Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among vehicles.However,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users.Trust mechanisms serve as an effective solution to this issue.In recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area.In this paper,we propose a distributed vehicular network scheme based on trust scores.Specifically,the designed architecture partitions multiple vehicle regions into clusters.Then,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by vehicles.Additionally,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation model.Our proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among vehicles.Both theoretical and experimental analysis show that our scheme outperforms the compared schemes.

V2X Offloading and Resource Allocation in SDN-Assisted MEC-Based Vehicular Networks

As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing(MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking(SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything(V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others’ algorithms.

Mobility-Aware Partial Computation Offloading in Vehicular Networks: A Deep Reinforcement Learning Based Scheme

Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.

Joint Allocation of Wireless Resource and Computing Capability in MEC-Enabled Vehicular Network

In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE(VUE),a Joint Allocation of Wireless resource and MEC Computing resource(JAWC)algorithm is proposed.The JAWC algorithm includes two steps:V2X links clustering and MEC computation resource scheduling.In the V2X links clustering,a Spectral Radius based Interference Cancellation scheme(SR-IC)is proposed to obtain the optimal resource allocation matrix.By converting the calculation of SINR into the calculation of matrix maximum row sum,the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced.In the MEC computation resource scheduling,by transforming the original optimization problem into a convex problem,the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained.The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network.

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology,a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications.Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs,which bring great challenges to the on-board system.It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience(QoE).In this paper,a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing(MEC)enabled vehicular networks is presented.To minimize the delay cost and energy consumption,a task execution optimization model is formulated to assign the task to the available service nodes,which includes the service vehicles and the nearby Road Side Units(RSUs).For the task offloading process via Vehicle to Vehicle(V2V)communication,a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence.Next,an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption.To maintain the diversity of the population,the crossover operator is introduced to exchange information among individuals.Besides,the crossover probability is defined to improve the search ability and convergence speed of the algorithm.Meanwhile,an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations.Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.

Machine learning in vehicular networking:An overview

As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

Performance Analysis for Large Intelligent Surface Assisted Vehicular Networks

Large intelligent surface(LIS)is considered as a new solution to enhance the performance of wireless networks[1].LIS comprises low-cost passive elements which can be well controlled.In this paper,a LIS is invoked in the vehicular networks.We analyze the system performance under Weibull fading.We derive a novel exact analytical expression for outage probability in closed form.Based on the analytical result,we discuss three special scenarios including high SNR case,low SNR case,as well as weak interference case.The corresponding approximations for three cases are provided,respectively.In order to gain more insights,we obtain the diversity order of outage probability and it is proved that the outage probability at high SNR depends on the interference,threshold and fading parameters which leads to 0 diversity order.Furthermore,we investigate the ergodic achievable rate of LIS-assisted vehicular networks and present the closed-form tight bounds.Similar to the outage performance,three special cases are studied and the asymptotic expressions are provided in simple forms.A rate ceiling is shown for high SNRs due to the existence of interference which results 0 high SNR slope.Finally,we give the energy efficiency of LIS-assisted vehicular network.Numerical results are presented to verify the accuracy of our analysis.It is evident that the performance of LIS-assisted vehicular networks with optimal phase shift scheme exceeds that of traditional vehicular networks and random phase Received:Aug.6,2020 Revised:Nov.17,2020 Editor:Caijun Zhong shift scheme significantly.

Enabling QoE-aware Mobile Cloud Video Recording over Roadside Vehicular Networks

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

Compact passing algorithm for signalized intersection management based on vehicular network

To improve the traffic efficiency at signalized intersections, a compact passing algorithm is proposed based on a vehicular network. Its basic principle is that several waiting vehicles after the stop line of the considered intersection should simultaneously start in green periods. Thus, more vehicles can pass the intersection in a green period. Then, the having passed vehicles should follow the planned trajectories to enlarge their longitudinal clearances. Phase timing is not considered in the compact passing algorithm, and therefore, the proposed compact passing algorithm can be combined with other algorithms on phase timing to further improve their performances. Several simulations were designed and performed to verify the performance of the proposed algorithm. The simulation results show that the proposed algorithm can increase the number of completed vehicles and decrease the travel time in the signalized intersections managed by fixed-time and vehicle actuated algorithms, which indicates that the proposed algorithm is effective for improving the traffic efficiency at common signalized intersections.

