Vehicular Communications最新文献

{"title":"MIEC: A magnetism-inspired framework for MS deployment and joint task offloading and resource allocation optimization in LMREC","authors":"Mingyu Zhang ,&nbsp;Zhibo Sun ,&nbsp;Fengjie Li ,&nbsp;Hong Zhang","doi":"10.1016/j.vehcom.2025.100948","DOIUrl":"10.1016/j.vehcom.2025.100948","url":null,"abstract":"<div><div>With the rapid growth of Internet of Things (IoT) devices, Mobile Edge Computing (MEC) faces challenges in meeting increasing computational demands, especially in resource-constrained environments. To address this issue, we propose the LEO Satellite-MS-RSU Edge Computing (LMREC) framework, which integrates Mobile Servers (MSs), Low Earth Orbit (LEO) satellite networks, and Roadside Units (RSUs) into an innovative edge computing architecture. We first introduce “attraction” and “repulsion” metrics to model the willingness of vehicular satellite servers to serve specific users. Subsequently, we design a Magnetic Equilibrium Algorithm (MEA), which dynamically adjusts the MS deployment and service allocation by balancing user-driven attraction and server repulsion. To address the latency sensitivity of task scheduling and user satisfaction in LMREC, we formulate a mixed-integer nonlinear programming (MINLP) optimization problem for task offloading and resource allocation. Since this optimization problem is intractable to solve in polynomial time, we propose a Magnetic Domain Migration Algorithm (MDMA) to obtain a near-optimal solution. In MDMA, tasks are modeled as magnetic domains migrating in a magnetic field, and the optimization problem is decomposed into subproblems, which are solved using Exact Potential Game Theory, convex optimization, and a hybrid genetic algorithm. Finally, simulation results validate the effectiveness of the LMREC framework, demonstrating its superiority over existing methods and its potential to enhance collaboration among end devices, RSUs, and LEO satellite networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100948"},"PeriodicalIF":5.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491512","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 Comprehensive Survey of Deep Reinforcement Learning in UAV-Assisted IoT Data Collection","authors":"Oluwatosin Ahmed Amodu, Huda Althumali, Zurina Mohd Hanapi, Chedia Jarray, Raja Azlina Raja Mahmood, Mohammed Sani Adam, Umar Ali Bukar, Nor Fadzilah Abdullah, Nguyen Cong Luong","doi":"10.1016/j.vehcom.2025.100949","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100949","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) play a critical role in data collection for a wide range of Internet of Things (IoT) applications across remote, urban, and marine environments. In large-scale deployments, UAVs often face complex decision-making challenges, for which Deep Reinforcement Learning (DRL) has emerged as a promising solution. This paper presents a comprehensive review of UAV-assisted IoT applications utilizing DRL, covering key research questions, DRL algorithm variants, deployment objectives, architectural features, integrated technologies, UAV roles, optimization constraints, energy management strategies, and performance metrics. Findings indicate that value-based and actor-critic algorithms are the most commonly employed, targeting objectives such as path planning, transmit power control, scheduling, velocity and altitude control, and charging optimization. Architectural considerations include clustering, security, obstacle avoidance, buffered sensors, and multi-UAV coordination. Beyond data collection, UAVs are also used for tasks such as device selection, data aggregation, and sensor charging, with energy management primarily achieved through charging and energy harvesting techniques. Performance is typically assessed using metrics like energy efficiency, throughput, latency, packet loss, and Age of Information (AoI). The paper concludes by outlining several promising research directions and open challenges critical to the successful deployment of UAVs in IoT data collection.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"48 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515946","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":"HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks","authors":"Chen Sun,&nbsp;Liang Hou,&nbsp;Suqi Yu,&nbsp;Jian Shu","doi":"10.1016/j.vehcom.2025.100947","DOIUrl":"10.1016/j.vehcom.2025.100947","url":null,"abstract":"<div><div>Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100947"},"PeriodicalIF":5.