Traditional federated learning (FL) approaches face significant challenges when applied to dynamic and heterogeneous Internet of Vehicles (IoV) environments, which are characterized by frequent node mobility, unstable communication links, and highly non-independent and identically distributed (Non-IID) data. In particular, decentralized network topologies exacerbate the difficulty of maintaining model consistency, thereby impairing overall learning performance. To address these challenges, we propose a new hierarchical decentralized federated learning (HDFL) framework. This framework combines the advantages of centralization and decentralization, builds a three-layer collaborative structure, and improves communication flexibility through an asynchronous model exchange mechanism between the edge and the client. Simultaneously, HDFL introduces a local fine-tuning strategy based on knowledge distillation to enhance the generalization ability and stability of the model. Experimental results using an urban traffic simulation platform show that HDFL consistently outperforms representative decentralized FL methods in terms of the achieved accuracy and convergence speed under heterogeneous IoV environments.

This paper introduces a real-time semantic communication system for remote driving, addressing the challenges of video transmission over constrained and fluctuating wireless communication links. Instead of transmitting raw video streams, the proposed system extracts and transmits compact semantic representations, significantly reducing bandwidth requirements while preserving task-relevant visual information. An end-to end semantic encoding-decoding pipeline enables low latency operator-view reconstruction with low latency, improving robustness without relying on high-throughput links. Implemented and evaluated on the real-time prototype, the proposed system demonstrates the practicality of semantic communication for enhancing responsiveness and reliability in remote driving scenarios.

Accurate traffic congestion prediction is essential for optimizing urban traffic management and mitigating congestion and its consequences. However, conventional prediction models often struggle to simultaneously capture long-term periodic patterns and short-term fluctuations, leading to low prediction accuracy and computational inefficiencies. To overcome this limitation, we propose a knowledge distillation-based framework for enhanced long and short term road traffic prediction. The framework employs a teacher-student architecture, where the teacher model utilizes long-term historical data and a dynamic adjacency matrix to extract periodic traffic patterns, while the student model captures short-term variations and integrates distilled long-term knowledge to enhance responsiveness to sudden congestion changes. To resolve the dimensional mismatch between long-term and short-term feature representations, we introduce a feature alignment mechanism that reduces the dimensionality of high-dimensional intermediate outputs from the teacher model. Experimental evaluations demonstrate that our approach significantly outperforms baseline models, such as Graph Convolutional Gated Recurrent Units, Spatio- Temporal Graph Convolutional Networks and Long Short-Term Memory, in terms of Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error. Moreover, the proposed framework maintains high prediction accuracy even in scenarios with severe traffic fluctuations, offering an efficient and robust solution for traffic congestion forecasting.

Unmanned Aerial Vehicles (UAVs) provide a wide range of opportunities for the service sector, such as last-mile deliveries, surveillance, and data gathering. Finding an optimal collision-free path is necessary to enable UAVs to complete their mission successfully. However, most of the existing path-planning solutions focus on a single UAV and a single target only while overlooking the case of a UAV swarm that collaborates to find collision-free service delivery paths spanning multiple targets or points of interest within a three-dimensional (3D) map. Therefore, to overcome this limitation we propose a novel Multi-UAV Direct Goal Bias Rapidly Exploring Random Trees Star (MDGB-RRT*) algorithm to provide robust path solutions where multiple UAVs traverse a shared 3D urban environment, with each having unique identified goal positions whilst traversing the map with collision-free guarantees. In contrast to RRT*, MDGB-RRT* directly connects the expanding tree to the target location within a predefined search radius, which reduces both the initial pre-validated path length and computation time. The simulation results obtained show that MDGB-RRT* achieves a notable performance advantage compared to existing algorithms for both single and dual-UAV urbanised 3D environment. In addition, MDGB-RRT* maintains its performance advantages with the introduction of two UAVs within the same environment.

