilities in the design of some secure protocols. To this aim, in this paper we focus in particular on broadcast authentication in resource constrained devices. This security service is still in its infancy when devices are deployed in unattended environments. We propose a new authentication mechanism based on the state of the art protocol μTESLA, that aims to reduce the delay of forged packets in the receivers buffer, by efficiently computing the key disclosure delay. Then, we integrate this mechanism to two protocols of state of the art LEAP and LEAP++. Furthermore, we assess the feasibility of our solution with a thorough simulation study, taking into account the energy consumption, the delay of forged packets, and the authentication delay.

Duty-cycling scheme consists in switching between active and sleeping modes. While this technique is widely exploited in WSNs to reduce power consumption, It raises several challenges especially for broadcast communications. In fact, as many neighboring nodes are involved in the communication, keeping them all active during a specific period of time requires a preliminary synchronization process which is a complex task and causes extra overhead. In this context, we propose an Efficient Context-Aware multi-hop Broadcasting (ECAB) over asynchronous duty cycled networks. ECAB exploits both the neighborhood knowledge and the traffic load conditions to regulate node behaviors and dynamically adjust duty-cycling schedules. Our objective is to reduce energy consumption without performances penalties. Simulation results show that ECAB outperforms RI-MAC and greatly improves energy consumption compared to broadcast under always-on networks with small degradation related to end-to-end delay and packet delivery.

In Wireless Sensor Networks, flooding in one of the basic communication primitives. It is used to propagate informations from one node to the entire network. Every node, receiving a piece of information, has to flood it to all its neighborhood. As informations to spread are important, such as fire or intrusion alerts, communications should be secured. In this paper, we present a greedy flooding algorithm for Wireless Sensor Networks secured by a Random Key Pre-distribution model that make them redoubtable. We present two theoretical upper bounds of the time complexity of this algorithm that depend on the network structure. We then use the ViSiDiA platform to implement and simulate our algorithm to validate these theoretical results.

Simulation tools are useful for detailed analysis of distributed systems that are increasingly present in our daily lives. ViSiDiA (Visualization and Simulation of Distributed Algorithms) is a platform that aims both to facilitate teaching distributed algorithms and to contribute to the research activities. This paper presents design features and examples of how to implement new ViSiDiA's algorithms. These implementations can be done using the Java language or by using the ViSiDiA's GUI (just drawing the relabelling rules that correspond to considered algorithm). We also present a more complex problem for which ViSiDiA served as a simulation and analysis tool.

Due to the lack of knowledge of the global state and the non determinism in the execution of the processes, distributed algorithms are considered to be very complex to design and to prove. However, it becomes crucial to guarantee that these algorithms run as designed. Modularization mechanism in formal development provides a simple way to manage this complexity. In this paper, we rely on the modularization mechanism of the Event-B method and on local computations model to propose a reuse based approach for modelling classes of distributed algorithms. The proposed approach consists in developing a formal pattern defined as a set of proved logical entities called modules. These modules are developed separately and, when needed, can be incorporated and instantiated in a given system development. Such a mechanism can save efforts on modelling and proving the computation steps in distributed algorithms.

Intervehicular communication (IVC) is an important emerging research area that is expected to considerably contribute to traffic safety and efficiency. In this context, many possible IVC applications share the common need for fast multihop message propagation, including information such as position, direction, and speed. However, it is crucial for such a data exchange system to be resilient to security attacks. Conversely, a malicious vehicle might inject incorrect information into the intervehicle wireless links, leading to life and money losses or to any other sort of adversarial selfishness (e.g., traffic redirection for the adversarial benefit). In this paper, we analyze attacks to the state-of-the-art IVC-based safety applications. Furthermore, this analysis leads us to design a fast and secure multihop broadcast algorithm for vehicular communication, which is proved to be resilient to the aforementioned attacks.

The problem of providing source authentication in Wireless Sensor Networks (WSNs) has been a roadblock to their large scale deployment, and is still in its infancy. In this paper, we present novel symmetric-key-based authentication schemes which exhibit low computation and communication authentication overhead. Our schemes are built upon the integration of a reputation mechanism, a Bloom filter, and a key binary tree for the distribution and updating of the authentication keys. Analytical evaluation of the proposed authentication schemes shows that the estimated average number of concatenated message authentication code in a packet from time 0 till time t is 4pt, with p is the probability that a key is corrupted. Our schemes are lightweight and efficient with respect to computation, communication and energy overhead.

