The emergence of Software-Defined Networking (SDN) has marked a profound shift in the landscape of network management, revolutionizing how networks are designed, operated, and controlled. However, the task of attaining optimal routing in SDN while enhancing efficient network performance and ensuring the highest Quality of Service (QoS) still remains a challenging problem. This paper introduces a novel Distributed Teacher-Student (DTS) method that jointly harnesses the power of Multi-Agent Deep Reinforcement Learning (MADRL) with Knowledge distillation for effective QoS routing. The core idea is to transfer knowledge from a domain expert (the teacher) to an intelligent agent (the student) through a distributed learning process using DRL. This collaboration involves multiple teacher and student entities working together to decide and create optimal routes while considering the specific QoS requirements for each type of data flow. The experimental findings vividly demonstrate the effectiveness of the proposed DTS framework, revealing significant enhancements in network performance, including delay and throughput ratio. Compared to the conventional MADRL approach without the Teacher-Student framework, our DTS solution showcases a remarkable improvement of 30% in normal traffic scenarios and over 52% in situations with link failures.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model’s accuracy, under UAV constraints of energy consumption, communication quality, and local datasets’ heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

Named Data Networking (NDN) is attracting increasing attention from researchers and companies due to its characteristics and its promised results as a better alternative to the current TCP/IP Internet. Among these features are the use of names instead of addresses, and the use of caches in the nodes. Both have proved to be an excellent addition to network functionality that allow receiving information from a nearby location while relieving the pressure on the main servers. Yet, caches are still limited compared to the huge amount of data consumed. Most research has focused on finding and caching the most relevant data, to retrieve it in the future from the nearest point. Most research agrees that the data that needs to be stored is the one that is constantly requested by many consumers, and this theory has been generally effective in most research works. However, high data consumption levels are not always considered important, especially in academic or corporate environment. This is particularly true whenever the consumption of data associated to the institution’s own servers is very low compared to the other data, such as entertainment videos and private messages from social networking sites. Hence, the data that is stored and delivered in a short time is not essential for these institutions. Also, the servers that are discharged from the pressure are not affiliated with these institutions either. The existence of these cases proved by our study on real consumption data belonging to the Amar Telidji University of Laghouat in Algeria, where we found through simulations that only 4% of the overall traffic is associated with data belonging to the university itself. In this paper, we propose a new placement strategy named NBCC (Naive Bayes Classifier for Caching). The NBCC is used to cache the imported data by classifying the received content using a Multinomial Naive Bayes classifier that can classify the received data using only their names. The strategy is shown to be effective and provides the best results compared to other state-of-art strategies.

Unmanned Aerial Vehicles (UAVs) cloud have attracted wide attentions from both industrial and academic communities as a new paradigm offering flexible services. This new concept utilizes the recent technologies of mobile cloud computing. The collaboration between these technologies represents a fantastic vision of the future, where everything is connected to the Internet, thereby offering intelligent services and facilitating decision-making between cooperative UAVs. In this work, we study the UAVs clouds computing services selection strategies. Our objectives are to (1) provide a detailed review of the existing services' selection methods and to (2) propose a new Game Theoretic approach for Selection of Services in UAV Clouds (GTSS-U C) aiming to enable normal users to select the most suitable UAV-Service-Provider. Given that every service’ provider is characterized by specific features, limitations, and prices, a user must select the most suitable provider. The technique of selection is based on the Game Theory (GT) method and takes into account the user requirements and the UAVs provider qualities to find the most adequate provider. The results of the simulation conducted using NS-2 simulator advocate for the efficiency of our method in both network Quality-of-Service and Service gain.

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles' thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.

