Vehicular Communications最新文献

{"title":"Millimeter-wave vehicular collaborative communication assisted by intelligent reflecting surface","authors":"Xiangrui Guan,&nbsp;Jianbin Xue,&nbsp;Han Zhang,&nbsp;Jialing Xu","doi":"10.1016/j.vehcom.2025.100940","DOIUrl":"10.1016/j.vehcom.2025.100940","url":null,"abstract":"<div><div>The combination of the intelligent reflecting surface (IRS) with reconfigurable wireless propagation environment and the millimeter-wave (mmWave) with abundant bandwidth resources can play a great advantage over the rate and delay in vehicular communications. Considering the problem of non-line-of-sight (NLOS) communication between the requesting nodes (RNs) and the service nodes (SNs) in the mmWave vehicular system in this paper, we propose an IRS-assisted multi-hop vehicle-to-vehicle (V2V) cooperative communication method to realize low-delay vehicular communication. Aiming to minimize the communication delay of RNs, an optimization problem is formulated by optimizing the link selection and reflection coefficient matrix of IRS. To tackle the optimization problem, an alternate optimization algorithm is proposed to decompose the original optimization problem into two subproblems for iterative optimization. First, we establish a link selection mechanism based on link quality and vehicle distance and propose a link selection algorithm based on the evaluation function to select communication links for each RN. Then, in particular, we derive the closed-form expression based on successive convex approximation (SCA) techniques for updating the reflection coefficient matrix of IRS. The simulation results show that the IRS-assisted mmWave vehicular cooperative communication scheme proposed in this paper can effectively reduce the communication delay and improve the performance of the mmWave vehicular network.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100940"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195605","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":"Enhancing security and trust using efficient privacy-preserving authentication in vehicular edge computing networks","authors":"Anwar Ghani ,&nbsp;Saeed Ullah Jan ,&nbsp;Shehzad Ashraf Chaudhry ,&nbsp;Rashid Ahmad ,&nbsp;Ashok Kumar Das ,&nbsp;Do Hyeun Kim","doi":"10.1016/j.vehcom.2025.100921","DOIUrl":"10.1016/j.vehcom.2025.100921","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) requires advanced safety, security, and services in Intelligent Transportation Systems (ITS). Frequent broadcast message intervals and Dedicated Short-Range Communication (DSRC) make validating messages in such short timeframes extremely challenging. Additionally, current authentication protocols cannot meet the demands of the environment due to high communication and computational overheads. Furthermore, lightweight authentication proposals have been put forth to solve the problem; however, such attempts cannot provide a sophisticated solution that can handle advanced security needs and suffer from accuracy issues. This article presents a two-factor authentication protocol using lightweight cryptographic operations such as one-way hashing and exclusive OR (XOR) with acceptable efficiency and security. The identities are generated using Physically Unclonable Functions (PUFs), passwords, and randomized nonces to ensure two-factor authentication. The proposal is rigorously verified through formal security analysis, confirming that the protocol is untraceable, anonymous, protects key secrecy, and is resistant to well-known attacks in the IoV domain. The performance analysis confirms its robustness and reliability, with 43.31% low energy consumption in terms of computation, communication, and support for more security requirements, making it a feasible solution for the IoV domain.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100921"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855879","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":"An adaptive bendable virtual tunnel routing protocol for flying ad-hoc networks","authors":"Huizhi Tang ,&nbsp;Demin Li ,&nbsp;Yihong Zhang ,&nbsp;Xuemin Chen ,&nbsp;Abdul Rauf","doi":"10.1016/j.vehcom.2025.100922","DOIUrl":"10.1016/j.vehcom.2025.100922","url":null,"abstract":"<div><div>Flying ad-hoc networks (FANETs) play a crucial role in disaster response, surveillance, and remote sensing. However, their highly dynamic topology and frequent link disruptions pose significant challenges to efficient routing. Existing protocols suffer from excessive control overhead, unstable links, and inefficient energy utilization, that limits their practical deployment. To address these issues, we propose an adaptive bendable virtual tunnel routing protocol (ABVTR), which constructs a three-dimensional (3D) adaptive bendable virtual relay tunnel (3D-ABVRT) using Bézier curves. This tunnel restricts the propagation of route request messages, reducing redundant transmissions, and enhancing routing stability, particularly in sparsely connected UAV networks. ABVTR employs a piecewise function to align UAV movement with the tunnel's centerline, effectively minimizing deviations and reducing delays. Furthermore, it dynamically determines the next-hop node by considering movement direction, relative speed, and residual energy. The latter two factors are assessed using a S-function and an exponential function, respectively, to enhance link reliability and optimize energy distribution. This work advances the state-of-the-art by introducing a more adaptive, energy-efficient, and scalable routing solution for FANETs. The simulation results show that ABVTR significantly outperforms existing protocols (EARVRT, iPipe, HMGOC, CF-GPSR, FM-DT-GDR) in end-to-end delay, packet delivery ratio (PDR), and routing overhead. These enhancements position ABVTR as a highly promising solution for mission-critical FANETs application, enabling more resilient, scalable, and efficient aerial networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100922"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859967","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 privacy-preserving access control protocol for 6G supported intelligent UAV networks","authors":"Khalid Mahmood ,&nbsp;Salman Shamshad ,&nbsp;Mohammad Hossein Anisi ,&nbsp;Alessandro Brighente ,&nbsp;Muhammad Asad Saleem ,&nbsp;Ashok Kumar Das","doi":"10.1016/j.vehcom.2025.100937","DOIUrl":"10.1016/j.vehcom.2025.100937","url":null,"abstract":"<div><div>Due to their autonomous operation, high mobility, and real-time communication capabilities, 6G-supported Unmanned Aerial Vehicles (6G-UAVs) (i.e., drones) are increasingly being utilized to enhance data collection and management in Intelligent Transportation Systems (ITSs). Despite their manifold benefits, 6G-supported UAV-based ITS (6G-U-ITS) faces unique security challenges beyond conventional cyber and physical threats. These include real-time authentication, impersonation attacks, physical tampering or cloning and protection against identity spoofing in highly dynamic environments. For instance, an attacker may steal a drone and use its identity to send authenticated malicious messages to the ITS, causing road accidents. Therefore, a secure authentication scheme must ensure resilience against UAV identity theft and unauthorized access while maintaining low-latency and computational efficiency to support the stringent real-time security requirements of 6G-U-ITS. Existing authentication schemes are not specifically designed to address these challenges, making it imperative to develop a lightweight and robust authentication mechanism tailored for 6G-U-ITS. Moreover, most of the existing protocols are vulnerable to physical tampering and impersonation attacks and also require high computation overhead. In this paper, to mitigate these limitations and satisfy the aforementioned requirements, we propose a secure access control protocol for 6G-U-ITS. To the best of our knowledge, this is the first security solution in the literature that can achieve security against UAVs physical attacks. Furthermore, we justify the robustness of the designed protocol against potential attacks through detailed formal and informal security assessment. Via testbed experiments, we show that our protocol achieves 20.66% and 22.82% higher efficiency on communication and computation overhead, respectively, compared to other contemporary competing protocols.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100937"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106564","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":"Deep learning-based receiver structure with superimposed channel estimation for autonomous underwater vehicles","authors":"Zeyad A.H. Qasem ,&nbsp;Xingbin Tu ,&nbsp;Fengzhong Qu ,&nbsp;Chunyi Song ,&nbsp;Hamada Esmaiel","doi":"10.1016/j.vehcom.2025.100926","DOIUrl":"10.1016/j.vehcom.2025.100926","url":null,"abstract":"<div><div>Although autonomous underwater vehicles can achieve complex tasks in harsh and inaccessible marine environments, they face several challenges related to the harsh channel effects and limited available bandwidth, making reliable channel estimation crucial task for achieving robust communication. Therefore, to track channel effects, a significant portion of bandwidth is usually reserved for overhead, which dramatically reduces the already limited bandwidth efficiency. In this paper, we present a real signal orthogonal frequency division multiplexing (OFDM) method based on deep learning to accurately track channel effects without losing bandwidth efficiency. The proposed scheme adopts the