Message authentication for resource constrained devices is a challenging topic. Indeed, given the scarceness of on-board resources, solutions that do not rely on asymmetric key cryptography are in demand. A few solutions to address this issue have been proposed, and some have gained the status of state of the art thanks to their effectiveness and efficiency. However, even if state of the art solutions do provide sender-receiver on-the-fly message authentication, they are not able to tackle a few relevant attacks on received messages when the time dimension is taken into account. In particular, we first introduce two types of attacks: the switch command attack (where an adversary pretends to “switch” two messages over time-that is, altering the relative time ordering), and the drop command attack (where an adversary could pretend not having received a message previously sent from the legitimate sender). We then propose a new solution for broadcast authentication that copes with the above introduced attacks: MASS. Our analysis shows that MASS is effective in detecting both switch command and drop command attacks.

Traffic safety through inter-vehicular communication is one of the most promising and challenging applications of Vehicular Ad-hoc Networks. In this context, information such as position, direction, and speed, is often broadcast by vehicles so as to facilitate fast multi-hop propagation of possible alert messages. Unfortunately, a malicious vehicle can inject bogus information or cheat about its position. In this work, we analyze the impact of a position cheating attack on an alert message application. We show that this weakness we found could be leveraged by an adversary in a very effective way. Furthermore, our analysis leads us to design a countermeasure to this threat. Finally, we run a set of simulations which confirm our findings.

SUMMARY Wireless sensor networks are a new generation of networks that need specific models and algorithms. We are interested specifically in mobile wireless sensor networks that are considered as anonymous asynchronous distributed mobile systems. As broadcast is one of the most important applications for such networks, and as it depends on the communication model, we tried to find the most suitable one to make a distributed broadcast algorithm. We adopted the population protocols, the Angluin's model of pairwise interactions of anonymous finite‐state agents, to broadcast an information. We tried to modify this model to avoid the information duplication and then calculated the complexity of the algorithm. Then, we extended the model with the rendezvous one that made the stabilization of the algorithm faster. The implementation, the simulation, and the validation of these algorithms and results have been done with Visidia. Copyright © 2012 John Wiley & Sons, Ltd.

Computing the global state of an asynchronous distributed system is a widely studied problem and finds a plethora of solutions under different assumptions. Most of them do not correspond to real-life requirements. In this paper, we address the global snapshot and the global predicate evaluation problems in anonymous and asynchronous networks. We present a fully-distributed solution which allows the debugging and the monitoring of such networks. We show that our contribution can be easily implemented and added as a new feature in existing simulation softwares by describing specifications of the used model and details of the development process. As an illustration, a debugging layer is implemented on the ViSiDiA platform.

The intrinsic characteristics of ad hoc networks, such as the frequent connectivity changes and the strict bandwidth and power constraints, impose further challenges, especially for routing tasks. Besides, existing routing algorithms devoted to ad hoc networks and based on proactive or reactive schemes suffer from scalability due to their intrinsic mechanisms. The control overhead induced by routing packets is a primary factor, since it increases with the number of nodes, especially in large and dense networks evolving in a dynamic environment. Relying on a virtual infrastructure seems a promising approach to overcome the scalability problem. The basic idea consists on assigning additional tasks to a limited set of dominating nodes, satisfying specific requirements. In this paper, we present a routing algorithm, which exploits the benefits of our clustering algorithm TBCA. Conducted simulations show the ability of our new approach to reduce the control overhead and improve the reactivity of routing to the topology changes.

Designing power aware protocols becomes a pre-requisite to conserve energy and extend network life time. In fact, many target applications may require such features when being deployed for long periods in hostile environments, such as rescue or military operations. To assure power saving while preserving network capacity, relaying on a virtual backbone seems a promising approach. Through keeping a subset of nodes active for communication tasks, we allow other nodes to switch to sleep mode reducing consequently energy waste. However, the benefits of clustering come at a cost in terms of time and the prohibitive overhead incurred during the clusters' establishment and maintenance. In this paper, we investigate the influence of the underlying clustering algorithm on the performance of a power saving mechanism. The conducted simulations confirm the importance given to the choice of an efficient clustering algorithm to achieve energy efficiency while preserving network capacity.