The deployment of a swarm of cooperative UAVs applications for the execution of distributed tasks has increased attention from both academia and industry researchers. The use of a group of UAVs instead of one single UAV offers many advantages like extending the mission coverage, providing a reliable ad-hoc networks services, and enhancing the service performance, to name a few. However, due to the highly dynamic nature of the swarm topology, the coordination of a large number of UAVs poses new challenges to traditional inter-UAV communication protocols. Therefore, there is a need for the design of new networking protocols that can efficiently support the fast-pace and real-time requirements of a coordinated swarm navigation in various environments. In this paper, we propose SEMRP a Swarm energy-efficient multicast routing protocol for UAVs flying in group formations. The main purpose of SEMRP is to facilitate the control and information delivery between UAVs while minimizing inter-UAV packet loss, packet re-transmission, and end-to-end delay. In this study we show how SEMRP achieves these objectives by taking into account various Quality-of-Service parameters like the network throughput, the UAVs mobility, and energy efficiency to ensure a timely and accurate information delivery to all members of a UAV swarm. The results of the conducted simulation using NS-2 advocate for the efficiency of our proposal through its to two presented versions (SEMRP-v1 and SEMRP-v2) in term of reducing the total emission energy (at least by 10 dBm), optimizing the End-to-End Delay by 44%, and increasing the packet delivery ratio by more than to 22% compared to SP-GMRF protocol.

Vehicular cloud (VC) is a promising environment, where intelligent transport applications can be developed relying on mobile vehicles, which can be both cloud users and cloud service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a variety of services to users' vehicles. In this context, to consume a cloud service on the move, a user vehicle must first identify the most stable vehicles, relative to his/her motion, which are able to provide the service, and then select the most suitable service according to his/her preferences, while both provider vehicles and their services are described by attributes or quality constraints. Therefore, we introduce a generic relative motion model, as a generic link stability metric, upon which vehicles can form a stable cloud, and we address the VC service selection by using linguistic quantifiers and fuzzy quantified propositions, to define our flexible quantified service selection (FQSS) scheme, which aggregates efficiently both user preferences and service constraints and ranks service providers from the most to the least satisfactory. To break ties among the top-ranked service providers, we make use of our parameters for ranking refinement, called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp). The simulation results show that our link stability achieves generic motion, by modeling a wider range of vehicle motion types, and our FQSS scheme allows a good successful service consumption rate while reducing latency.

Vehicular Cloud (VC) has gained popularity today allowing mobile users to access a variety of on demand resources while on the move using low cost Vehicular Network. VC enables vehicles with sufficient resources to act as mobile cloud servers and provide their computing, communication and caching resources to nearby vehicles. However, due to high mobility and intermittent connectivity, it is challenging for mobile users to efficiently discover providers' services before request targeted services from them. Therefore, service advertising is of great interest with which offered services by Provider Vehicles (PVs) in the vehicular cloud can be fast propagated into the network. Given PVs' limited budget for renting advertiser vehicles, how to achieve the maximum service advertising coverage within a given period of time for a given budget requirements is NP-hard. This work aims to propose a new Centrality-based approach, PUBLISH, for PVs' services advertising in the vehicular cloud. We exploit the centrality score of both services and vehicles to find the best set of 'appropriate' vehicles as services advertisers. Results from scalable simulations show that PUBLISH efficiently identify the best services advertisers in comparison to other schemes in the literature.

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

With the deployment of multimedia services over VANETs, there is a need to develop new techniques to insure various levels of quality of services (QoS) for real time applications. However, in such environments, it is not an easy task to determine adequate routes to transmit data with specific application QoS requirements. In this paper, we propose CBQoS-Vanet, a new QoS-based routing protocol tailored towards vehicular networks in a highway scenario. This protocol is based on the use of two techniques: first a clustering technique which organizes and optimizes the exchange of routing information and, second, a bee colony inspired algorithm, which calculates the best routes from a source to a destination based on given QoS criteria. In our approach, clusters are formed around cluster heads that are themselves elected based on QoS considerations. The QoS criteria here are based on the two categories of metrics: QoS metrics and mobility metrics. The QoS metrics consists of the available bandwidth, the end-to-end delay, and the jitter. The mobility metrics consists of link expiration time and average velocity difference. We have studied the performance of CBQoS-Vanet through simulation and compared it to existing approaches. The results that we obtained show that our technique outperforms, in many aspects, the approaches that it was compared against.

