Vehicular Communications最新文献_第3页

A secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET 一种安全高效的基于格的VANET条件隐私保护认证协议

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-22 DOI: 10.1016/j.vehcom.2025.100958

Dongxian Shi , Xuwen Nie , Ming Xu , Hongbing Cheng , Muhammad Alam

{"title":"A secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET","authors":"Dongxian Shi , Xuwen Nie , Ming Xu , Hongbing Cheng , Muhammad Alam","doi":"10.1016/j.vehcom.2025.100958","DOIUrl":"10.1016/j.vehcom.2025.100958","url":null,"abstract":"<div><div>On the benefit of the prosperous development of the communication techniques and automatic driving, the vehicle ad hoc network (VANET) is becoming more and more commonplace. The information transmitted in the VANETs is exposed in the open wireless communication environment, so it is vulnerable to several types of attacks. To balance the information security and privacy in the VANETs, there are a great number of conditional privacy-preserving authentication protocols proposed. However, only a few of them are resistant to quantum attacks, and these existing quantum-resistant works are unsatisfactory, ether are insecure or suffer from other problems. In this paper, we propose a secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANETs, which can achieve authentication and privacy protection, and a batch verification method is provided to further optimize the performance. Compared with the existing counterparts, our protocol is secure, efficient, and achieving lowest communication overhead. We provide several parameter sets, and the protocol achieves least execution time under some of them. We also show a security proof of the protocol in the random oracle model, based on the assume of inhomogeneous small integer solution problem.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100958"},"PeriodicalIF":5.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703540","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}

引用次数: 0

MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security MetaCAN：一种基于优化自适应混合元启发式的CAN总线安全入侵检测系统

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-10 DOI: 10.1016/j.vehcom.2025.100956

Kadir Ileri , Abdur Rakib , Soufiene Djahel

{"title":"MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security","authors":"Kadir Ileri , Abdur Rakib , Soufiene Djahel","doi":"10.1016/j.vehcom.2025.100956","DOIUrl":"10.1016/j.vehcom.2025.100956","url":null,"abstract":"<div><div>The Controller Area Network (CAN) bus is a message-based protocol widely used in modern vehicles to facilitate communication between various Electronic Control Units (ECUs). However, its simplistic design lacks fundamental security measures, making it highly susceptible to cyberattacks. These vulnerabilities pose significant risks to vehicle safety, highlighting the critical need for implementation of effective intrusion detection systems (IDS). Therefore, in this paper, a machine learning based IDS optimized through an adaptive hybrid metaheuristic approach, named MetaCAN, is proposed to secure the CAN bus. MetaCAN leverages the complementary strengths of particle swarm optimization (PSO) for fast convergence and cuckoo search (CS) for robust global search to ensure effective hyperparameter tuning and model optimization. MetaCAN is evaluated using three real-world datasets including Survival Analysis, Car Hacking: Attack & Defense Challenge 2020, and OTIDS. Unlike traditional binary detection systems, MetaCAN offers multi-class attack detection by identifying five distinct attack types including Denial of Service (DoS), fuzzy, masquerade, malfunction, and replay attacks. Moreover, the detection accuracy of the system is enhanced through a feature engineering process that introduces two effective features such as Time Interval and ID Repetition Count. The experimental results show that MetaCAN consistently outperforms existing IDS solutions targeted the same datasets, making it a promising solution for securing the CAN bus in real-world vehicular environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100956"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614566","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}

引用次数: 0

Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI 统计CSI下无人机机载IRS辅助ISAC系统的能效优化

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-05 DOI: 10.1016/j.vehcom.2025.100953

Peng Wang , Huizhi Tang , Demin Li , Yihong Zhang , Xuemin Chen

{"title":"Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI","authors":"Peng Wang , Huizhi Tang , Demin Li , Yihong Zhang , Xuemin Chen","doi":"10.1016/j.vehcom.2025.100953","DOIUrl":"10.1016/j.vehcom.2025.100953","url":null,"abstract":"<div><div>Integrated Sensing and Communication (ISAC) systems are advantageous for enhancing both communication and sensing capabilities, but their performance is significantly impacted by signal blockages in dynamic vehicular environments. An Unmanned Aerial Vehicle (UAV)-mounted Intelligent Reflective Surface (IRS) for air-to-ground communication and sensing can significantly enhance coverage and deployment flexibility. However, the additional power consumption of the UAV-mounted IRS (UIRS) remains a challenge. To mitigate this, we propose a novel UIRS-assisted ISAC system that aims to maximize communication energy efficiency (EE) while meeting sensing quality-of-service (QoS) requirements by optimizing the UAV trajectory, IRS passive beamforming, and base station (BS) active beamforming. Due to the complex and dynamic nature of wireless channels, acquiring Channel State Information (CSI) is challenging, especially with the UAV's mobility and the passive mode of IRS. Therefore, statistical CSI is adopted in the proposed scheme. The optimization problem is reformulated into a tractable form and solved by decomposing it into three subproblems, which include using the Dinkelbach transformation for fractional programming in EE calculation, Successive Convex Approximation (SCA) for UAV trajectory optimization, and Semi-Definite Relaxation (SDR) for both active and passive beamforming designs. An alternating optimization (AO)-based framework iteratively solves all subproblems, with proven algorithm convergence and computational efficiency. Simulation results demonstrate that the proposed UIRS-assisted ISAC system significantly improves both communication and sensing performance compared to benchmark schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100953"},"PeriodicalIF":5.8,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570530","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}

