The emerging technology of Generative AI (GenAI) and its applications in various fields have opened new possibilities for educational technology, particularly in the development of advanced Intelligent Tutoring Systems (ITSs) as there is an increasing need for systems that can provide personalized learning experiences. Despite significant advancements in the development of ITSs, many proposed solutions struggle to effectively adapt to diverse learner profiles, creating barriers to personalized and adaptive learning experiences. This paper explores the transformative potential of GenAI in developing ITSs and proposes a novel ITS powered by a Generative Pre-trained Transformer (GPT), which leverages advanced Large Language Models (LLMs) to deliver contextually relevant tutoring sessions tailored to individual learning styles and proficiency levels. Key aspects of the methodology used in this study is prompt engineering and Multi-Agent Systems (MAS). This approach involves crafting specific prompts that elicit tailored responses, enhancing the overall learning experience. To maintain student engagement and motivation, the system incorporates educational techniques such as gamification, interactive simulations, and adaptive feedback mechanisms. Furthermore, the ITS is designed to recognize cues for fatigue and distraction by analyzing patterns in student interactions, such as response times and engagement levels. The effectiveness of the system is validated through extensive testing with AI-simulated students of varying proficiency levels, providing valuable data for refining prompts and improving personalization. Overall, the paper demonstrates how integrating GenAI technology can create more efficient and flexible educational environments, addressing the diverse needs of learners and redefining the landscape of personalized education.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across diverse industries, yet their communication protocols remain vulnerable to cyber threats and performance bottlenecks. MAVLink, the widely adopted standard for UAV communication, offers lightweight messaging but lacks robust security measures. This paper introduces eMAVLink, an enhanced protocol that integrates advanced cryptographic mechanisms to improve security and efficiency. We conduct a comparative evaluation of hashing and encryption algorithms utilized in eMAVLink, assessing the performance on both cryptographic co-processors and general-purpose CPUs. Our analysis examines end-to-end encryption, mutual authentication, replay attack prevention, and message integrity verification, quantifying their computational overhead across hardware-accelerated and software-based implementations. Additionally, we explore the trade-offs between security strength and real-time performance in dynamic UAV environments. The results highlight eMAVLink’s ability to achieve enhanced security while maintaining low computational overhead through optimized cryptographic offloading. This study provides key insights into the feasibility of deploying secure and efficient UAV communication protocols across diverse hardware architectures.

Brain–computer interfaces (BCIs) offer direct interaction between human brain and external devices, bypassing peripheral nerves and muscles. Within noninvasive BCI, electroencephalography (EEG) stands out due to its affordability and high temporal resolution. Motor imagery (MI)–based BCIs, in particular, harness the user’s imagination of body movements to generate control commands without external triggers. This survey aims to synthesize recent literature on MI–based BCIs, providing an overview of key methods, existing challenges, and prospective solutions. By focusing on thematic insights, we highlight how researchers are refining signal preprocessing, feature extraction, and classification strategies to boost system performance. We conducted a thematic analysis of major works in EEG acquisition, artifact removal, feature extraction methods, classification approaches,deep neural networks, and adaptive or transfer learning frameworks. We also explored user-centered factors such as training protocols and the phenomenon of BCI–illiteracy. Notable advancements have emerged in signal preprocessing, sophisticated machine learning models, and calibration-reducing strategies like transfer learning. However, the complexity of EEG signals, high inter-subject variability, and the presence of BCI–illiteracy still limit real-world adoption. Adaptive techniques and personalized feedback can mitigate these constraints, and novel methods continue to emerge. MI–BCIs hold promise for rehabilitation, assistive technologies, neuroergonomics, and broader human–machine interactions. By clarifying these core themes this survey underscores the need for multidisciplinary, user-centric approaches to advance MI–BCI reliability, accuracy, and overall usability in day-to-day contexts.

Brain-Computer Interface Illiteracy (BI) has challenged BCI research for decades. Some users negatively impact performance, regardless of decoding robustness. This issue is especially evident in the Motor Imagery (MI) paradigm, particularly during offline calibration without feedback. The literature lacks a comprehensive definition of BI. Extensive experiments with many subjects are needed to thoroughly analyze BI’s effect in offline MI decoding.This paper proposes a robust definition of BI by investigating decoding performance differences between MI and motor execution (ME) of the same task. We introduce a BI experiment using the largest MI-BCI dataset. By comparing BCI decoding accuracy for the same users during both movement execution and imagined movement, our study offers a new perspective on understanding and identifying BI. We propose that users’ neural networks are less familiar with imagining than executing movement, causing BI in MI but not in ME. We evaluate whether BI relates to subjects’ training and behavior during MI tasks. We suggest ruling out other factors like BCI setup, noise, and brain structure differences among subjects. This paper offers a new perspective on BI for the MI-BCI research community. Empirical evidence shows BI depends on subjects’ familiarity with MI, influenced by proper training.