Measuring vehicles density and distribution in urban environments is an important task. The results of such estimation are highly required for different applications such as road lights configuration, congestion control, and also inter-vehicle data routing. This task, which is known as crowd sensing, is mostly based on smartphone-assisted sensing. However, in urban environments the multiple kinds of obstacles make it hard and mostly inaccurate especially for RoadSide Units (RSUs) low density cases. Furthermore, the assumption that all vehicles are collaborative and honest can lead to unexpected and unwanted situations. To address the above mentioned problems, we propose in this paper a trust-aware crowd sensing technique based on Unmanned Aerial Vehicle (UAV) for vehicular urban environments. Considering the real traffic information and the distribution of dishonest nodes in the network gathered by UAVs, our proposed solution provides a global view to both vehicles and RSUs, which can be used for different applications such as: finding the shortest and most trusted possible path to messages' final destinations, and also for the intelligent congestion control. Our simulation results show that our solution offers instant crowd and trust information over which in addition to the high detection ratios, also high packet delivery ratios with low network overhead are achieved.

Vehicular Cloud Computing (VCC) exploits resources at vehicles, such as computing, storage and internet connectivity to provide services for applications supporting different ITS (Intelligent Transportation System) services. Current Vehicular Cloud (VC) systems allow Consumer Vehicles (CVs) to discover and consume offered services by nearby mobile cloud servers (vehicles). However, to consume the required services, the CVs must first select the most suitable service provider, given that each of providers is characterized by specific features, limitations and prices. To the best of our knowledge, no work to date addresses the critical question of how to select the best provider fitting the quality of services and costs requirements of the consumer vehicles. Similarly, Provider Vehicles (PVs) should adjust the provided services' features and prices under certain conditions such as the rate of consumers' requests which makes this issue even harder. In this paper, we propose GSS-VC as a new distributed game theory-based approach to manage the service provisioning in vehicular cloud. Our approach takes into account the benefit of each player and allows the CVs to find the most suitable PV based on the probability interaction between them. Simulation results are carried out using urban mobility model and illustrate the effectiveness of the proposed approach to answer the raised questions: what is the best condition under which the CVs may request the PVs for services? and how to select the best service with respect to the CV preferences? Results from extensive simulations on up to 1, 500 vehicles show that GSS-VC is a an efficient and reliable service selection scheme while achieving high QoS.

Successive location recommendation has recently emerged as an important service in Location-Based Social Networks (LBSNs). It aims at recommending the next location(s) to visit to a user given its current and previous locations. Although several recommenders have been proposed, only few works have considered the sequential correlations among locations in addition to other influential factors in recommendation. In this paper, a novel sequential rule mining-based approach called Location Recommender (LocRec) is proposed. Our proposal is designed to perform successive location recommendation for users of LBSN by considering sequential, social and temporal influence factors. The proposed approach first extracts the set of location sequences from mobility data and then generates recommendation rules from it. Based on the concept of recommendation influence factor, two rule-based recommenders are designed, namely temporal-based and social-based recommenders. An experimental evaluation was conducted on a real large-scale LBSN dataset to compare the performance of the proposed recommenders. Obtained results show that the tolerance to order-variations and the use of a window constraint enhance the performance of LocRec. They also depict that LocRec outperforms classical sequential-based models for successive location recommendation.

Ensuring the required trustiness among communicating peers is an important task in Vehicular Adhoc Networks (VANETs), especially for safety-related applications where the margin of error is extremely undesired. Most the safety applications are a kind of decision aided system, and final decision is always taken by humans. Thus, in addition to securing inter-vehicle communication, the human factor must be also considered. With the appearance of 5G technology it became possible to connect VANET to any other network including Online Social Networks (OSNs). In this paper, we took advantage of this possibility to connect VANET and OSN, for the purpose of estimating the drivers honesty based on their OSN profiles. Afterward, we combined both inter-vehicle and OSN-based trust to compute the overall trust about the different vehicles and their drivers. Conducted simulation show that our proposal offers more than 5% detection ratio than the classical inter-vehicle solution. Furthermore, it also reduced the detection error ratio by about 3% with a reduced standard deviation for both detection and error ratios.

