Vehicular Communications最新文献

{"title":"A survey on physical layer security for 5G/6G communications over different fading channels: Approaches, challenges, and future directions","authors":"Parmila Devi,&nbsp;Manoranjan Rai Bharti,&nbsp;Dikshant Gautam","doi":"10.1016/j.vehcom.2025.100891","DOIUrl":"10.1016/j.vehcom.2025.100891","url":null,"abstract":"<div><div>The surge in wireless network attacks has intensified the focus on physical layer security (PLS) within academia and industry. As PLS provides security solutions by leveraging the randomness of wireless channels without the need for encryption/decryption keys, fading channels play a major role in PLS solutions. This survey aims to understand the effect of fading on PLS for 5G/6G communications by utilizing various PLS techniques such as beamforming, artificial noise injection, cooperative and opportunistic relaying, physical authentication, and intelligent reflective surface-based PLS over various fading channels. Initially, the role of PLS in 5G/6G communications, its fundamentals, and various techniques available for 5G/6G communications are examined. Since PLS for 5G communications has been extensively studied in the literature, we categorize it into two cases, direct and indirect communications, and provide a comprehensive survey on PLS for 5G communications over various fading channels. Thereafter, we survey the PLS for 6G communications over various fading channels, noting that the work available for PLS in 6G communications is limited and in its early stages. Given the increasing attention on artificial intelligence and machine learning (AI/ML) for wireless communications, this survey also explores PLS based on AI/ML techniques over various fading channels. Finally, the survey concludes with observations on challenges and future directions.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100891"},"PeriodicalIF":5.8,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SC-VDTwinAuth: Smart-contract Assisted Handover Authentication Protocol for Vehicular Digital Twin Network","authors":"Deepika Gautam,&nbsp;Garima Thakur,&nbsp;Sunil Prajapat,&nbsp;Pankaj Kumar","doi":"10.1016/j.vehcom.2025.100890","DOIUrl":"10.1016/j.vehcom.2025.100890","url":null,"abstract":"<div><div>Vehicular digital twin network is partitioned into multiple networks either due to the geographical differences or their accelerating expansion, which necessitates a secure and incessant transition of cross-regional vehicles. Therefore, in this dynamic topology, the handover process for cross-regional vehicles becomes imperative. The literature encompasses an abundance of blockchain-based handover mechanisms, specifically designed for vehicle and the roadside units. Unfortunately, some of these are not feasible for vehicular digital twin networks due to their high computational overhead and susceptibility to security threats. Therefore, this paper presents a handover authentication protocol for the blockchain-based vehicular digital twin networks, leveraging the smart contract. It entirely depends on digital twin, which reduces the burden of the vehicle and enhances the efficiency and security of the handover process. Security strengths and competency against attacks like sybil and impersonation attacks are investigated through a real-or-random oracle model (ROR) and non-mathematical analysis. The operational analysis evaluates the proposed mechanism with pertinent works based on security functionalities, computation, and communication overhead. Moreover, to illustrate suggested smart contract's viability and the reasonable cost of blockchain consumption, it is implemented via the Ethereum test network. Hence, obtained results indicate the relevancy of the mechanism for vehicular digital twin networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100890"},"PeriodicalIF":5.8,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource allocation in unmanned aerial vehicle networks: A review","authors":"Siva Sai ,&nbsp;Sudhanshu Mishra ,&nbsp;Vinay Chamola","doi":"10.1016/j.vehcom.2025.100889","DOIUrl":"10.1016/j.vehcom.2025.100889","url":null,"abstract":"<div><div>Currently, resource allocation in Unmanned Aerial Vehicles (UAVs) is a major topic of discussion among industrialists and researchers. Considering the different emerging applications of UAVs, if the resource allocation problem is not addressed effectively, the upcoming UAV applications will not serve their proposed purpose. Although there are numerous and diverse research works addressing the resource allocation in UAVs, there is an evident lack of a comprehensive survey describing and analyzing the existing methods. Addressing this research gap, we present an extensive review of the resource allocation in UAVs. In this work, we classify the existing research works based on four criteria - optimization goal-based classification, mathematical model-based classification, game theory framework-based classification, and machine learning model-based classification. Our findings revealed that the mathematical models are relatively more explored to solve the resource allocation problem in UAVs. Researchers have explored a variety of game theory techniques, like the Stackelberg model, mean-field game theory, cooperative games, etc., for optimized resource allocation in UAVs. The optimization of energy and throughput factors is more seen in the literature compared to the other optimization goals. We also observed that the reinforcement learning technique is a heavily exploited technique for resource allocation in UAVs compared to all other machine learning-based methods. We have also presented several challenges and future works in the field of resource allocation in UAVs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100889"},"PeriodicalIF":5.