Unmanned Aerial Vehicles (UAVs) offer new ways to fulfil a variety of urban transportation and service solutions. The ability to successfully plan and re-plan paths across a complex urban environment remains an unsolved significant problem. New Q-learning approaches have potential to address this problem, however they must first learn complex environment spaces. A traditional challenge within this field is the selection of suitable learning hyper-parameters that assist a Q-learning algorithm in achieving an optimal learning policy. It is known that testing and evaluating multiple hyper-parameter combinations is computationally expensive. Thus, this paper proposes a new method for hyper-parameter self-tuning, cyclically assigning hyper-parameters within a single learning process, eliminating the need to experimentally seek optimal hyper-parameter value combinations. Evaluation of the captured results show, training with cyclical hyper-parameter exploration instead of fixed values, achieves improved path generation, while reducing the cumulative learning time required. Although the focus of this approach is centred around a Multi Q-table Path Planning solution, this work presents a practical tool applicable to Reinforcement Learning techniques generally.

In the rapidly evolving landscape of federated learning (FL), hierarchical architectures are pivotal for improving computational efficiency and safeguarding data privacy. A key challenge in this research area is the optimal selection of edge servers, crucial for executing distributed learning tasks across multiple clients and servers efficiently. Traditional selection methods falter due to their inability to dynamically handle the uncertainties in network conditions and server capabilities. To addressing this weakness, we propose a fuzzy logic-based approach that optimizes edge server selection in a novel smart way, thus enhancing resource allocation by efficiently handling the unpredictable nature of network environments and servers performance. This method is integrated with a previously developed scheme for selecting an optimal subset of clients, thereby establishing a comprehensive framework that significantly boosts the performance and reliability of FL networks. The performance of our approach is validated through real-world experiments and the results demonstrate its superiority over existing methods in terms of accuracy and processing time.

The Internet of Vehicles (IoV) enhances smart city functionalities by interconnecting diverse components, yet it introduces significant challenges in terms of user privacy, communication efficiency, and energy consumption. Traditional federated learning frameworks, while adept at addressing these concerns, fall short in personalization due to heterogeneous data distributions among clients. To overcome this, we introduce Hier-FedMeta, a novel framework that combines hierarchical federated learning with meta-learning to provide tailored and efficient solutions. Our comparative analyses with four estab-lished methods show Hier-FedMeta's superior generalization capabilities and adaptability, achieving enhanced performance with minimal computational overhead after just one update step. Furthermore, our in-depth analysis of aggregation parameters offers valuable insights for the optimization of hierarchical federated meta-learning architectures, representing a significant step forward in personalized learning for IoV in smart cities.

The number of vehicles in developed countries has grown more rapidly than available road capacity, resulting in increased congestion, air pollution, and more accidents. A recent UN report predicts that the increasing size of cities and levels of population mobility will mean 2.9 billion vehicles on the road in cities alone by 2050. To mitigate the consequences of this increase without dramatically increasing the number of built roads, novel methods to better utilise existing road capacity are required. To that end, this paper introduces COALITION, a cognitive radio-enabled probabilistic offloading of congested lanes, as an innovative solution to efficiently handle traffic congestion in urban areas. This solution builds upon and improves the performance of our previous work, named CRITIC, and makes use of Electric Connected and Autonomous Vehicles (ECAVs) features to maximize the usage of road capacity through opportunistic exploitation of under-utilized reserved lanes while fostering the use of electric vehicles to support carbon neutral transportation objectives. Simulation results have proven the effectiveness of COALITION and its potential impact in real-world scenarios.

Reducing unexpected urban traffic congestion caused by en-route events (e.g., road closures, car crashes, etc.) often requires fast and accurate reactions to choose the best-fit traffic signals. Traditional traffic light control systems, such as SCATS and SCOOT, are not efficient as their traffic data provided by induction loops has a low update frequency (i.e., longer than 1 minute). Moreover, the traffic light signal plans used by these systems are selected from a limited set of candidate plans pre-programmed prior to unexpected events’ occurrence. Recent research demonstrates that camera-based traffic light systems controlled by deep reinforcement learning (DRL) algorithms are more effective in reducing traffic congestion, in which the cameras can provide high-frequency high-resolution traffic data. However, these systems are costly to deploy in big cities due to the excessive potential upgrades required to road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles (UAVs) can play a crucial role in dealing with unexpected traffic congestion because UAVs with onboard cameras can be economically deployed when and where unexpected congestion occurs. Then, we propose a system called "AVARS" that explores the potential of using UAVs to reduce unexpected urban traffic congestion using DRL-based traffic light signal control. This approach is validated on a widely used open-source traffic simulator with practical UAV settings, including its traffic monitoring ranges and battery lifetime. Our simulation results show that AVARS can effectively recover the unexpected traffic congestion in Dublin, Ireland, back to its original uncongested level within the typical battery life duration of a UAV.