Vehicular Cloud (VC) is an emerging paradigm where vehicles having sufficient resources act as mobile cloud servers by offering a variety of services to user vehicles. To consume a cloud service on the move, a user vehicle must first identify the most stable vehicles, relatively to its motion, capable of providing the service, then select the most suitable service according to its preferences and service provider quality or constraints. In this paper, we introduce a link stability metric based on a generic relative motion model among vehicles to form a stable cloud and address vehicular cloud service selection by using linguistic quantifiers and fuzzy quantified propositions aggregating efficiently both user preferences and service constraints to rank service providers from the most to the least satisfactory. To break ties, we also define new parameters, called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp). Simulation results show that the link stability achieves generic motion and the selection approach allows a good successful service consumption rate while reducing latency.

Considered as a primordial component of Cooperative Intelligent Transportation Systems (C-ITS), Vehicular Ad Hoc Network (VANET) plays a weighty role to facilitate different on-road applications, most of which primary rely on multi hop communications. To ensure reliability and security of such communication, it is very pertinent to guarantee that the most proper and trustworthy vehicles are selected as relays. This paper takes up this challenge by introducing a weighted probabilistic and trust-aware strategy called WeiSTARS, to ensure high delivery ratios within reduced delays. Simulation results conducted using NS2 tool depicted that our technique enhances the delivery ratio by more than 8% with around 30% less delay compared to both GytAR and GPSR routing protocols even in the presence of high ratios of dishonest vehicles.

Summary Communication‐induced checkpointing (CIC) protocols aim at finding consistent global checkpoint from which a system can safely restart without the risk of domino effect. CIC class gives processes the maximum autonomy for taking their local checkpoints while directs them to take forced checkpoints when harm patterns are detected. Such patterns can constitute of non‐causally doubled Z‐paths that violate the causal relation and can cause checkpoints uselessness, which results in hidden dependencies between checkpoints. Ensuring the absence of hidden dependencies hence means the ensure of the Rollback‐Dependency Trackability (RDT) property. Reducing the control information overhead is the main aim of our proposals. Our first proposal is called CSFDAS (Constant Size Fixed Dependency After Send), and the second is called CSRDTParner (Constant Size RDTPartner). Unlike to the previous works, the new proposed protocols use direct dependency clocks instead of transitive dependency vectors while keeping only a constant size of control information on messages. Conducted simulations show that the new protocols achieve a good performance compared to the RDT protocols proposed so far in the literature.

Cooperative intelligent transportation systems (CITS), mainly represented by Vehicular Ad Hoc Networks (VANETs), were developed to enhance safety on roads before being generalized to support other comfort and efficiency applications. Most VANET applications, including safety ones, are based on multi-hop communications. Hence, a certain trustworthiness should exist among vehicles to ensure a reliable and trusted communication excluding dishonest peers from all network operations. In this paper we propose an hierarchical trust establishment solution able to cope with VANET applications and their requirements. Our solution is based on a three-level architecture, which enables it to adapt to the communication scenario and the required security level. Simulation results conducted done NS2 tool evidence that our solution is able to reach almost optimal attacker detection ratios (more than 90%) even in the presence of a significant number of attackers in the network (25%) while reducing the wrong relay decision ratios.