discrete Hartley transform to produce a real signal modulation, and a unitary neural network (UNN) at the sending end to add extra packages. At the receiving end, we use a deep neural network (DNN) in conjunction with channel estimation and equalization to accurately detect the transmitted information data. Therefore, we jointly train both UNN and DNN to prevent interference between the data and pilot, as well as to address factors that impact data detection performance effectively. We also deploy the carrier frequency offset estimation technique without sacrificing any subcarriers. Consequently, we track channel effects without the need for dedicated pilot subcarriers and/or data detection degradation. The proposed method has a better bit error rate, spectral efficiency, and computational complexity than current benchmarks, as shown by both the simulation and the real experiments done in the sea over a distance of 300 m</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100926"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894803","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 Multi-Head Attention mechanism assisted MADDPG algorithm for real-time data collection in Internet of Drones","authors":"A.K.M. Atiqur Rahman ,&nbsp;Muntasir Chowdhury Mridul ,&nbsp;Palash Roy ,&nbsp;Md. Abdur Razzaque ,&nbsp;Md. Rajin Saleh ,&nbsp;Mohammad Mehedi Hassan ,&nbsp;Md Zia Uddin","doi":"10.1016/j.vehcom.2025.100944","DOIUrl":"10.1016/j.vehcom.2025.100944","url":null,"abstract":"<div><div>Flexible movement and rapid deployment capabilities of unmanned aerial vehicles (UAVs) or drones have enabled them to be ideal for fresh and real-time data collection in the Internet of Drones (IoD) network. With the rising demand for IoD applications, optimizing the Age of Information (AoI), and energy efficiency of drones has become a challenging problem. The existing literature works are either limited by considering single-drone data collection from 2D space or by not prioritizing data from diverse IoT devices. In this paper, we have developed an optimization framework for multi-drone-assisted data collection in 3D space, which brings a trade-off between minimizing drone energy consumption and AoI, exploiting the Mixed Integer Linear Programming (MILP) problem. However, due to the NP-hardness of the developed optimization framework for large networks, we have devised a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, supported and enhanced by a Multi-Head Attention (MHA) mechanism for multi-drone-assisted data collection to minimize drone energy consumption and AoI jointly, namely MECAO. The MHA in the MECAO system helps prioritize IoT data sources and ensures the timely collection of important data. This system enables the agents to coordinate effectively among themselves and provides innovative solutions to complex network issues. Our findings demonstrate substantial advancements in real-time data collection and drone performance, offering a practical and efficient solution for modern IoD applications. The developed MECAO system is implemented in the OpenAI Gym simulator platform, and the simulation trace file content depicts the improvement in AoI by up to 56% while the energy consumption is reduced by as high as 38.5%, respectively, compared to the state-of-the-art works.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100944"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205001","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":"EGBCR-FANET: Enhanced genghis Khan shark optimizer based Bayesian-driven clustered routing model for FANETs","authors":"Reham R. Mostafa ,&nbsp;Dilna Vijayan ,&nbsp;Ahmed M. Khedr","doi":"10.1016/j.vehcom.2025.100935","DOIUrl":"10.1016/j.vehcom.2025.100935","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) technology has advanced rapidly, with broad use in both the military and commercial sectors. As a result, multi-UAV networks, also known as Flying Ad Hoc Networks (FANETs), have become a vital part of current communication systems. However, FANETs confront numerous challenges such as limited energy resources, high mobility, frequent topological changes, and inconsistent communication links. These difficulties influence network stability, limit data transmission efficiency, and shorten network longevity. Addressing these issues requires an adaptable routing strategy in FANETs. Cluster-based routing in UAVs is a great way to save energy, increase scalability, and improve network performance. This paper introduces a new clustering and routing framework for FANETs based on the Enhanced Genghis Khan Shark Optimizer (EGKSO). Unlike previous clustering approaches, the suggested solution dynamically selects the appropriate number of clusters while taking node coverage and network bandwidth into account. EGKSO is