Network congestion is a major issue affecting communications in Connected and Autonomous Vehicles (CAVs) under high vehicle density scenarios. The common idea used by emergency message broadcast protocols to overcome this challenge is to reduce the number of retransmissions. This is achieved by suppressing redundant retransmissions while maintaining broadcast reliability. In this paper, we analyze the problem of inaccurate suppression of redundant retransmissions in delay-based broadcast protocols and propose uHBS (unHurried Broadcast Suppression), a new more efficient broadcast suppression mechanism. uHBS avoids inaccurate decisions to suppress or forward an emergency message by considering the occurrence of duplicate receptions of this message and using an indication about the channel’s busy status. Next, we use the proposed uHBS mechanism as a basis for designing uHBS-DP (uHBS based Dissemination Protocol), a novel delay-based protocol for broadcasting emergency messages in urban vehicular networks. The simulation results show that uHBS-DP, using the proposed broadcast suppression mechanism, significantly improves the efficiency of emergency message broadcasting by ensuring high reliability with low broadcasting overhead, compared to two other variants of uHBS-DP that use conventional suppression mechanisms. Furthermore, the results show a substantial improvement, compared to a well known protocol, in terms of reduced collision ratio (up to 36.57%), lower dissemination delay (up to 19.17%) and reduced broadcast overhead (up to 19.28%).

The emergence of wireless body area networks (WBANs) has paved the way for real-time sensing of human biometrics in addition to remote control of smart medical devices, which in turn is revolutionising the smart healthcare industry. However, the limited power and computational capabilities of WBAN sensors make them vulnerable to a myriad of security attacks, thus securing them is paramount to their success and wider adoption. Received signal strength indicator (RSSI) secure key extraction (SKE) methods are used for securing WBAN sensors. However, such methods may suffer from stagnant RSSI values, significantly increasing the secret keys construction time. To remedy this, we propose a new method that involves one of the two sensors being mobile and thus can be picked up and moved around. This results in the stimulation of RSSI values which in turn improves the quality of the generated keys and thus shortening the execution times of the SKE process. The evaluation results highlighted the effectiveness of our method.

This paper introduces an original multi destination path planning approach for Unmanned Aerial Vehicles (UAVs) named MQTPP (Multi Q-Table Path Planning). MQTPP aims to reduce the computational burden of cyclical/continuous path planning through a Q-learning planning process whilst overcoming the fixed path origin problem. The preliminary performance evaluation results indicate that MQTPP performs well for longer paths, and allows for more efficient re-planning should mission objectives or environmental topography change.

The application spectrum of Unmanned Aerial Vehicles (UAVs) has been expanding rapidly in recent years, beyond their intended original use in the military sector, to target many strategic civilian sectors such as agriculture, rescues, surveillance etc. In this paper, we focus on the application of UAVs in disaster scenarios to provide efficient and rapid alternative to the damaged communication infrastructure. In this context, a swarm of UAVs is deployed as a wireless mesh network to provide connectivity to users on the ground and ensure real-time communication between rescue teams. To this end, we proposed a new routing protocol, named Particle Swarm Optimization enabled OLSR (PSO-OLSR) to create and maintain connectivity within the mesh network. PSO-OLSR consists of three phases; the initialization phase where OLSR is used to compute routes that enable UAVs to operate collaboratively. Afterwards, due to UAVs’ high speeds, their dynamically changing topologies, and their three-dimensional mobility, a Particle Swarm Optimization (PSO) model is designed to maintain the UAVs connectivity within the deployed mesh network. PSO-OLSR’s performance has been evaluated through simulation and compared against the legacy OLSR. The obtained results show that PSO-OLSR outperforms OLSR in terms of packet delivery ratio and end-to-end delay.