It is a challenging task to develop an efficient routing solution for a reliable data delivery in urban vehicular environments. Indeed, it is difficult to find a shortest end-to-end connected path especially in urban city given the mobility pattern of the vehicles and the various obstructions to a clear transmission such as buildings. To overcome these difficulties, we investigate how unmanned aerial vehicles (UAVs) can assist vehicles on the ground in relaying in urban areas. In this paper, we propose UVAR (UAV-Assisted VANET Routing Protocol), a new routing technique for Vehicular Ad hoc Networks (VANets). This protocol is based on the use of the traffic density and the knowledge of vehicular connectivity in the streets. With this approach UAVs collect information about the traffic density on the ground and the state of vehicles connectivity, and exchange them with vehicles through Hello messages. These information allow UAV to place themselves so as to allow relaying data when connectivity between sole vehicles on the ground is not possible. Through vehicle-to-UAV (V2U) communication, the overall connectivity between vehicles is improved and therefore the routing process is efficiently improved. The performance of the proposed protocol is evaluated and the results to different scenarios are discussed.

Vehicular Cloud (VC) is a new concept which enables vehicles to offer and rent out their advanced on-board resources to other vehicles. So, individual vehicles can be both service providers and cloud users. Vehicles users need to discover vehicles' services and request targeted services from them. To achieve this, a cloud directory must be used in which provider vehicles register their services and from which vehicle users discover offered services in order to consume them. In a previous work [1], we have designed a new protocol in VC, named Discovering and Consuming Cloud Services in Vehicular Cloud (DCCS-VC). Due to their predictability of time and space in urban scenarios, DCCS-VC was based on public buses as a cloud directory in order to form a dynamic index of provider vehicles. However, DCCS-VC provides a low efficiency of both registration and discovering operations, given the introduced high waiting time of vehicles to perform these operations. In this paper, we extend our previous protocol to minimize the provider and user vehicles' waiting time. To do so, we allow vehicles to exploit the providing real time bus information in order to discover existing public buses in the vicinity. In addition, we introduce an optimization technique which enables provider vehicles to select the most adequate public bus as a service registration node. We illustrate the superiority of this enhancement throughout the results obtained from simulation experiments, using an urban mobility model.