TBDDoSA-MD:Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network(SDVN)

Reliable vehicles are essential in vehicular networks for effective communication.Since vehicles in the network are dynamic,even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences.In this paper,a Trust-Based Distributed DoS Misbehave Detection Approach(TBDDoSA-MD)is proposed to secure the Software-Defined Vehicular Network(SDVN).A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood.It uses the jamming technique by sending unnecessary signals in the network,as a result,the network performance degrades.Attacked vehicles in that network will no longer meet the service requests from other vehicles.Therefore,in this paper,we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles.Trust values are calculated based on direct trust and recommendations(indirect trust).These trust values help to decide whether a vehicle is legitimate or malicious.We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages.The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator,which uses OMNeT++and Simulation of Urban Mobility(SUMO).We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework(TBF)scheme using the following performance parameters such as detection accuracy,packet delivery ratio,detection time,and energy consumption.Simulation results show that the proposed work has a high detection accuracy of more than 90%while keeping the detection time as low as 30 s.

A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network

An Information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been designed,including Named Data Networking(NDN),which is designed around content delivery instead of hosts.As data is the central part of the network.Therefore,NDN was developed to get rid of the dependency on IP addresses and provide content effectively.Mobility is one of the major research dimensions for this upcoming internet architecture.Some research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer mobility.To solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular Networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the consumer.When a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring routers.Thus,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster overhead.Moreover,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been described.For the validation of our proposed scheme,MATLAB simulations are conducted.The simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction ratio.The proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobility speeds of 5 m/s,10 m/s,15 m/s,20 m/s,and 25 m/s,and the efficiency of the packet delivery ratio is improved by up to 1.2048%,5.0632%,6.4935%,6.943%,and 8.4507%.Furthermore,the simulation results of our proposed scheme show better efficiency in terms of Packet Delivery Ratio(PDR)from 0.071 to 0.077 and a decrease in the handover delay from 0.1334 to 0.129.

A Systematic Approach for Cyber Security in Vehicular Networks

Vehicular Networks (VANET) are the largest real-life paradigm of ad hoc networks which aim to ensure road safety and enhance drivers’ comfort. In VANET, the vehicles communicate or collaborate with each other and with adjacent infrastructure by exchanging significant messages, such as road accident warnings, steep-curve ahead warnings or traffic jam warnings. However, this communication and other assets involved are subject to major threats and provide numerous opportunities for attackers to launch several attacks and compromise security and privacy of vehicular users. This paper reviews the cyber security in VANET and proposes an asset-based approach for VANET security. Firstly, it identifies relevant assets in VANET. Secondly, it provides a detailed taxonomy of vulnerabilities and threats on these assets, and, lastly, it classifies the possible attacks in VANET and critically evaluates them.

A Trusted and Privacy-Preserving Carpooling Matching Scheme in Vehicular Networks

With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportation efficiency and solving environmental problems. However, attackers usually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and privacy-preserving carpooling matching scheme in Vehicular Networks (TPCM). TPCM scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopted the privacy set intersection technology based on the Bloom filter to match the passengers with the vehicles to achieve the purpose of protecting privacy and meeting the individual needs of passengers simultaneously. TPCM scheme adopted a multi-faceted trust management model, which calculated the trust value of different travel preferences of vehicle based on passengers’ carpooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical system.In order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire systemorchestration.Hence,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised earlier.By performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy.Additionally,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions.Our model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model uploading.Second,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks.The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

Information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

Trajectory-Based Data Forwarding Schemes for Vehicular Networks

This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.

Heterogeneous Vehicular Networks for Social Networks： Requirements and Challenges

Heterogeneous vehicular networks （HetVNETs） are regarded as a promising technique for meeting various requirements of intelli- gent transportation system （ITS） services. With the rapid development of mobile Internet in the past decade, social networks （SNs） have become an indispensable part of human life. Based on this indivisible relationship between vehicles and users, social charac- teristics and human behaviors can significantly affect vehicular network performance. Hence, we firstly present two architectures for SNs by introducing social characteristics into the HetVNETs. Then, several user cases are also given in this paper, in which service requirements are analyzed simultaneously. At last, we briefly discuss potential challenges raised by the HetVNETs for SNs.