Incomplete data due to discrepancies between medical data sources and their storage methods represents a serious concern as it may lead to the loss, or misrepresentation of important medical information. This concern is anticipated to grow in the era of smart healthcare as the volume, variety and speed at which medical data is collected will increase significantly. This paper aims to identify the main causes of data incompleteness in the medical domain, discuss some techniques currently used to build a complete medical picture and highlight how they may affect the consistency and accuracy of the collected data. It also outlines future research directions to efficiently handle data incompleteness and its consequences.

In this paper we will explore novel ways of utilizing inter-vehicle and vehicle to infrastructure communication technology to achieve a safe and efficient lane change manoeuvre for Connected and Autonomous Vehicles (CAVs). The need for such new protocols is due to the risk that every lane change manoeuvre brings to drivers and passengers lives in addition to its negative impact on congestion level and resulting air pollution, if not performed at the right time and using the appropriate speed. To avoid this risk, we design two new protocols, one is built upon and extends an existing protocol, and it aims to ensure safe and efficient lane change manoeuvre, while the second is an original lane change permission management solution inspired from mutual exclusion concept used in operating systems. This latter complements the former by exclusively granting lane change permissions in a way that avoids any risk of collision. Both protocols are being implemented using computer simulation and the results will be reported in a future work.

Periodic beacon messages are one of the building blocks that enable the operation of vehicular ad hoc networks (VANETs) applications. In vehicular networks environments, congestion and awareness control mechanisms are key for a reliable and efficient functioning of vehicular applications. In order to control the channel load, a reliable mechanism allowing real-time measurements of parameters like the local density of vehicles is a must. These measurements can then serve as an input to perform a fast adaptation of the transmit parameters. In this paper, considerable efforts have been directed in the recent years toward designing flexible yet robust protocols solving this problem; yet, very few have considered a proactive adaptation of the transmit parameters as a preventive measure from channel load peaks. To this end, we take the opportunity to introduce prediction and adaptation algorithm (P&A-A), a new congestion control protocol that performs a joint adaptation of the transmit rate and power, relying on an altruistic short-term prediction algorithm that estimates the vehicular density around a given vehicle within the next short while. Additionally, P&A-A adapts the transmit parameters in a way that guarantees the strict beaconing requirements and satisfies the level of awareness required for the operation of most critical VANET applications. The results of the simulations performed in a realistic scenario justify our theoretical considerations and confirm the efficiency and the effectiveness of our protocol by showing significant improvements in terms of network performance (up to 8% and 14% improvement in collision rate; and up to 10% and 20% increase in busy ratio compared to our previous scheme and the ETSI schemes, respectively) as well as the achieved level of awareness (higher coverage with higher transmission rate and power in dense scenarios, and up to 8% and 55% improvement in density perception accuracy compared to our previous scheme and the ETSI schemes, respectively).

According to a recent UN report, continuing population growth and urbanization are expected to increase the world’s urban population by 2.5 billion people by 2050, with 2.9 billion extra vehicles. This massive growth in both population and number of vehicles, together with urban transformation and a trend toward mega cities, creates greater and more challenges for achieving smart transportation goals in smart cities. Therefore, new and more integrated modes of transportation, and environment friendly solutions are required to accommodate the rising demands of high liveability in smarter cities that offer safe, secure, affordable, reliable and sustainable transportation in old and new markets alike.

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

Channel congestion is a well-known problem in wireless networks in general and Vehicular Ad Hoc Networks (VANETs) in particular. Literature solutions propose to alleviate this problem by controlling the network load based on parameters like vehicle density or packet collision rate. In other words, each vehicle will observe the density of vehicles (or the packet collision rate) around itself in a certain time interval, and use this information to adjust its transmit parameters i.e. transmit rate and/or power, the next time it has a beacon to transmit (in the following time window). However, the information collected in the current time window might not still be valid in the next one. In fact, in a highly dynamic network like VANETs, vehicle density, and consequently the busy ratio and the collision rate, might vary a great deal even in the smallest time intervals. To cope with this newly identified problem, we propose a novel vehicle-centric short-term density prediction scheme that estimates the vehicular density around a given vehicle within the next time window allowing each vehicle to adapt its transmit parameters based on the current state of the network (as opposed to the previous state). The accuracy and the efficiency of our proposed scheme is demonstrated in a proof-of-concept case study, showing a significant improvement in terms of network performance.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). Because the human visual system (HVS) is the ultimate receiver of visual signals, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel scalable saliency-aware DCVS codec. Firstly, we perform saliency estimation in the the side information (SI) frame generated at the decoder side and adaptively control the size of region-of-interest (ROI) according to the measurements budget by applying a saliency guided foveation model. Subsequently, based on online estimation of the correlation noise between a non-key frame and its SI, we develop a saliency-aware block compressive sensing scheme to more accurately reconstruct the ROI of each non-key frame. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.

