Vehicular Communications最新文献

{"title":"5G NR sidelink time domain based resource allocation in C-V2X","authors":"Mehnaz Tabassum,&nbsp;Aurenice Oliveira","doi":"10.1016/j.vehcom.2025.100902","DOIUrl":"10.1016/j.vehcom.2025.100902","url":null,"abstract":"<div><div>This study explores the need for efficient resource allocation in fifth generation (5G) New Radio (NR) sidelink communication for cellular vehicle-to-everything (C-V2X) applications. With the advent of 5G networks, C-V2X can enable direct connection between neighboring vehicles and infrastructure without relying on the cellular network. However, direct communication between devices in 5G NR sidelink makes resource allocation more challenging than in a cellular network. Efficient resource allocation is essential to maintain dependable communication, especially in crowded and interference-prone contexts. There are different type of resource allocation methods such as time-domain, frequency-domain, and power-domain resource allocation, which can be used separately or in combination to achieve efficient resource allocation. In this study, the authors discuss time domain based resource allocation method based on packet generation time and packet allocation time. The implications of efficient resource allocation in 5G NR sidelink in C-V2X include increased signal-to-noise ratio, reduced interference, lower latency, and increased network capacity. The proposed approach is demonstrated on a Network Simulator (NS3.34) along with the traffic scenarios generated using Simulated Urban Mobility (SUMO). Our results demonstrate that time allocation is a promising approach to achieve efficient resource allocation, enabling safer and more effective transportation systems for C-V2X applications.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100902"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509061","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 question-centric review on DRL-based optimization for UAV-assisted MEC sensor and IoT applications, challenges, and future directions","authors":"Oluwatosin Ahmed Amodu ,&nbsp;Raja Azlina Raja Mahmood ,&nbsp;Huda Althumali ,&nbsp;Chedia Jarray ,&nbsp;Mohd Hirzi Adnan ,&nbsp;Umar Ali Bukar ,&nbsp;Nor Fadzilah Abdullah ,&nbsp;Rosdiadee Nordin ,&nbsp;Zuriati Ahmad Zukarnain","doi":"10.1016/j.vehcom.2025.100899","DOIUrl":"10.1016/j.vehcom.2025.100899","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) applications vary widely including data monitoring, data collection and analysis, intelligent navigation and object tracking, surveillance and emergency response, vehicular and intelligent transport, and agricultural, marine, and photogrammetry. Mobile Edge Computing (MEC)-based UAV-assisted IoT networks enable resource-constrained mobile or IoT devices to offload computationally demanding tasks to UAVs or edge nodes with more computing power in order to improve battery consumption, performance, or Quality of Service. UAV-assisted IoT applications generally require a lot of precision for efficient UAV control involving UAV movement and position optimization and Deep Reinforcement Learning (DRL) has recently been identified as one of the most prominent techniques for facilitating this and optimizing of the terrestrial network performance, thus improving the operation of these applications. This paper aims to answer twelve important research questions relating to the research on DRL for Mobile Edge Computing (MEC)-based UAV-assisted sensor and IoT applications from 47 systematically selected articles. The questions cover a variety of topics including the UAV-assisted MEC IoT applications studied, variants of deployed DRL, the purpose of DRL, Markov Decision Processes (MDPs) components, unique network architectural features, environments and integrated technologies, role of UAVs, optimization constraints, joint optimization frameworks, energy-management techniques, metrics examined, benchmark algorithms and performance results as well as identified probable future considerations based on the review. Lastly, the challenges and future directions of DRL application in UAV-assisted MEC systems are discussed. This paper aims to provide both communication generalists and optimization specialists with a comprehensive understanding of the research landscape in this field.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100899"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563347","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":"Task offloading and multi-cache placement based on DRL in UAV-assisted MEC networks","authors":"Kai Xue,&nbsp;Linbo Zhai,&nbsp;Yumei Li,&nbsp;Zekun Lu,&nbsp;Wenjie Zhou","doi":"10.1016/j.vehcom.2025.100900","DOIUrl":"10.1016/j.vehcom.2025.100900","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are being developed as a promising technology to assist mobile edge computing (MEC) systems due to their reliable wireless communication, flexible computing service capabilities, and flexible deployment. However, in the face of huge information and demanding task delay, it is a challenging problem to reduce the system cost. This paper studies task offloading and cache space placement for ground users, and proposes a multi-UAV assisted computing framework, which is a four-layer transmission system composed of ground users (UE), UAVs, edge data centers (EDC) and remote clouds. By jointly optimizing UAV cache space, flight path, offloading decision, channel ratio, and battery power, we formulate the problem to minimize the long-term average weighted cost of the system under the constraint of cache space and computing resources. Since this problem is a mixed integer variable problem, we design a task offloading and cache placement algorithm based on deep reinforcement learning, namely the Cooperative Long-term Average Cost Minimization Optimization Algorithm (CLACMO). Firstly, we transform the mixed action variable space by using embedded tables and conditional variational autoencoders (VAE) combined with latent space, and map the mixed action variable to the latent action space. This approach effectively unifies discrete and continuous actions, addressing the challenge of mixed action spaces that traditional deep reinforcement learning algorithms struggle with. Secondly, based on the deep reinforcement learning (DRL), we achieve the long-term system average weighted cost minimization more efficiently under the constraints of task offloading and cache placement. The results show that compared with the PER-UOS-RL, MASAC, and MADDPG algorithms, the average reward has increased by 54.5%, 66.7%, and 69.7% respectively, and the average task completion rate has increased by 12.9%, 38.1%, and 9.11% respectively, demonstrating the effectiveness of our novel method.