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense capsule stacked auto-encoder model based DDoS attack detection and hybrid optimal bandwidth allocation with routing in VANET environment","authors":"Murali Krishna Tanati ,&nbsp;Manimaran Ponnusamy","doi":"10.1016/j.vehcom.2025.100888","DOIUrl":"10.1016/j.vehcom.2025.100888","url":null,"abstract":"<div><div>The vehicle ad hoc network, or VANET, is a fantastic tool for smart transport since it improves efficiency, management, traffic safety, and comfort. Distributed Denial of Service (DDoS) attacks on VANET infrastructure have the potential to compromise traffic safety by causing collisions and fatalities. Therefore, while integrating VANETs into intelligent transport networks, the pertinent security issues must be addressed. This paper provides an efficient routing optimization as well as a deep learning-based attack detection approach. The input data are first collected from publically accessible datasets. After that, a unique Dense Capsule Stacked Auto Encoder (DCSAE) network is developed to detect the presence of DDoS attacks in the inputs. Here, the detection method is enabled by the hybridization of the Capsule Network with a Stacked Auto Encoder. Moreover, the Improved Fire Hawks Optimization Algorithm (IFHOA) is employed to refine the proposed detection technique. Once assaults have been discovered, bandwidth is allocated using the Hybrid Remora Whale Optimization (HRWO) approach. Finally, an Improved Osprey Optimization (IOO) method is utilized to identify a better routing path by taking into account aspects such as energy usage, delay, and drop. The DDoS SDN dataset is employed to implement the proposed method. In the results section, the suggested technique is compared to existing methods in terms of recall, accuracy, precision, F1 score, Mean Absolute Error (MAE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), and consumption of energy. The proposed model achieved an accuracy of 94.07 % while achieving the precision, recall, and F1-score of 94.2 %, 93.33 %, and 93.88 %, respectively. The model achieved the MAE of 0.132, delay of 4812.976, energy consumption of 40.13 %, PDR of 95.1805, and PLR of 3.6816 %, respectively.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100888"},"PeriodicalIF":5.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel cylindrical filtering-based greedy perimeter stateless routing scheme in flying ad hoc networks","authors":"Amir Masoud Rahmani ,&nbsp;Amir Haider ,&nbsp;Khursheed Aurangzeb ,&nbsp;May Altulyan ,&nbsp;Entesar Gemeay ,&nbsp;Mohammad Sadegh Yousefpoor ,&nbsp;Efat Yousefpoor ,&nbsp;Parisa Khoshvaght ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100879","DOIUrl":"10.1016/j.vehcom.2025.100879","url":null,"abstract":"<div><div>Flying ad hoc networks (FANETs) are a new example of ad hoc networks, which arrange unmanned aerial vehicles (UAVs) in an ad hoc form. The features of these networks, such as the movement of UAVs in a 3D space, high speed of UAVs, dynamic topology, limited resources, and low density, have created vital challenges for communication reliability, especially when designing routing methods in FANETs. In this paper, a novel cylindrical filtering-based greedy perimeter stateless routing scheme (CF-GPSR) is suggested in FANETs. In CF-GPSR, cylindrical filtering reduces the size of the initial candidate set to accelerate the selection of the next-hop node. In this phase, the formulation of the cylindrical filtering construction process is expressed in the cylindrical coordinate system because the filtered area is a cylinder enclosed within the communication range of flying nodes. The cylindrical filtering construction process includes three steps, namely transferring coordinate axes, rotating coordinate axes, and cylinder construction. When selecting the next-hop node, CF-GPSR first uses this cylindrical filtering to limit the candidate set of each flying node. Then, CF-GPSR decides on the best next-hop UAV based on a merit function, which includes four criteria, namely velocity factor, ideal distance, residual energy, and movement angle, and selects a candidate node with the highest merit value as the next-hop UAV. Finally, the simulation process is performed using the NS 3.23 simulator, and four simulation scenarios are defined based on the number of UAVs, the communication area of nodes, network connections, and the size of packets to evaluate CF-GPSR. In the simulation process, CF-GPSR is compared with the three GPSR-based routing schemes, namely UF-GPSR, GPSR-PPU, and GPSR in terms of delay, data delivery ratio, data loss ratio, and throughput. In the first scenario, namely the change in the number of flying nodes, CF-GPSR improves delay, PDR, PLR, and throughput by 17.34%, 4.83%, 16%, and 7.05%, respectively. Also, in the second scenario, namely the change in communication range, the proposed method optimizes delay, PDR, PLR, and throughput by 4.91%, 5.71%, 6.12%, and 8.45%, respectively. In the third scenario, namely the change in the number of connections, CF-GPSR improves EED, PDR, PLR, and throughput by 18.41%, 9.09%, 9.52%, and 7.03%, respectively. In the fourth simulation scenario, namely the change in the packet size, CF-GPSR improves delay, PDR, PLR, and throughput by 14.81%, 19.39%, 7.19%, and 0.39%, respectively.