Due to the limited capacity of road networks and sporadic on-route events, road traffic congestions are posing serious problems in most big cities worldwide and resulting in considerable number of casualties and financial losses. In order to deal efficiently with these problems and alleviate their impact on individuals, environment, and economic activities, advanced traffic monitoring and control tools (e.g., SCATS and SCOOT) are being widely used in hundreds of major cities in the world. However, due to increasing road traffic and dynamic spatio-temporal events, additional proactive mechanisms remain needed to prevent traffic congestions. Within this context, we argue that the emergent V2X communication technologies, and especially V2I (Vehicle to Infrastructure), would be of great help. To this end, we investigate in this paper the opportunities that could be offered by V2I technology in improving commuters' journey duration and mitigating the above irritating and frequent problems. We then propose an approach where road-side facilities (e.g. traffic light controllers at road intersections) communicate traffic light cycle information to approaching vehicles. Based on this information, the vehicles collaboratively determine their optimal speeds and other appropriate actions to undertake in order to cross road intersections with minimum delays while ultimately avoiding stoppings. The obtained evaluation results show that our approach achieves a significant gain in terms of the commuters' average travel time reduction.

Data aggregation techniques have emerged as promising solutions for extending Wireless Sensor Networks (WSNs) lifetime. However, this approach suffers from a design issue in delivering the strict requirements needed by some monitoring applications. Carefully balancing Energy, Delay and Accuracy is essential for achieving these requirements. In this work, we focus on distributed data aggregation, where a sensor estimates the network information by the exchange of readings with different priority levels. We then propose an optimal decision policy for scheduling the transmission of the aggregated data at the node level. To model the investigated problem, we first adopt Markov Decision Process (MDP) whereby we define the reward function. Then, we apply a Genetic Algorithm (GA) to find a set of optimal decisions that ensures the best trade-off between energy saving, delay and accuracy of the received data based on their priority level. The simulation results yield excellent performance and our optimization shows a significant enhancement up to 20% compared to the other policies.

By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring the best ways to utilize the spatial/temporal redundancy of video data to achieve efficient sparse representation and reconstruction at the decoder. Unlike the existing DCVS schemes, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel perceptually-aware DCVS codec. Based on online estimation of the correlation noise between a non-key frame and its side information (SI) considering the effect of human visual system (HVS), we design an efficient perceptually-aware block compressive sensing scheme for a non-key frame in our DCVS codec, in order to more accurately reconstruct the salient regions in the video frames. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.

Abstract Session Initiation Protocol (SIP) is widely used as a signaling protocol to support voice and video communication in addition to other multimedia applications. However, it is vulnerable to several types of attacks because of its open nature and lack of a clear defense line against the increasing spectrum of security threats. Among these threats, flooding attack, known by its destructive impact, targets both of SIP User Agent Server (UAS) and User Agent Client (UAC), leading to a denial of service in Voice over IP applications. In particular, INVITE message is considered as one of the major root causes of flooding attacks in SIP. This is due to the fact that an attacker may send numerous INVITE requests without waiting for responses from the UAS or the proxy in order to exhaust their respective resources. Most of the devised solutions to cope with the flooding attack are either difficult to deploy in practice or require significant changes in the SIP servers implementation. Apart from these challenges, flooding attacks are much more diverse in nature, which makes the task of defeating them a real challenge. In this survey, we present a comprehensive study of flooding attack against SIP, by addressing its different variants and analyzing its consequences. We also classify the existing solutions according to the different flooding behaviors they are dealing with, their types, and targets. Moreover, we conduct a thorough investigation of the main strengths and weaknesses of these solutions and deeply analyze the underlying assumptions of each of them for better understanding of their limitations. Finally, we provide some recommendations for enhancing the effectiveness of the surveyed solutions and address some open challenges. Copyright © 2015 John Wiley & Sons, Ltd.