Increased online accounts and services require robust password management solutions and practices to maintain secure authentication. Deploying unique passwords for all accounts is impractical, as cracking that password leads to losing access to all the related accounts. Thus, managing complex and unique passwords for different accounts is the primary challenge for users. To address this challenge, a secure offline vault-based password management system (Vault-PMS) is proposed. The system leverages multiple security features: AES256 encryption, multi-factor authentication (MFA), and backup to enhance password security and resilience. The system 1) deploys the MFA security control, requiring users to enter a master password and a one-time password (OTP) sent to their emails, 2) relies on password encryption and an offline backup feature that allows the storage of encrypted passwords locally on the user’s device or externally on hard drives. The proposed system aims to mitigate risks associated with online storage, specifically cloud storage, and ensure data restoration in case of device damage or loss. The proposed password management system has been implemented using Java and evaluated in terms of security and execution time. The results demonstrate that the system offers a secure, reliable, and efficient solution for password management, effectively addressing the challenges associated with maintaining secure authentication practices for multiple online accounts.

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

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style.

Federated learning (FL) is a promising approach for training AI models across multiple clients in Edge Computing (EC), without sharing raw local data. By enabling local training and aggregating updates into a global model, FL maintains privacy while facilitating collaborative learning. Nevertheless, FL encounters several challenges, including trustworthy client participation, inefficient model aggregation due to client with malicious or less accurate model. In this paper, we propose a trustworthy FL method incorporating Q-learning, trust, and reputation mechanisms, enhancing model accuracy and fairness. This method promotes client participation, mitigates malicious attacks' impact, and ensures fair model distribution. Inspired by reinforcement learning, the Q-learning algorithm optimizes client selection using the Bellman equation, enabling the server to balance exploration and exploitation for improved system performance. Furthermore, we explored the advantages of peer-to-peer FL settings. Extensive experimentation demonstrates our proposed trustworthy FL approach's effectiveness in achieving high learning accuracy while ensuring fairness across clients and maintaining efficient client selection. Our results reveal significant improvements in model performance, convergence speed, and generalization.

Unmanned aerial vehicles (UAVs) have been more prevalent over the past several years with the intent to be widely deployed in many industries, including agriculture, cinematography, healthcare, delivery, and disaster management missions due to their ability to provide real-time situational awareness. However, various limitations such as the battery capacity, the charging method, and the flying range make it difficult for most applications to carry out routine tasks in vast areas. In this paper, a deep reinforcement learning (DRL) method for multi-UAV path planning that considers a cooperative action amongst UAVs in which they share the next destination to avoid visiting the same location at the same time. The Deep Q-Network algorithm (DQN) enables UAVs to autonomously plan their fastest path and ensure the continuity of the mission by deciding when to schedule a visit to a charging station or a data collection point. An objective function with a tailored reward is designed to maintain the stability of the model and ensure the quick convergence of the model. Lastly, the proposed strategy has been demonstrated by the experiments on different scenarios and showed its effectiveness in ensuring the continuity of the mission with a fastest path possible.

A trust management system (TMS) is an integral component of any Internet of Things (IoT) network. A reliable TMS must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices’ reputations, which can help predict the future behaviors of network entities and subsequently judge the reliability of different entities in the IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this article, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. 2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAV s to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the performance results obtained show its effectiveness in finding an optimal trajectory by minimizing the UAV's travel and service time.

Unmanned aerial vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, as UAVs execute their missions in the sky, it becomes beneficial to wirelessly harvest energy from external and adjustable flying energy sources (FESs) to power their onboard batteries and avoid disrupting their trajectories. For this purpose, wireless power transfer (WPT) is seen as a promising charging technology to keep UAVs in flight and allow them to complete their missions. In this work, we leverage a multiagent deep reinforcement learning (MADRL) method to optimize the task of energy transfer between FESs and UAVs. The optimization is performed by carrying out three essential tasks: 1) maximizing the sum-energy received by all UAVs based on FESs using WPT; 2) optimizing the energy loading process of FESs from a ground BS; and 3) computing the most energy-efficient trajectories of the FESs while carrying out their charging duties. Furthermore, to ensure high-level reliability of energy transmission, we use directional energy transfer for charging both FESs and UAVs by using laser beams and energy beam-forming technologies, respectively. In this study, the simulation results show that the proposed MADRL method has efficiently optimized the trajectories and energy consumption of FESs, which translates into a significant energy transfer gain compared to the baseline 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.