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491511","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":"Securing the unforeseen: Enhancing VANET security with dynamic honeypots and attack rate analysis","authors":"Mohammed A. Abdelmaguid,&nbsp;Hossam S. Hassanein,&nbsp;Mohammad Zulkernine","doi":"10.1016/j.vehcom.2025.100946","DOIUrl":"10.1016/j.vehcom.2025.100946","url":null,"abstract":"<div><div>Addressing known threats constitutes the foundational layer of cybersecurity defenses. However, the real challenge emerges in anticipating and mitigating unforeseen attacks. Current security methodologies work well against familiar threats but often struggle with new or unforeseen attacks. This paper examines the Trust Origin within Trust Management Systems (TMS) by linking it to the network attack rate, thereby refining trust assessments and predicting new attacks. Combining Machine Learning (ML) algorithms with honeypots, we offer a comprehensive defense for Vehicular Ad-hoc Networks (VANETs), adept at detecting anticipated and unexpected attacks through attack rate analysis. Our methodology evaluates the network's security status by examining its ability to identify known attacks, referred to as prepared-for attacks. Subsequently, this information serves as a foundation to predict future attacks that still need to be identified, termed unprepared-for attacks. Through extensive testing, we demonstrate the viability of a dual strategy that encompasses the detection of prepared-for attacks and the prediction of unprepared-for ones. Experimental results reveal a significant improvement in predicting unprepared-for attacks, evidenced by enhanced accuracy, precision, and recall. Additionally, we conduct experiments to determine the optimal deployment of honeypots for maximum efficiency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100946"},"PeriodicalIF":5.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337779","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":"BFP-Net: A DL-based ISAC beamforming prediction method for extended vehicle","authors":"Peng Chen ,&nbsp;Ting Zhou ,&nbsp;Zhimin Chen ,&nbsp;Fan Meng ,&nbsp;Jun Liu","doi":"10.1016/j.vehcom.2025.100945","DOIUrl":"10.1016/j.vehcom.2025.100945","url":null,"abstract":"<div><div>To enable the next generation of connected autonomous vehicles, the millimeter wave (mmWave)-based integrated sensing and communication (ISAC) system will be a critical technology in future vehicle-to-everything (V2X) networks. However, the rapid mobility of vehicles and the narrow beamwidth of mmWave signals present significant challenges for beam alignment, and point-target modeling methods often lead to substantial overhead, high latency, and complications. To address these issues, in this paper, a hybrid analog-digital (HAD) multi-input multi-output (MIMO) ISAC framework is adopted for the mmWave-based V2X network to reduce hardware costs and power consumption. Then, considering the narrow beamwidth of the mmWave system, the vehicle is modeled as an extended surface target with multiple scattering points, and a new association technique for these points is developed to improve prediction accuracy. Hence, a deep learning (DL)-based beamforming prediction network, namely beamforming prediction network (BFP-Net), is designed according to the ISAC signal beam prediction protocol and enables roadside units (RSUs) to transmit ISAC signals effectively for both downlink communication and sensing operations. The BFP-Net leverages a convolutional neural network long-short-term memory (CNN-LSTM) architecture to capture spatial and temporal correlations, providing enhanced modeling capabilities for beam prediction. Moreover, for highly dynamic vehicles, the BFP-Net predicts optimal beams for future time slots by extracting features from the received echo signals and eliminates the repetitive beam training inherent in the traditional communication protocol. Simulation results demonstrate that the proposed method significantly outperforms extended Kalman filter (EKF)-based methods in the mmWave V2X scenario, achieving higher beam gains and better performance for high-speed vehicles, and substantially reduces the overhead associated with beam training compared to the conventional neural network relying on pilot signals.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100945"},"PeriodicalIF":5.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243172","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":"2025-05-30","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":"Cognitive UAV-assisted secure and reliable communications based on robust joint trajectory and power control optimization","authors":"Can Wang,&nbsp;Junhong Zhang,&nbsp;Helin Yang","doi":"10.1016/j.vehcom.2025.100941","DOIUrl":"10.1016/j.vehcom.2025.100941","url":null,"abstract":"<div><div>The cognitive unmanned aerial vehicle (UAV) communication system has emerged as a pivotal technology in addressing the scarcity of spectral resources for UAV communications, but the jamming and eavesdropping attacks are severe due to the high-quality air-to-ground communication links. Consequently, this paper introduces a UAV-enabled cooperative jammer to disrupt the eavesdropping activities of active eavesdroppers by emitting artificial noise. Our objective is to jointly optimize the three-dimensional UAV trajectory and transmit power to maximize the secrecy communication rate under quality of service (QoS) requirement. To tackle the non-convex problem, the block coordinate descent (BCD) and successive convex approximation (SCA) methods are utilized to transform it into an approximate convex problem, and then we design an alternative optimization iterative algorithm to achieve suboptimal but efficient solution. Moreover, we extend the developed algorithm into an imperfect channel state information (CSI) scenario to maximize the worst-case secrecy rate by jointly optimizing the robust UAV's trajectory and transmit power, where the location uncertainties of ground primary, secondary, and eavesdropping devices are considered. Simulation results demonstrate that the proposed joint optimization algorithm significantly enhances system secrecy performance under different real-world settings compared to existing state-of-the-art algorithms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100941"},"PeriodicalIF":5.