Abstract Secure routing protocols that are based only on cryptographic techniques cannot guarantee security against all attacks. Among solutions that have been proposed to enhance the security in vehicular networks are the distributed revocation protocols, which provide vehicles with the ability to quickly detect and avoid malicious attacks. However, most of the proposed revocation protocols are vulnerable to colluding attacks conducted by malicious nodes, a situation which results in denial of service. In this work, we propose a new and robust distributed revocation protocol for vehicular ad hoc networks, called Secure Distributed Revocation Protocol (SDRP), with the main objective to exclude misbehaving nodes conducting or not a colluding attack from the routing operation in VANET. We present an evaluation analysis of SDRP on the basis of the simulation results and show that our scheme provides a high detection rate of misbehaving nodes with a low rate of false positives even in the presence of a large number of attackers. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Trust establishment over vehicular networks can enhance the security against probable insider attackers. Regrettably, existing solutions assume that the attackers have always a dishonest behavior that remains stable over time. This assumption may be misleading, as the attacker can behave intelligently to avoid being detected. In this paper, we propose a novel solution that combines trust establishment and a risk estimation concerning behavior changes. Our proposal, called risk‐aware trust‐based architecture, evaluates the trust among vehicles for independent time periods, while the risk estimation computes the behavior variation between smaller, consecutive time periods in order to prevent risks like an intelligent attacker attempting to bypass the security measures deployed. In addition, our proposal works over a collaborative multi‐hop broadcast communication technique for both vehicle‐to‐vehicle and vehicle‐to‐roadside unit messages in order to ensure an efficient dissemination of both safety and infotainment messages. Simulation results evidence the high efficiency of risk‐aware trust‐based architecture at enhancing the detection ratios by more than 7% compared with existing solutions, such as T‐CLAIDS and AECFV, even in the presence of high ratios of attackers, while offering short end‐to‐end delays and low packet loss ratios. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Routing in cooperative vehicular networks is a challenging task because of high mobility of vehicles and difficulty of localization. In this paper, we study the geocast routing problem in Vehicular Ad‐hoc NETworks (VANETs), which aims at delivering data to a specific group of mobile vehicles identified by their geographical location. Although many geocast routing protocols have been proposed, only partial inherent constraints of VANETs (such as mobility, internal network fragmentation problem, external network fragmentation problem, and overload) are taken into account. Therefore, we propose two novel and robust geocast routing protocols: the first one, called Robust Geocast Routing Protocol for Safety Applications (RGRP‐SA), is dedicated to road safety applications, while the second, namely, Robust Geocast Routing Protocol for Comfort Applications (RGRP‐CA), is designed for comfort applications. Simulations conducted in NS‐2 demonstrate that our safety‐oriented RGRP‐SA protocol outperforms Inter‐Vehicle Geocast protocol and Mobicast Routing Protocol in VANETs by sending up to 25% more packets, cutting the end‐to‐end delay in half, and solving the internal network fragmentation problem. Besides, it is also shown that our comfort‐oriented RGRP‐CA protocol serves well comfort applications with only light overhead by solving internal and external network fragmentation problems and providing more reliable data delivery (with a 25% higher packet delivery ratio) and higher network throughput utilization in comparison with Mobicast with Carry‐and‐Forward protocol. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract An important application in wireless networks is data collection. It aims to gather and deliver specific data for concerned authorities. Many researchers invest in vehicular ad hoc networks for that purpose to acquire data from different sources on the roads as from its vicinity. A vehicle is considered as a mobile data collector, it gathers real‐time or delay‐tolerant data such as road traffic, environmental information, and event advertisements. In a previous work, we have proposed a novel clustered data gathering protocol (CDGP) for vehicular ad hoc network, which improves the collection performance by implementing a new space division multiple access technique called dynamic space division multiple access and a retransmission mechanism in case of errors. However, CDGP supports only delay‐tolerant data as it does not use any aggregation technique. In this paper, we propose an enhancement of this protocol by extending it to support: (i) both real‐time and delay‐tolerant applications; (ii) multiple types of data; and (iii) aggregation of collected data prior to sending them to the initiator. We present the plausible analytical complexity of the extended CDGP, as we illustrate the superiority of its performance throughout the results obtained from simulation experiments, using a Freeway mobility model. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we study the problem of efficient safety message dissemination in Vehicular Ad-Hoc Networks (VANETs). The objective is to reduce vehicular communication interferences for safety message delivery while guaranteeing the timeliness and the reliability of messages to avoid accidents. Therefore, we propose a heuristic algorithm called Time-Limited Reliable Broadcast Incremental Power (TRBIP) to construct a safety message delivery tree. Extensive simulation results show that the proposed algorithm outperforms its counterparts in terms of message timeliness, reliability and interference reduction.

Routing in Vehicular Ad hoc Networks (VANETs) is an important factor to ensure a reliable and efficient delivery of data packets. In urban environments, routing protocols must efficiently handle the constantly changing network topology and frequent disconnections due to the high mobility and direction changes of vehicles. The challenge is greater when there are traffic lights fixed along intersections which affect directly the mobility and therefore can greatly impact routing in urban areas. In our previous work [1] we have proposed IRTIV (Intelligent Routing protocol using real time Traffic Information in urban Vehicular environment) that takes into account the real time traffic variation without any use of pre-installed infrastructures or additional messages. However, IRTIV does not take into consideration the traffic lights impact. In this paper, we propose ETAR (Efficient Traffic Light Aware Routing Protocol for Vehicular Networks). This protocol's objective is to find the most stable path for delivering data packets based on traffic lights and traffic density of vehicles using the periodical exchange of Hello messages. We present simulation-based performance results, which show that the proposed protocol increases the packet delivery ratio and reduces the end-to-end delay.