The assessment of course learning outcomes is an essential component in the continuous efforts of course improvement. The assessment is a tedious process and often incurs for many educators an overhead to the teaching and learning operation. Thus the need to investigate efficient methods to improve the process of course assessment by minimizing unnecessary efforts for the planning, preparation and execution of the assessment process. Automating the assessment process is instrumental in taking away its tediousness allowing teachers to focus their efforts on the improvement of the teaching and learning quality. For the case of information technology (IT) curriculum, one main concern is the difficulties encountered by students in learning programming skills; thus the need for an assessment-driven course improvement for programming courses. In this paper, we propose an automated proactive assessment method for assessing the learning outcomes of a course by embedding the assessment instruments in the tests and student homeworks. We selected a network programming course for its suitability to embed assessment instruments as part of the programming library used by students during their test and homeworks. The embedded instruments consist of a set of use-case routines to test the validity of each design and implementation component of the developed protocol. This approach streamlines the process of learning outcomes assessment as well as the continuous improvement of the course.

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.

Brain–computer interfaces (BCIs) are an emerging technology with applications for persons with disabilities as well as the able-bodied. In this paper, we present a new framework of cooperative BCI control system for robot–drone interaction using P300-based BCI. This system is aimed at supporting and assisting complex and cooperative multitask military applications. In our online experiments, a robot “BB-8” and a parrot drone are separately mind-controlled to execute cooperative tasks using noninvasive brain measurements. We use real-time electroen-cephalography (EEG) signals to drive cooperative mission-based tasks by exchanging control information between two BCI users. The proposed cooperative BCI system is based on controlling the mobile robot and drone using the P300 speller modality and exchanging mapped messages between these two wearable EEG-headset-based systems. Using the EEG Unicorn Hybrid Black equipment, we quickly construct an interface that contains predefined visual cues for robot movements to be selected by the first BCI user; these cues are sent online as commands to the robot through the JavaScript server code. Another interface has been constructed that contains visual cues that are to be converted to drone-movement commands through the Python server code. The robot will perform a ground survey while the drone performs an aerial survey, and the final task will be performed by mutual communication between them. This cooperative BCI application can be operated by two soldiers. For instance, the actions of a robot may indicate different signs for the drone to undertake specific actions. This novel BCI application is evaluated based on the ability of two users to send commands using their brain activity, as well as the capability of the control algorithm to receive, send, and map the commands between the drone and the BB8 robot to allow them to achieve their mission.

Paralysis is the most inhibiting among all the severe motor disabilities. Indeed, people are inflicted with paralysis as the result of an accident or a medical condition that affects - completely or partially, the way muscles and nerves function. However, these patients are cognitively aware, and their mental abilities are unimpaired, and can still be autonomous and more useful in many other ways than many able-bodied people. Brain-computer interface (BCI) technology is now being incorporated into the treatment of physically impaired patients offering them an improved mobility and thus autonomy. In this paper, we propose to develop a smart brain-controlled wheelchair with autonomous navigation system for people with severely impaired motor functions. Our proposed solution allows its users to move around in indoor premises with great flexibility and minimum instructions. That is, high-level commands such as “Go to location X” is enough for the wheelchair to move to the desired location while finding its way around obstacles and obstructions. This system relies on two main components: a BCI interface to issue high level commands to the wheelchair, and a component for autonomous indoor navigation system which integrates all the elements of path planning obstacle detection and avoidance. In addition, the solution relies on the use of trained models that are deployed in cloud and provided as facility specific services.

In the above article[1], in the first-page footnote at the bottom, the corresponding author information should read: “(Corresponding authors: Moumena Chaqfeh; Hesham El-Sayed.)”

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.

Swarm technology for autonomous unmanned aerial vehicles (UAVs) has gained popularity in the last few years due to their potential for civilian and military applications. Intelligent swarm systems are very efficient at solving group-level problems and their capability to accomplish complex missions with no or little human intervention. One of the most challenging problems is operating in environments with surrounding obstacles such as buildings, thus, often obstructing inter-UAV communication. A UAV swarm is required to maintain continuous communication between its members and preserve the stability of its formation while flying towards an ultimate goal. In this paper we propose MSCS, a new cooperative and adaptive scheme for multi-clustered autonomous UAV swarms. This schemes allows several UAVs operating in a swarm formation to coordinate their navigation and path planning operations by using an adaptive multi-clustered leader-follower approach. The swarm members follow a dynamically elected leader based on the UAV with best fit to lead the swarm towards the destination. The coordination of the UAVs is achieved through SBP (Swarm Broadcast Protocol), a single-hop broadcast UAV-to-UAV (U2U) communication protocol, which allows swarm members to exchange information about their current location and their local cluster leader. We present a set of performance results, which show that this scheme contributes efficiently at maintaining the swarm formation's stability at acceptable density and disconnection ratio.

Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

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.

Vehicular communication systems play an important role in the context of smart cities. Different applications are being proposed and evaluated to improve our daily driving in terms of safety and convenience. These applications require efficient data dissemination that guarantees full coverage with minimum overhead and delay. Existing data dissemination protocols often rely on extra communication to gather knowledge about the neighborhood and set the dissemination criteria accordingly. This extra communication poses serious overhead issues that affect the scalability of data dissemination, which is an essential criterion under high density scenarios. In this paper, we propose and evaluate an Efficient multi-directional Data Dissemination Protocol (EDDP), which considers the requirements of an urban vehicular environment without requiring the extra communication overhead. We rely only on simple local data to indicate the road condition for better dissemination performance. In this paper, the design considerations of urban layout include message format, broadcast suppression mechanism, and delay control. The EDDP utilizes the properties of the received messages along with positioning information to make decisions on suppressing broadcasts, with the objective of improving coverage in different directions without unnecessary transmissions. Simulation results show that the EDDP can effectively disseminate traffic data with a high data delivery ratio and a minimized overhead.

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.

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 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.

Broadcasting forms the basis of all types of communications in ad hoc networks. In Vehicular Ad hoc Networks (VANETs), broadcasting is usually appropriate for data dissemination, since traffic data is of public interest and usually benefits a group of users rather than a specific individual. However, broadcasting in high density networks may easily lead to the broadcast storm problem due to high data redundancy and collisions. In VANETs, it is still challenging for data dissemination methods to address scalability in broadcasting under high densities, such that data redundancy is reduced while the delivery ratio is maintained. In this paper, we develop a beacon-free approach for data dissemination in multi-hop VAENT that relies on traffic regime estimation adaptively to provide scalable broadcast, without extra communication overhead. Simulation results show the efficiency of our approach in achieving low broadcasting overhead while maintaining a high delivery ratio.

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.

The rapid evolution of wireless communication capabilities and vehicular technology would allow traffic data to be collected and disseminated by travelling vehicles in the near future. Vehicular Ad hoc Networks (VANETs) are self-organizing networks that can support different types of traffic information systems without requiring fixed infrastructure or centralized administration. Since traffic data is of public interest and usually benefits a group of users rather than a specific individual, it is more appropriate to rely on a broadcasting scheme for data dissemination in VANETs. However, broadcasting in dense networks suffers from a high percentage of redundant data that wastes the limited radio channel bandwidth. Moreover, packet collisions may lead to the broadcast storm problem since a large number of vehicles in the same vicinity may rebroadcast the same data nearly simultaneously. A common employed solution to deal with such scalability issues is reducing the percentage of redundant data. This is typically done by selecting only some of the vehicles to relay data packet as opposed to letting every single vehicle rebroadcast it. In this work, we propose involving the high mobility feature of VANETs to improve scalability in data dissemination, by relying on the speed of travelling vehicles to determine the rebroadcasting probability.

Since vehicular transportation is the preferred travel mode, people are spending more time on the road due to traffic problems, that are increasing with the growing levels of motorization worldwide. A significant percentage of this time is spent during traffic congestion and at road junctions. With the advances in wireless technologies and the emergence of Inter-Vehicular Communication (IVC) techniques, new research studies are carried out to develop new applications and services over vehicular ad hoc networks, for improving the efficiency and safety on the road, with the advantage of bypassing the need for expensive infrastructure. However, despite the considerable research effort that is concentrating on data dissemination and routing protocols, the support for applications other than the access to the internet remains limited. In this paper, we propose a vehicular communication application for providing travelling vehicles with timely information to find the shortest-time route to their destinations according to the road condition. The proposed application employs a Geocasting protocol for information sharing among vehicles within a certain geographic area. We show, through simulation results, that the proposed application can significantly decrease the average travel time under different traffic patterns.