used to choose energy-efficient and stable cluster heads, resulting in balanced load distribution and a longer network lifetime. A dynamic cluster maintenance technique is proposed to ensure network stability and maintain efficient communication performance. In addition, a Bayesian-inspired next-hop selection model for adaptive routing is presented, allowing probabilistic decision-making to respond to network changes efficiently. This combination of swarm intelligence and probabilistic modeling improves communication reliability, reduces latency, and maximizes energy efficiency. The simulation results show that the suggested method outperforms existing clustering and routing protocols in terms of delivery ratio, energy consumption, latency, and clustering stability. The results demonstrate the efficacy of combining metaheuristic-based clustering with Bayesian-inspired routing, providing a resilient and scalable solution for FANETs in dynamic and resource-constrained contexts.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100935"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947097","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 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":"2025-06-01","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":"An efficient resource orchestration algorithm for enhancing throughput in fog computing-enabled vehicular networks","authors":"Md Asif Thanedar ,&nbsp;Sanjaya Kumar Panda","doi":"10.1016/j.vehcom.2025.100911","DOIUrl":"10.1016/j.vehcom.2025.100911","url":null,"abstract":"<div><div>The delay-sensitive applications, such as self-driving, smart transportation, navigation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The intelligent vehicles are connected to the roadside infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), for obtaining on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network throughput. Therefore, we propose an efficient resource orchestration (ERO) algorithm to maximize the throughput by reducing the allocated resource blocks (RBs) of FNs. The ERO algorithm partitions the FN coverage region into restricted and non-restricted coverage regions. Then, it coordinates the RBs allocation among FNs by reducing RBs for the vehicles in the non-restricted coverage regions. This reduction is carried out by migrating RBs for offloading upstream services so that the overall occupied capacity of FNs is minimized. ERO constructs the minimum priority queue using the occupied capacity of FNs to perform optimal RBs migration between pairs of FNs. The ERO algorithm is evaluated, and simulation results show that the proposed algorithm performs better in terms of throughput, serviceability, availability, and service capability than existing algorithms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100911"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621242","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 strategy for vehicular communication networks based on multi-agent deep reinforcement learning","authors":"Zhibin Liu,&nbsp;Yifei Deng","doi":"10.1016/j.vehcom.2025.100895","DOIUrl":"10.1016/j.vehcom.2025.100895","url":null,"abstract":"<div><div>In complex and high-mobility vehicular communication networks, rapidly changing channel conditions, signal interference, and stringent latency requirements of safety services pose significant challenges to existing wireless resource allocation schemes. We propose a novel resource allocation method named AMADRL. It is based on the multi-agent deep reinforcement learning (MADRL) algorithm and incorporates attention mechanisms (AM). This method first improves the traditional MADRL framework by employing two critic networks to estimate the corresponding global and local reward functions, achieving joint optimization of spectrum and power allocation. This optimization balances the individual interests of agents with the collective benefits, meeting the low-latency communication requirements of vehicle-to-vehicle (V2V) links. And this method effectively reduces the interference to the vehicle-to-infrastructure (V2I) links. Building on this foundation, we further integrate AM into the framework. The AM enables the model to selectively focus on critical information, dynamically adjusting resource allocation strategies. Simulation results demonstrate that, compared with random methods and conventional deep reinforcement learning (DRL) methods, the proposed algorithm exhibits superior convergence speed and stability. It effectively meets the communication requirements of different links and significantly improves spectrum efficiency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100895"},"PeriodicalIF":5.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349672","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}