Fast transmission of event-driven warning messages and energy conservation are primary concerns to design robust Hybrid Sensors and Vehicular Networks (HSVNs). In last few years, several protocols have been proposed to address these issues. However, the tradeoff between energy consumption and latency has not been carefully studied and sometimes it is given higher priority than event detection efficiency which remains the first objective of HSVNs. Unlike the existing works, we propose a framework that provides equilibrium between the following three metrics of HSVNs; dangerous events detection, energy consumption and transmission delay. The main advantage of our framework is its ability to ensure an effective detection of dangers on the road and timely transmission of the corresponding warning messages towards the passing by vehicles. This is achieved through the proposed mechanism to switch the sensors' status between sleep and active modes as well as the devised communication scheme between WSN-Gateway and the vehicles cluster head. The preliminary simulation results confirm the effectiveness of our framework and encourage us to pursue further investigation to extend it.

Nowadays, Vehicle to Vehicle (V2V) communication is attracting an increasing attention from car manufacturers due to its expected impact in improving driving safety and comfort. IEEE 802.11P is the primary channel access scheme used by vehicles; however it does not provide sufficient spectrum to ensure reliable exchange of safety information. To overcome this issue, many efforts have been devoted to enhance the frequency spectrum utilization efficiency. To this end, the Cognitive Radio (CR) principle has been applied to assist the vehicles to gain extra bandwidth through an opportunistic use of the unused spectrums in their surrounding. In this paper, we focus on safety messages for which we propose an original scheme that makes their exchange among the nearby vehicles more reliable with a significant reduce in their dissemination delay. This improvement is due to the use of a Hidden Markov Model that enables the prediction of the available channels for the subsequent time slots, leading to faster channel allocation for the vehicles. The obtained simulation results confirm the efficiency of our scheme.

In this paper, we address the beacon congestion issue in Vehicular Ad Hoc Networks (VANETs) due to its devastating impact on the performance of ITS applications. The periodic beacon broadcast may consume a large part of the available bandwidth leading to an increasing number of collisions among MAC frames, especially in case of high vehicular density. This will severely affect the performance of the Intelligent Transportation Systems (ITS) safety based applications that require timely and reliable dissemination of the event-driven warning messages. To deal with this problem, we propose an original solution that consists of three phases as follows; priority assignment to the messages to be transmitted /forwarded according to two different metrics, congestion detection phase, and finally transmit power and beacon transmission rate adjustment to facilitate emergency messages spread within VANETs. Our solution outperforms the existing works since it doesn't alter the performance of the running ITS applications unless a VANET congestion state is detected. Moreover, it ensures that the most critical and nearest dangers are advertised prior to the farther and less damaging events. The simulation results show promising results and validate our solution.

Abstract While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks (WLAN), its study for multi‐hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by manipulating intelligently MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi‐hop fashion over wireless links. In this case, an attempting greedy node, if it behaves similarly as in a WLAN, trying to starve all its neighbors, then its next hop forwarder will be also prevented from forwarding its own traffic, which leads obviously to an end to end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc network, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. Then, we present a new strategy to launch such a greedy attack in a proactive routing based wireless ad hoc network. A detailed description of the proposed strategy is provided along with its validation through extensive simulations. The obtained results show that a greedy node, applying the defined strategy, can gain more bandwidth than its neighbors and keep the end‐to‐end throughput of its own flows highly reasonable. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless mesh Networks (WMNs) are a prominent paradigm of wireless communication that have been widely used in many applications. The growing popularity of such networks opened the door to a profusion of attacks that may target their core functioning leading to a harmful impact on their performance. Hence, the need of robust and fast detection of those attacks became a major prerequisite in order to guarantee an efficient and fair share of network resources among nodes. One of the well known devastating attacks is MAC layer misbehavior which may lead to severe collapse of network performance. In this study, we focus on such misbehavior and in particular on the adaptive greedy behavior of a node in wireless mesh network environment. In such environment, wireless nodes compete to gain access to the medium in order to communicate with a mesh router (MR). In this case, a greedy node may violate the MAC protocol rules to earn extra bandwidth share upon its neighbors. To evade from detection, the cheater node may use more than one technique and switch dynamically between each of them. To counter such misuse, we propose to extend our previous solution, dubbed FLSAC, through the use of a Bayesian statistical model. This new scheme is implemented in conjunction with FLSAC at the mesh router/gateway to monitor the behavior of the attached wireless mesh clients and detect any deviation from the proper protocol rules. The simulation results reveal that this new solution outperforms both of DOMINO and FLSAC in terms of detection rate and accuracy.