The advanced on-board vehicles' resources have given birth to the Vehicular Cloud (VC) concept. The VC is an emerging paradigm where individual mobile vehicles can be both cloud users and service providers, it enables vehicles that have sufficient resources to act as mobile cloud servers and rent out them to other vehicles. However, and with the high mobility of vehicles, user vehicles need to discover vehicle providers, know their services, and request targeted services from them. In this paper, we propose a new protocol that enables user vehicles to discover and rent providers' services in VANet using public buses. Due to the predictability of time and space of these buses in urban scenarios, our protocol use them as cloud directories with which provider vehicles register and from which user vehicles discover all offered services. Hence, they hold a dynamic index of services' providers. We demonstrate the efficiency of our protocol, in terms of service discovery and consuming delays, by conducting an extensive set of simulation experiments using OMNet++ network simulator.

Delivering data through the most reliable and trusted path is essential for any kind of network. Moreover, in highly mobile and dynamic networks such as VANETs, the problem is more complex since every node requires, at least, a previous knowledge about its own neighborhood to select the most adequate path. In addition, the open communication medium causes other problems that any routing protocol must manage, without forgetting the VANETs' sensitivity to delay. In this paper we propose a trust-based routing protocol for vehicular urban environments called TROUVE. The proposed protocol aims at finding the shortest and most trusted path to destination taking into account the real traffic information and the distribution of dishonest nodes in the network. In this study, simulation results show the effectiveness of our protocol in terms of data delivery and end-to-end delay. We also show that, even in the presence of a high number of misbehaving nodes, our protocol offers equally good results.

Trust management is one of the major issues for secure communication in vehicular network. Most of the existing trust models are identity-based and use excessive periodic exchange between vehicles to build a decision about trustiness of participating vehicles. Malicious data can be minimized in such models using identities reputation. In general, the nature and the quality of the data are not taken into account, despite some models which may revoke messages based on their nature and their type. In this work, we propose a new trust model for VANETs, which based on the early detection of attacks by relying on the opinion of the last forwarder and delayed verification of the exchanged messages. In our scheme, we developed an intrusion detection module which perform such operation and evaluate the trustiness of received messages. We introduce a new concept of companion vehicles which is used to filter out and select the most trusted nodes among neighboring vehicles, to be used as relays in the forwarding procedure. The deployment of this mechanism allow us to prevent vehicles identified as probable dishonest nodes from participating in the network. In this study, we simulation results show the effectiveness of our trust model in detecting dishonest nodes as well as malicious messages that are sent by honest or dishonest nodes, after a very low number of messages exchange. We also show that in the worst case scenario, our trust model offers equally good results.

A vehicle-mounted GPS receiver used for localization can suffer from signal blockage. To remedy to this problem, GPS/ INS integration can be considered as a solution in some cases. However, in the case of urban areas where there are severe multipath conditions, the performance degrades considerably. The last decade have seen many proposals of techniques aiming at improving the accuracy of GPS positions. The complexity of these techniques increases with the increase of the accuracy required. These techniques usually combine Kalman Filters (KF) with neural network or fuzzy logic... etc. In this paper, we propose a new technique based solely on neural network, which offers a better performance while presenting a lower complexity. The idea is to use a neural network, which emulates the behavior of a given estimator in order to replace it. We present simulations results, which validate the performance and the robustness of our proposed scheme in various conditions.

By adopting different detection technologies, vehicles in Vehicular Ad-hoc Networks (VANets) are able to collect various kinds of information, like road traffic and environmental information, then, to transmit them to interested entities. Data collection in VANets is considered as an interesting application which aims at providing a safer, more efficient and more comfortable driving. In a previous study [8] we have proposed a Robust Clustered Data Gathering Protocol (CDGP), by using a Dynamic Space Division Multiple Access (D-SDMA) technique with a retransmission mechanism. However, CDGP provides a low collection efficiency given the large number of unused slots, which are wasted in a vehicle-to-vehicle communication (V2V). In this paper, we propose a new data collection protocol, which improves the data collection efficiency by using an enhanced Dynamic SDMA technique. Through simulation results we show that our new protocol enhances data collection efficiency and provides a reliable data collection.