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100900"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509231","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 task offloading in vehicular networks: A multi-agent cloud-edge-device framework","authors":"Peiying Zhang ,&nbsp;Enqi Wang ,&nbsp;Lizhuang Tan ,&nbsp;Neeraj Kumar ,&nbsp;Jian Wang ,&nbsp;Kai Liu","doi":"10.1016/j.vehcom.2025.100898","DOIUrl":"10.1016/j.vehcom.2025.100898","url":null,"abstract":"<div><div>In vehicular networks, the increasing demand for computational resources often exceeds the capabilities of in-vehicle devices. To address these challenges, we propose a cloud-edge-device collaborative framework integrated with a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm for dynamic optimization of task offloading and resource allocation. Experimental evaluations demonstrate the proposed algorithm's superiority over traditional methods, achieving an 11% reduction in energy consumption and a 23% increase in task completion rate compared to local processing-only strategies, while reducing average task delay by 50% relative to static offloading approaches. The MADRL-based framework not only ensures efficient task distribution but also adapts to fluctuating network conditions, achieving a resource utilization rate of 85%. These findings underscore its potential to enhance performance in intelligent transportation systems by balancing computational efficiency, energy consumption, and task latency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100898"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512480","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":"Optimizing task offloading in MIMO-enabled vehicular networks through deep reinforcement learning","authors":"Jian Xu,&nbsp;Shengchao Su","doi":"10.1016/j.vehcom.2025.100901","DOIUrl":"10.1016/j.vehcom.2025.100901","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) effectively alleviates the computational burden faced by vehicles in processing compute-intensive tasks due to resource limitations. However, traditional approaches typically employ coarse-grained task offloading strategies that utilize sequential protocols and discrete action spaces, resulting in high latency and increased energy consumption. These limitations render such strategies unsuitable for real-time applications. To address these challenges, an innovative computation offloading strategy is proposed, specifically designed to minimize the long-term average computation cost in a multi-vehicle, multi-server Internet of Vehicles (IoV) system. The MEC system model is constructed using Multiple-Input Multiple-Output (MIMO) technology, which facilitates simultaneous uplink transmissions from all vehicles, significantly reducing the time required for data uploads. Subsequently, a continuous action space is adopted to enhance both the flexibility and precision of decision-making. Additionally, Batch-Constrained Q-learning (BCQ) is introduced to further constrain the actions taken by the policy, mitigating overly optimistic estimates through a batch constraint mechanism. Finally, the Twin Delayed Deep Deterministic Policy Gradient with Batch-Constrained Q-learning (TD3BCQ) framework is developed to enable fine-grained decision-making for local execution and power allocation during task offloading within a continuous action space. Experimental results demonstrate that the proposed scheme achieves a more balanced offloading strategy and better exploits the available computing resources, leading to an approximate 20% improvement compared to the baselines.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100901"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509060","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 security-enhanced certificateless aggregate signcryption scheme for vehicular ad hoc networks","authors":"Wanqing Wu ,&nbsp;Junjie Chen","doi":"10.1016/j.vehcom.2025.100897","DOIUrl":"10.1016/j.vehcom.2025.100897","url":null,"abstract":"<div><div>Vehicular Ad Hoc Networks (VANETs) are high-speed mobile wireless communication networks that play a pivotal role in shaping the future of intelligent transportation systems. In many fields, certificateless aggregate signcryption (CLASC) serves as a reliable method that reduces computational and communication overheads while enhancing security. Due to these advantages, in recent years, an increasing number of signcryption schemes have been presented in VANETs. In this paper, a security analysis of Dai et al.'s pairing-free CLASC scheme for vehicular sensor networks is conducted. The analysis results reveal its vulnerability to public key replacement attacks. To address this security flaw, a security-enhanced certificateless aggregate signcryption scheme is proposed and demonstrated its confidentiality and unforgeability against adaptive chosen ciphertext/message attack in the random oracle model. Additionally, the proposed scheme is demonstrated to satisfy the security attributes in VANETs. Eventually, the performance analysis reveal that the proposed CLASC scheme demonstrates superior computational and communication efficiency compared to the related schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100897"},"PeriodicalIF":5.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480269","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":"2025-02-06","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}