The CSMA/CA protocol is the most commonly used medium access mechanism in wireless networks. This protocol schedules properly the access to the medium among all the competing nodes. However, in a hostile environment, such as Mobile Ad Hoc Networks (MANETs), selfish or greedy nodes may prefer to decline the proper use of MAC protocol rules in order to increase their throughput at the expense of their honest neighbors. In this work, we propose a new backoff scheme that allows the neighbors of any node to detect its attempt to deliberately disobey the MAC protocol rules, by either fabricating a small backoff value or refusing to increase its contention window after an unsuccessful transmission. Our scheme uses one way function to generate the backoff values and modifies the RTS frame format by piggybacking the DATA packet's CRC value and the transmission attempt. So any cheating attempt will be detected by the receiver node as well as the other neighbors of the cheater, as long as the monitoring conditions are held. Moreover, our scheme is robust against sender-receiver collusion and provides a novel reaction mechanism to punish the detected cheaters. The simulation results, in different topologies, have confirmed the efficiency of this scheme in terms of fairness index and detection rate.

In wireless ad hoc networks, employing the IEEE 802.11 technology, access to the wireless medium is scheduled according to the CSMA/CA protocol. This protocol was designed with the assumption that nodes would follow properly its operation and never deviate from it. However, it is common to have selfish nodes that may choose to disobey this protocol in order to either gain more bandwidth or degrade the network performance. In this paper we analyze and quantify the impact of each misbehaving technique on the network performance using extensive simulations. We emphasize on the propagation of MAC layer misbehavior effects to the higher layers, particularly to the routing layer, in a pure ad hoc environment.

While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks, its study for multi-hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by intelligently manipulating the MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi-hop fashion over wireless links. Therefore, if a greedy node behaves similarly as in WLAN case, trying to starve its neighbors, then its next hop forwarding node will also be prevented to forward its own traffic, which leads to an end-to-end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc networks, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. We then present a strategy to launch such greedy attack in a proactive routing based wireless ad hoc network. Through the extensive simulations, the obtained results show that by applying the proposed algorithm, a greedy node can gain more bandwidth than its neighbors and keep the end-to-end throughput of its own flows highly reasonable.

The most commonly used medium access mechanism in WLAN is based on the CSMA/CA protocol. This protocol schedules properly the access to the medium among all competing nodes. However, in a hostile environment, such as wireless local area networks (WLANs), selfish or greedy behaving nodes may prefer to decline the proper use of the protocol's rules in order to increase their bandwidth shares at the expense of well behaving nodes. In this paper, we focus on one such misbehavior and in particular on the adaptive greedy misbehavior of a node in the context of wireless local area network environment. In such environment, wireless nodes compete to gain access to the medium and communicate directly with an access point (AP). In this case, a greedy node may violate the common rules in order to earn extra bandwidth upon its neighbors. In order to avoid its detection, this node may adopt intelligently different techniques and switch dynamically between each of them. To counter such a misbehavior, we propose the use of a fuzzy logic technique in a new detection scheme. This scheme, implemented in the access point, monitors the behavior of associated wireless nodes and reports any deviation from the proper use of the CSMA/CA protocol. The simulation results of the proposed scheme show its robustness and ability to detect and identify quickly most of the deviations of an adaptive cheater.

As compared to its wired counterpart, wireless network is relatively new and is exposed to some additional threats specific to the underlying medium. Among these threats the jamming attack which can take place easily due to the open nature of wireless medium. A device or person can continuously emit radio signals to disturb a valid conversation. If it lasts for sometime continuously, it can result in total collapse of a network using single channel. In order to evade a jammer in an ad hoc network, we propose a proactive channel hopping scheme based neighbor correspondence. Rather than detect and react we rely on prevention is better than cure. Each node communicates with its neighbors on different channels, coordinated between them dynamically. Furthermore, the control and data channels of each node are separated. This way redundancy at the node-level is provided so that even if nodes on the jammed channel can not be approached but they still are able to contact others by visiting their control channels; avoiding the node on the jammed channel from starvation. Hence, even if the network is exposed to the jammer, a complete failure is prevented. The simulation results show that our scheme is efficient and is able to reduce the jammer's impact significantly, as compared to the scheme presented in [8].