Vehicular Ad hoc Networks (VANETs) have become a cornerstone component of Intelligent Transportation Systems (ITS). VANET applications present a huge potential for improving road safety and travel comfort, hence the growing interest of both academia and industry. The main advantage of VANETs is its ad hoc nature which does not require fixed infrastructure or centralized administration. However, designing scalable information dissemination techniques for VANET applications remains a challenging task due to the inherent nature of such highly dynamic environments. Existing dissemination techniques often resort to simulation for performance evaluation and there are only few studies that offer mathematical modeling. In this paper we provide a comparative study of existing performance modeling approaches for data dissemination techniques designed for different VANET applications.

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.

Road traffic congestion in urban areas and metropolitan cities is becoming nowadays an increasingly critical issue. With the advances in wireless technologies and the emergence of vehicle to vehicle communication techniques, new research studies are carried out to develop new applications and services over vehicular ad hoc networks for road safety and road traffic management. In this paper we present a peer-to-peer VANET application for sharing and exchanging road traffic information which allows vehicles to detect and avoid road congestion. We designed a pull-based geocast protocol which allows vehicles to cooperatively collect and disseminate data in an efficient way. This protocol is integrated with a caching mechanism which reduces the amount of information exchanged between vehicles. We show that despite the use of caching, we managed to preserve the accuracy of the traffic information collected and obtain equivalent results for the overall road traffic congestion reduction.

Discovering and accessing services while on the road is an important component in the architecture of future vehicular ad hoc networks, and for a successful deployment of services. Several studies have focused on the design and development of new routing and dissemination techniques that allow vehicles to communicate with each other and with road side units. However, detecting and reaching available services in a vehicular network remains problematic due to the amount of wireless traffic generated when service queries or advertisements are flooded across the network. In this paper, we propose a cooperative hybrid service discovery scheme for discovering services provided by mobile vehicles. This scheme is achieved through cooperating vehicles using store-and-forward approach and by sharing collected service information. We also propose to study the performance of the scheme by varying its degree of reactiveness and proactiveness. It is integrated with a caching mechanism which substantially improves the performance of the service discovery in terms of reduction of the traffic generated, and minimization of the response time while increasing the discovery success rate.

Thanks to the advances in wireless technologies, mobile vehicular networks are likely to become the most relevant form of mobile ad hoc networks (MANET). Vehicular communication which facilitates the exchange of information between vehicles is the prerequisite not only for extending the access to the Internet while on the road, but also to cater for special applications such as of road traffic and travel management. However despite the increasing number of studies on vehicular routing protocols, the support for applications other than the access to the internet remains limited. In this paper we propose a novel vehicular communication system for road congestion detection and avoidance by disseminating and exploiting road information. The system is an integrated solution for sharing congestion information using a simple geocast protocol and dynamic Dijkstra algorithm for planning and computing least congested travel itineraries. In this paper, we show, through simulation results, that the proposed solution not only allows vehicles to detect road congestion ahead of time, but also contributes to reducing the congestion level on the roads by allowing vehicles to avoid congestion points.

E-learning is an emerging approach to education reflected in new learning techniques such as active and cooperative learning. Supporting communication technologies such as web-based tools, mobile devises and smart classroom used for interactive learning often come with side-effects that can be disruptive to the normal flow of the learning and the teaching processes. The use of technology van be hindered by the learning curve associated with the technology used during the class, the distraction from the initial goals of the course, the risk of mixing-up material related to the course, and concepts associated with the tools used in class, and finally, the time and effort overhead required from participants in preparing and using these tools. Therefore, it becomes crucial to continue benefiting from the advances in cooperative and communication tools, and in the same time minimizing the incurred overhead. In this paper, we propose a new classroom assessment tool: Active Class Probe (ACP) which can be used in an active and cooperative environment. ACP's main goal is designed such as to maintain focus on the initial course's learning goals, and minimize the external distraction incurred by its use in the classroom.

The differentiated services architecture is a simple, but novel, approach for providing service differentiation in an IP network. However, there are various issues to be addressed before any sophisticated end-to-end services can be offered. This work proposes an aggregate flow control (AFC) technique with a Diffserv traffic conditioner to improve the bandwidth and delay assurance of differentiated services. A prototype has been developed to study the end-to-end behavior of customer aggregates. In particular, this new approach improves performance in the following manner: (1) fairness issues among aggregated customer traffic with different number of micro-flows in an aggregate, interaction of non-responsive traffic (UDP) and responsive traffic (TCP), and the effect of different packet sizes in aggregates; (2) improved transactions per second for short TCP flows; and (3) reduced inter-packet delay variation for streaming UDP traffic. Experiments are also performed in a topology with multiple congestion points to show an improved treatment of conformant aggregates, and the ability of AFC to handle multiple aggregates and differing target rates.