We define in this paper, a formal conceptual model, which presents the most fundamental concepts for mobile agent systems and unifies their representation and defines the relations between them. On this base, we propose a formal security framework which consists of three basic frameworks. The specification framework proposes a generic definition of security policies for the entities of mobile agent systems. The verification framework checks the intra-policy consistency. The reconfiguration framework describes how to reconfigure security policies and to maintain their consistency. Finally, we examine this theoretical work by defining an operational framework for dynamic enforcement of security policies.

Needless to say, distributed algorithms are usually hard to design mush harder to prove and to use in real distributed systems. In these systems, local computations theory has proved its power to formalize and prove in an intuitive way distributed algorithms. This paper uses this formalism to present solutions to the election problem in several network topologies using mobile agents at the design and the implementation levels. We formalized the proposed solutions in the local computations model using transition systems [11]. This facilitates the proof of the proposed solutions using the mathematical tool-box provided by the local computation theory. Using mobile agents, the proposed solutions get rid of synchronization and do not need continuous use of all machines computational resources. Proposed solutions are also simulated within the VISIDIA [3] platform.

Security in mobile agent systems is twofold: protection of mobile agents and protection of agent execution system. Indeed, the proposed solutions for the security of distributed systems arenpsilat sufficient. Moreover, therepsilas no solution which treats the different concerns of security in the mobile agent systems. To achieve this goal, we use formal foundations which provide a rigorous reasoning about security of mobile agent systems. We propose in this paper a formal framework for the security in mobile agent systems which consists of three basic frameworks. The specification framework proposes, explicitly, a generic definition of security policies that may be enhanced by several concepts related to one or more security models. For illustration, we present a security policy enhancement based on the concepts of the RBAC model. Inevitably, we associate to the specification framework a verification framework which checks the consistency of the proposed specifications as well as the consistency intra-policy. In response to the dynamic changes of security requirements in mobile agent systems, we propose a third framework for the reconfiguration of policies.

This paper presents efficient deterministic and randomized distributed algorithms for decomposing a graph with n nodes into a disjoint set of connected clusters with small radius and few intercluster edges. Our algorithms can be easily implemented in the distributed CONGEST model of computation i.e., limited message size, improving the time complexity of previous algorithms (Moran and Snir, 2000; Awerbuch, 1985; Peleg, 2000) from linear to sublinear. One important application of our algorithms is efficient construction of sparse graph spanners. In fact, given a parameter k, we show that there exists a sublinear deterministic distributed algorithm that constructs a graph spanner of stretch 2k - 1 with at most O(n1+1k/) edges in the CONGEST model

In this paper, we present a method to build an homogeneous and interactive visualization of self-stabilizing distributed algorithms using Visidia platform. The approach developed in this work allows to simulate the transient failures and their correction mechanism. We use local computations to encode self-stabilizing algorithms like the distributed algorithms implemented in Visidia. The resulting interface is able to select some processes and incorrectly change their states to show the transient failures. The system detects and corrects these transient failures by applying correction rules. Many examples of self-stabilizing distributed algorithms are implemented.

We present a formal method to design self-stabilizing algorithms by using graph rewriting systems (GRS). This method is based on two phases. The first phase consists of defining the set of illegitimate configurations (GRSIC). The second phase allows to construct some local correction rules to eliminate the illegitimate configurations. Then the graph relabeling system composed of the initial graph rewriting system improved with the addition of the correction rules is a self-stabilizing system (LSGRS). We obtain a general approach to deal with fault-tolerance in distributed computing. We illustrate our approach by various self-stabilizing algorithms for computing distributed spanning trees and SSP's algorithm.

Visidia is a tool for the simulation and the visualization of distributed algorithms. The simulation has been done using one machine [M. Bauderon et al., (2002)]. We present an approach of such a simulation by using a network of machines. Indeed, we suppose that several machines connected by a network can take part in the simulation. We give both a specification of the set up model and a description of the implementation carried out.

In a probabilistic graph grammar, each production has a probability attached to it. This induces a probability assigned to each derivation tree, and to each derived graph. Conditions for this probability function to be a probabilistic measure are dis