引用次数: 0

Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles 互联自动驾驶汽车领域中基于能量和实验信任的任务卸载

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-04 DOI: 10.1016/j.vehcom.2025.100954

Sachin Kumar Gupta , Anuradha Banerjee

{"title":"Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles","authors":"Sachin Kumar Gupta , Anuradha Banerjee","doi":"10.1016/j.vehcom.2025.100954","DOIUrl":"10.1016/j.vehcom.2025.100954","url":null,"abstract":"<div><div>Task offloading among connected and autonomous vehicles (CAVs) has recently gained much attention. The current literature in this context mostly optimizes only the criterion of energy and latency. Further, issues like connectivity and spontaneous attitude of selflessness have remained unexplored despite their importance and probable contribution to preserving vehicles' energy and reducing overall delay in completing the tasks. Therefore, the key objectives of the present study are maximization of residual energy and percentage of successful offloading, as well as minimization of energy consumption and delay. We have also considered trust, which has two components; efficiency and certainty. Efficiency is defined as the inverse of the estimated time duration required to complete the execution of the current task based on the history of the previous sessions. Certainty is related to the stability of the connection between the server and task off-loader vehicles and the selfless cooperation of the server, as revealed from the history of communication with the off-loader. Experimental results show that our proposed method of offloading tasks based on energy and experiential trust (OTEET) increases the offload success percentage and reduces cost by approximately 40%, which can be considered a significant improvement.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100954"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563852","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}

引用次数: 0

Optimizing end-to-end latency in C-V2X networks: A novel FD-RAN and MEC integration approach 优化C-V2X网络的端到端延迟：一种新的FD-RAN和MEC集成方法

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-03 DOI: 10.1016/j.vehcom.2025.100955

Saber F. Mohammed , Pan Zhiwen , Haithm M. Al-Gunid , Zeyad A.H. Qasem

{"title":"Optimizing end-to-end latency in C-V2X networks: A novel FD-RAN and MEC integration approach","authors":"Saber F. Mohammed , Pan Zhiwen , Haithm M. Al-Gunid , Zeyad A.H. Qasem","doi":"10.1016/j.vehcom.2025.100955","DOIUrl":"10.1016/j.vehcom.2025.100955","url":null,"abstract":"<div><div>The increasing demand for low-latency services in cellular vehicle-to-everything (C-V2X) communications is crucial for the efficient operation of connected vehicles and autonomous driving systems. As C-V2X networks become integral to modern transportation infrastructure, minimizing end-to-end (E2E) latency remains a significant challenge in ensuring system reliability and effectiveness. To this end, we propose a novel approach that integrates a fully decoupled radio access network (FD-RAN) with multi-access edge computing (MEC) in C-V2X networks, aiming to optimize latency-sensitive applications. We apply a tractable analytical model for the E2E latency that accounts for latency contributions across the radio, backhaul, network, and processing layers. By leveraging FD-RAN's decoupled access and MEC's distributed processing, our approach effectively mitigates latency bottlenecks inherent in conventional RAN architectures. Additionally, we propose a stochastic optimization-based resource allocation method using Lyapunov techniques and Markov decision processes to dynamically manage base station selection and bandwidth allocation, thereby enhancing the system performance. Simulation results demonstrate that FD-RAN with MEC significantly reduces E2E latency compared with conventional RAN architectures, even under high traffic densities, while maintaining high data rates. These findings validate the proposed approach and offer key insights for developing low-latency infrastructures for next-generation V2X applications.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100955"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570531","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}

引用次数: 0

Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach 基于sagin的应急VEC动态卸载策略：一种多无人机聚类与协同计算方法

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-03 DOI: 10.1016/j.vehcom.2025.100952

Zhenzheng Shi, Liang Wang, Yaguang Lin, Anna Cai, Jiamin Fan, Cong Liu

{"title":"Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach","authors":"Zhenzheng Shi, Liang Wang, Yaguang Lin, Anna Cai, Jiamin Fan, Cong Liu","doi":"10.1016/j.vehcom.2025.100952","DOIUrl":"10.1016/j.vehcom.2025.100952","url":null,"abstract":"<div><div>Mobile edge computing (MEC) technology can provide stable and efficient computing services for ground vehicles and users. However, maintaining stable MEC services becomes challenging in scenarios where ground MEC servers are damaged or unavailable, such as in post-disaster or remote areas. To tackle this issue, this paper proposes a novel space-air-ground integrated network (SAGIN) based emergency vehicular edge computing (VEC) framework, leveraging the rapid deployment characteristic of unmanned aerial vehicle (UAV) to provide VEC services for ground vehicles. A distance-based UAV clustering (DUC) algorithm is designed for efficient multi-UAV collaboration, executed by low earth orbit (LEO) satellite with wide coverage. Within each cluster, a task splitting algorithm based on a novel expected computing delay (ECD) metric is performed by the cluster-head UAV (CHU). Focusing on the issue of limited line-of-sight (LoS) range of UAV and computing sustainability during vehicle moving, we propose a dynamic offloading strategy. Simulation results show that the proposed framework enhances UAV utilization by 60% and significantly reduces task process delays across varying scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100952"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563851","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}