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100879"},"PeriodicalIF":5.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RORA: Reinforcement learning based optimal distributed resource allocation strategies in vehicular cognitive radio networks for 6G","authors":"Mani Shekhar Gupta ,&nbsp;Akanksha Srivastava ,&nbsp;Krishan Kumar","doi":"10.1016/j.vehcom.2025.100882","DOIUrl":"10.1016/j.vehcom.2025.100882","url":null,"abstract":"<div><div>The next generation (5G/B5G) vehicular cognitive radio networks (VCRNs) flag the track to intelligence-based autonomous driving in the initiation of future wireless networking and make daily vehicular operation more convenient, greener, efficient, and safer. However, with the continuous evolution of vehicles, the vehicular network becomes large-scale, dynamic, and heterogeneous, making it tough to fulfill the strict necessities, such as high security, resource allocation, massive connectivity, and ultralow latency. The combination of cognitive radio (CR) networks (different network coexistence) and machine learning (ML) has arisen as an influential artificial intelligence (AI) approach to make both the communication system and vehicle more adaptable and efficient. Naturally, applying ML to VCRNs has become an active research area and is being extensively considered in industry and academia. In this work, a reinforcement learning (RL) based optimal resource allocation (RORA) technique is proposed to solve the myopic decision-making problem by an autonomous vehicle (RL agent) takes its action to select the power level and optimal sub-band and maximize long-term rewards with a maximum payoff in VCRNs. The aim of this work is to design and implement an intelligent, resource allocation framework that ensures efficient and adaptive spectrum utilization while minimizing communication latency, energy consumption, and transmission cost in VCRNs. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network inter-working with Wi-Fi network with constant inter-space between Wi-Fi access points (APs) installed along the pathway is analysed. This framework is further analysed with variable inter-space between Wi-Fi APs. The key research problem addressed in this work is the challenge of optimizing spectrum and power allocation in highly dynamic vehicular environments characterized by rapid mobility, fluctuating network conditions, and interference from multiple vehicular CR nodes. The results show that the proposed RORA technique is more operative and outperforms other resource allocation schemes in terms of prediction accuracy and throughput.</div></div><div><h3>Impact Statement</h3><div>In this work, we proposed a machine learning-based technique applied to vehicular networks and opportunistic spectrum access parts in cognitive radio networks. The proposed technique envisions ways of enabling AI toward a future intelligent transportation system (ITS), including network intelligentization and the development of intelligent radio (IR).</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100882"},"PeriodicalIF":5.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing connected vehicle security through real-time sensor anomaly detection and recovery","authors":"Akshit Singh ,&nbsp;Heena Rathore","doi":"10.1016/j.vehcom.2025.100876","DOIUrl":"10.1016/j.vehcom.2025.100876","url":null,"abstract":"<div><div>Connected Vehicles (CVs) are a crucial element in the evolution of smart transportation systems, utilizing communication and sensing technologies to interact with each other and with infrastructure. As these vehicles become more interconnected, the risk of their components being affected by anomalies or intentional malicious attacks grows. It is essential, therefore, to identify and filter out any anomalous data to ensure reliable decision-making. Existing solutions for anomaly detection in CVs include methods such as kalman filter, cumulative summation, convolutional neural networks and other machine learning models. However, a prevalent issue is the limited universality of anomaly datasets along with the variability introduced by simulated data. Additionally, there are few methods for recovering the network from anomalies using sensor information. In this paper, we address these limitations by utilizing the Tampa CV (TCV) dataset and incorporating anomalies such as bias, noise, and spikes. Furthermore, we present a novel method for real-time anomaly detection in CVs using Bayesian Online Change Point Detection (BOCPD). We propose a unique recovery mechanism that employs Bayesian forecasting to interpret identified anomalies, marking the first of its kind in this field. This approach significantly enhances the security of CV systems by seamlessly merging instant detection with swift recovery, ensuring continuous protection against data integrity threats. Results demonstrate that the proposed model achieves an average accuracy improvement of 53.83 % over other machine learning models. This paper makes advancement through real-time anomaly detection and recovery mechanisms, thus significantly improving the resilience of smart transportation systems against data integrity threats.