It is well known that security threats, in wireless ad hoc networks, are becoming a serious problem which may lead to harmful consequences on network performance. Despite that, many routing protocols still not resilient to such threats or their countermeasures are not efficient. Moreover, the vulnerability of MAC layer protocols to some attacks exacerbates the damage caused by the threats at higher layers. Therefore, cooperation between layers in compulsory to face such devastating threats. In this paper, we address a cross-layer attack targeting proactive routing protocols, which is launched at the routing level and reinforced at the MAC layer in order to amplify the resulted damage. We demonstrate that this attack can severely compromise the routing protocols and lead to large data packets loss. We particularly analyze it under the Optimized Link State Routing (OLSR) protocol in detail and propose a lightweight solution to cope with it. The simulation results confirm the efficiency of this solution.

A mobile ad hoc network is made of a collection of nodes connected through a wireless medium and form a wireless multihop network with possible changing topologies. The widely accepted existing routing protocols designed to accommodate the needs of such self-organized networks do not address possible threats or attacks aiming at the disruption of the protocol itself. The widely assumed trusted environment is not really the environment that can be realistically expected in reality. In this paper, we describe a new attack against routing protocols which we call virtual link attack, where a misbehaving node tries to relay any Hello message originated from its neighbors aiming to create fake symmetric links in the network. We show that this attack can severely compromise any routing protocol and may lead to large data packets loss. We specifically analyze this attack under the optimized link state routing (OLSR) protocol in detail and devise a symmetric neighbor verification protocol (SNVP) to alleviate its impact and severity.

In this paper, we address the problem of cooperative black hole attack, one of the major security issues in mobile ad hoc networks. The aim of this attack is to force nodes in the network to choose hostile nodes as relays to disseminate the partial topological information, thereby exploiting the functionality of the routing protocol to retain control packets. In optimized link state routing (OLSR) protocol, if a cooperative black hole attack is launched during the propagation of topology control (TC) packets, the topology information will not be disseminated to the whole network which may lead to routing disruption. In this paper, we investigate the effects of the cooperative black hole attack against OLSR, in which two colluding MPR nodes cooperate in order to disrupt the topology discovery. Then we propose an acknowledgment based technique that overcomes the shortcomings of the OLSR protocol, and makes it less vulnerable to such attacks by identifying and then isolating malicious nodes in the network. The simulation results of the proposed scheme show high detection rate under various scenarios.

Abstract Vehicularad hocnetworks (VANETs) are becoming very popular and a promising application of the so‐called mobilead hocnetworks (MANET) technology. It has attracted recently an increasing attention from many car manufacturers as well as the wireless communication research community. Despite its tremendous potential to enhance road safety and to facilitate traffic management, VANET suffers from a variety of security and privacy issues which may dramatically limit their applications. In this paper, we address the problem of packet dropping attack launched against routing protocol's control packets, which represents one of the most aggressive attacks in MANET. The aim of this attack is to force nodes in the network to choose hostile nodes as relays to disseminate the partial topological information, thereby exploiting the functionality of the routing protocol to retain control packets. In particular, in optimized link state routing (OLSR) protocol, if a collusive packet dropping attack is launched during the propagation of the topology control (TC) packets, the topology information will fail in being disseminated to the entire network, which finally results in routing disruption. This paper focuses on the packet dropping attack, launched against OLSR, where two malicious multipoint relay (MPR) nodes collude to disrupt the topology discovery process. Based on the analysis of the attacker's behavior and the attack's consequence, we propose an acknowledgement‐based mechanism as a countermeasure to enhance the security of OLSR. This mechanism helps the OLSR protocol to be less vulnerable to such attack by detecting and then isolating malicious nodes in the network. The simulation results of the proposed scheme show high detection rate under various scenarios. Copyright © 2008 John Wiley & Sons, Ltd.