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170397","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":"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":"2025-05-23","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":"CLE-based authenticated key agreement with PUF-secured key for vehicle-to-infrastructure","authors":"Suhui Liu ,&nbsp;Liquan Chen ,&nbsp;Liqun Chen ,&nbsp;Yu Wang ,&nbsp;Yaqing Zhu","doi":"10.1016/j.vehcom.2025.100942","DOIUrl":"10.1016/j.vehcom.2025.100942","url":null,"abstract":"<div><div>Vehicle-to-infrastructure (V2I) communication is the basis for vehicles to obtain information about the road ahead. The confidentiality and reliability of V2I communication guarantee traffic safety and smooth flow. Authenticated key agreement (AKA) is the most commonly used technique to establish secure communication channels. Signature-based AKA inevitably exposes the identity information of vehicles, while Encryption-based AKA can bring deniability and high privacy, which means no adversary can know who sent the AKA message. Certificateless encryption (CLE) can simultaneously solve burdensome certificate management and key escrow. However, existing certificateless cryptography requires two loosely combined public keys to represent a device and does not consider the physical security of storing secret keys locally. This paper first designed an improved CLE scheme with one-device-one-public-key, and performance comparisons show that the proposed CLE has optimal storage and computation performance. Considering that rare work was put on encryption-based AKA, this paper proposed a deniable and privacy-preserving certificateless AKA for V2I communication by incorporating Physically Unclonable Function (PUF)-secured key management to prevent physical leakage of keys, named CLE-AKA-PUF. Feature comparison illustrates that CLE-AKA-PUF supports key escrow-free, dual authentication, physical security, deniability, and high privacy. Security proofs and performance analysis demonstrate the practicability and efficiency of CLE-AKA-PUF.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100942"},"PeriodicalIF":5.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170398","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":"Perceptual data importance and freshness aware transmission in millimeter wave vehicular networks","authors":"Chenyuan He ,&nbsp;Zhouyu Zhang ,&nbsp;Yingfeng Cai ,&nbsp;Hai Wang ,&nbsp;Long Chen ,&nbsp;Fenghua Huang","doi":"10.1016/j.vehcom.2025.100939","DOIUrl":"10.1016/j.vehcom.2025.100939","url":null,"abstract":"<div><div>The extensive sharing of perceptual data between vehicles and between vehicles and roads has significantly enhanced the performance of intelligent transportation system (ITS). The current vehicular networks using sub-6 GHz struggle to meet the demands for high-rate, low-latency, and highly reliable communication. To address this issue, this paper proposes a perceptual data sharing strategy based on millimeter-wave (mmWave) communication technology. This strategy takes into account the characteristics of vehicular perceptual data, i.e., the importance and freshness of the data, and constructs a mixed-integer nonlinear sum-of-ratios optimization problem. To meet the stringent real-time decision-making requirements of vehicular networks, we leverage the transmission slot characteristics of the Time Division Multiple Access (TDMA) Medium Access Control (MAC) architecture to transform the nonlinear original problem into a series of approximate integer linear programming (ILP) problems. Then we employ maximum weight matching in graph theory to further reduce computational complexity, enabling the problem to be solved in polynomial time. Additionally, we have designed a brute-force algorithm to ensure the global optimum is achieved, thereby validating the performance of our proposed algorithm. Comparative simulation studies with the brute-force algorithm, the ILP solver, the edge coloring algorithm, our previously developed parameterization-based iterative algorithm (PIA), and the First-Come-First-Serve (FCFS) scheduling scheme verify the effectiveness of our proposed algorithm.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"54 ","pages":"Article 100939"},"PeriodicalIF":5.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084086","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}