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100876"},"PeriodicalIF":5.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-enhanced multi-region data aggregation for Internet of Vehicles","authors":"Haibin Dai ,&nbsp;Yuanyuan Zhang ,&nbsp;Li Lin ,&nbsp;Jinbo Xiong ,&nbsp;Youliang Tian","doi":"10.1016/j.vehcom.2025.100886","DOIUrl":"10.1016/j.vehcom.2025.100886","url":null,"abstract":"<div><div>Data aggregation is evolving into an extremely crucial role for facilitating decision-making in Internet of Vehicles (IoV). Multi-region data is a typical attribute of IoV, containing sensitive information and driving trajectories. However, the existing privacy-preserving schemes face problems such as regional statistics, messages integrity, and collusion attacks. In order to tackle this challenge, we propose a privacy-enhanced multi-region data aggregation (PRDA) scheme for IoV. Specifically, PRDA protects both sensing data and location as masked values by multi-secret sharing. We design regional vectors to generate mask keys using symmetric bivariate polynomial without interaction. In addition, vehicles spontaneously generate verifiable and aggregatable signature to ensure messages integrity in insecure communication networks. Batch verification of bilinear pairing can improve efficiency while resisting tampering attacks by malicious adversaries. Experiments demonstrate that as the number of regions increases, comparing with existing works, PRDA has lower communication overhead, and decreases computational cost by over 32.6%.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100886"},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considering both energy effectiveness and flight safety in UAV trajectory planning for intelligent logistics","authors":"Zhiyang Liu ,&nbsp;Liuhuan Li ,&nbsp;Xiao Zhang ,&nbsp;Wan Tang ,&nbsp;Zhen Yang ,&nbsp;Ximin Yang","doi":"10.1016/j.vehcom.2025.100885","DOIUrl":"10.1016/j.vehcom.2025.100885","url":null,"abstract":"<div><div>In low-altitude economic logistics scenarios, trajectory planning for unmanned aerial vehicles (UAVs) can be treated as a typical traveling salesman problem (TSP). High-rise buildings in urban areas not only severely impact the flight safety of UAVs, but also increase their energy consumption when avoiding obstacles, thereby affecting their delivery ranges. To address these issues, this paper proposes a two-stage trajectory planning solution called ACO-DQN-TP for logistics UAVs. In the first stage, the ant colony optimization (ACO) algorithm is applied to solve the sequence for multi-target point deliveries, to obtain the optimal flight paths. The ant tabu table is reopened to allow for retracing of the movement paths in order to avoid forward search dilemmas. In the second stage, a deep Q-network (DQN) is combined with the traditional artificial potential field method to enhance the interaction between UAVs and their environment. The rewards are accumulated using two potential functions generated based on the target points and obstacles, to minimize the changes in the yaw angles and smooth the flight trajectory of the UAV. Simulation experiments were conducted on UAV trajectory planning for delivery missions with four to ten target points. The simulation results show that the average path length obtained by ACO-DQN-TP is 65% and 79% shorter than that of Greedy+DQNPF and BACO, respectively, and the sum of turning angles along the path is 56% of Greedy DQNPF and 72% of BACO on average. It indicates that the proposed ACO-DQN-TP scheme not only optimizes delivery routes compared to traditional ACOs but also effectively controls the magnitude of the changes in heading angle during flight. This ensures flight safety for the UAV through obstacle avoidance while reducing flight energy consumption. In particular, the heading angle optimization mechanism proposed in this paper has universal guiding significance for low-altitude flights in the areas of traffic and transportation using UAVs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100885"},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-assisted Internet of vehicles: A framework empowered by reinforcement learning and Blockchain","authors":"Ahmed Alagha ,&nbsp;Maha Kadadha ,&nbsp;Rabeb Mizouni ,&nbsp;Shakti Singh ,&nbsp;Jamal Bentahar ,&nbsp;Hadi Otrok","doi":"10.1016/j.vehcom.2025.100874","DOIUrl":"10.1016/j.vehcom.2025.100874","url":null,"abstract":"<div><div>This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination processes are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for efficient and autonomous UAV coordination, and finally, a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the coordination between the selected UAVs is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). MDRL offers decentralized control and intelligent decision-making for the UAVs to maintain coverage and connectivity over the assigned vehicles. The evaluation results demonstrate that the proposed selection mechanism improves the stability of the selected relays, while MDRL maximizes the coverage and connectivity achieved by the UAVs. Both methods show superior performance compared to several benchmarks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100874"},"PeriodicalIF":5.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}