引用次数: 0

Multi-objective resource allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks 无人机辅助地空一体化全双工OFDMA网络的多目标资源分配

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-02 DOI: 10.1016/j.vehcom.2025.100951

Tong Wang

{"title":"Multi-objective resource allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks","authors":"Tong Wang","doi":"10.1016/j.vehcom.2025.100951","DOIUrl":"10.1016/j.vehcom.2025.100951","url":null,"abstract":"<div><div>In multi-UAV-assisted air-ground integrated in-band full-duplex (IBFD) OFDMA networks, both uplink and downlink performances are critical and must be simultaneously considered. This study addresses effective resource allocation in such networks to maximize the total system uplink and downlink rates by jointly optimizing subcarrier assignment and power control. Given the significant trade-off between uplink and downlink transmissions owing to self-interference in IBFD systems and intercell interference, we formulate the resource allocation problem as a multi-objective optimization problem (MOOP), aiming to jointly maximize the uplink and downlink performances. To achieve Pareto optimal solutions, we employ the weighted Tchebycheff technique to transform the MOOP into a single-objective optimization problem (SOOP) and solve it using Successive Convex Approximation (SCA) within a Block Coordinate Descent (BCD) framework. This approach iteratively optimizes the subcarrier assignment and power control and effectively manages the trade-offs between uplink and downlink rates. The proposed method demonstrates the ability to achieve an efficient balance in resource allocation. Simulation results show that our method can obtain Pareto optimal solutions, demonstrating favorable performance trade-offs and fairness under various interference conditions, thereby improving the overall system performance in multi-UAV-assisted air-ground integrated OFDMA networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100951"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534422","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}

引用次数: 0

Deep reinforcement learning based migration and execution decisions for multi-hop task offloading in mobile vehicle edge computing 基于深度强化学习的移动车辆多跳任务卸载迁移与执行决策

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-07-02 DOI: 10.1016/j.vehcom.2025.100950

Wenjie Zhou, Tian Zhang, Zekun Lu, Linbo Zhai

{"title":"Deep reinforcement learning based migration and execution decisions for multi-hop task offloading in mobile vehicle edge computing","authors":"Wenjie Zhou, Tian Zhang, Zekun Lu, Linbo Zhai","doi":"10.1016/j.vehcom.2025.100950","DOIUrl":"10.1016/j.vehcom.2025.100950","url":null,"abstract":"<div><div>As the Internet of Things (IoT) drives the development of Vehicular Edge Computing (VEC), there is a surge in computational demand from emerging in-vehicle applications. Most existing studies do not fully consider the frequent changes in network topology under high mobility of vehicles and the underutilization of idle resources by single-hop offloading. To this end, we propose a task offloading scheme for vehicular edge computing based on multi-hop offloading. The scheme allows task vehicles to offload tasks to service vehicles with excess idle resources outside the communication range, and adapts to dynamic changes in network topology by introducing the concept of neighboring vehicle connection time. This study aims to minimize the delayed energy consumption utility value of the task under the conditions of satisfying the maximum task delay limit, vehicle computational and storage resource constraints. In response to this NP-hard problem, a two-stage reinforcement learning strategy MOCDD (combining Deep Q Network (DQN) and Deep Deterministic Policy Gradient (DDPG)) is proposed to divide the mixed action space into pure discrete and pure continuous action space to determine task migration, executive decision and vehicle transmission power. Simulation results verify the effectiveness of the proposed scheme.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100950"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563853","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}

引用次数: 0

MIEC: A magnetism-inspired framework for MS deployment and joint task offloading and resource allocation optimization in LMREC MIEC：一个磁力启发的框架，用于mrec中的MS部署和联合任务卸载和资源分配优化

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-06-25 DOI: 10.1016/j.vehcom.2025.100948

Mingyu Zhang , Zhibo Sun , Fengjie Li , Hong Zhang

{"title":"MIEC: A magnetism-inspired framework for MS deployment and joint task offloading and resource allocation optimization in LMREC","authors":"Mingyu Zhang , Zhibo Sun , Fengjie Li , 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}

引用次数: 0

A comprehensive survey of deep reinforcement learning in UAV-assisted IoT data collection 深度强化学习在无人机辅助物联网数据采集中的综合研究

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2025-06-25 DOI: 10.1016/j.vehcom.2025.100949

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

{"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":"10.1016/j.vehcom.2025.100949","url":null,"abstract":"<div><div>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 research on UAV-assisted IoT utilizing DRL, covering key research questions relating to 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. Other 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 as aerial communication platforms, especially in IoT data collection. By organizing existing work across key themes and outlining promising future directions, this review offers a valuable reference for researchers and technology professionals alike.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100949"},"PeriodicalIF":5.8,"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}

引用次数: 0