{"title":"Light security scheme for Cooperative, Connected and Automated Mobility (CCAM)","authors":"Ramzi Boutahala ,&nbsp;Hacène Fouchal ,&nbsp;Marwane Ayaida","doi":"10.1016/j.vehcom.2025.100892","DOIUrl":"10.1016/j.vehcom.2025.100892","url":null,"abstract":"<div><div>Cooperative, Connected and Automated Mobility (CCAM) is a new paradigm adopted by academia and industry in order to provide safe, secure and sustainable mobility on all kinds of roads (highways, urban and rural roads). CCAM takes advantage of Cooperative Intelligent Transport Systems (C-ITS) to improve safety and to reduce traffic congestion thanks to communication and cooperation among all relevant actors (vehicle, road infrastructure, pedestrians, etc.). In Europe and the USA, adapted communication protocols have been proposed to ensure cooperation through the exchange of specific messages. In this study, we focus on the European protocols, particularly the Cooperative Awareness Messages (CAMs) as defined by the European Telecommunications Standards Institute (ETSI). CAMs are composed of a payload containing information about the vehicle status (speed, location, heading, etc.) and additional security information. This information includes a Pseudonym Certificate (PC) and a signature to guarantee the sender's authentication. However, since CAMs are sent periodically by vehicles at frequencies from 1 to 10 Hz, this addition of security data to the payload significantly increases the load and bandwidth of the communication channel. Instead of exhaustive authentication, we propose a new approach that allows vehicles to authenticate each other for the first time when they meet. Once this level of trust is reached, vehicles stop sending authenticated messages during a variable period of time (called a trust period). In addition, we propose a trust verification process to avoid malicious activities during this trust period. Our approach significantly reduces the number of signed sent CAMs, the verification and signature computations, leading to reduce communication overhead. Simulation tests conducted over the OMNET++ platform demonstrate that our approach leads to a significant decrease in communication overhead, reducing the volume of CAMs exchanged by 56%.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100892"},"PeriodicalIF":5.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386873","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":"Assessing the impact of communication delays on advanced air mobility cooperative surveillance","authors":"Nour El-Din Safwat,&nbsp;Alessandro Gardi,&nbsp;Kathiravan Thangavel,&nbsp;Roberto Sabatini","doi":"10.1016/j.vehcom.2025.100896","DOIUrl":"10.1016/j.vehcom.2025.100896","url":null,"abstract":"<div><div>Advanced air mobility, the next evolution in air transportation, emphasizes the crucial need for a high level of automation to enable the coexistence of manned and unmanned aircraft and transform airspace from segregated to unsegregated, empowering aircraft to manage self-separation and collision avoidance autonomously. This paper introduces a separation assurance and collision avoidance that adopts a unified analytical framework, leveraging both cooperative and non-cooperative sensory data to generate an avoidance volume, considering the performances of navigation and surveillance systems. Expanding upon this system, we address performance issues in data link and vehicle-to-vehicle communication systems, considering delays attributed to human response, negotiation for deconfliction, and command and control communication for unmanned aircraft. We propose methods to enhance communication system performance, including the development of a predictive algorithm that uses piecewise linear regression to predict transmission delays based on network load and the probability of success for packet reception, enabling real-time adjustments to minimize communication delays. We also investigated how the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) automation framework can mitigate these communication delays and improve overall system performance. Additionally, we explore alternative communication technologies aimed at reducing position uncertainty arising from these delays. Finally, we present a simulation case study illustrating the impact of different communication technologies on cooperative separation and the minimum separation distance between Unmanned Aircraft Systems (UAS). The results demonstrate significant reductions in position uncertainty through these enhancements, underscoring their potential to improve safety and efficiency in air transportation.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100896"},"PeriodicalIF":5.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420048","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}

{"title":"PPORM: A PPO-assisted packet reordering mechanism of heterogeneous VANETs for enhancing goodput and stability in fog computing","authors":"Xiaoya Zhang ,&nbsp;Yuyang Zhang ,&nbsp;Ping Dong ,&nbsp;Xiaojiang Du ,&nbsp;Chengxiao Yu ,&nbsp;Hongke Zhang","doi":"10.1016/j.vehcom.2025.100894","DOIUrl":"10.1016/j.vehcom.2025.100894","url":null,"abstract":"<div><div>Integrated Vehicular Networks (VANETs) constructed through the collaboration of various heterogeneous networks, such as 4G, 5G, satellite networks, and Unmanned Aerial Vehicle (UAV) networks, provide an effective solution to the resource constraints between vehicles and edge fog computing nodes. Reordering Buffer (RB) is crucial in concurrent data transmission between vehicles and edge fog computing nodes via heterogeneous VANETs. RB is in charge of storing out-of-order packets, waiting for packets with smaller sequence numbers, and delivering in-order packets to upper-layer applications. However, current packet reordering mechanisms are challenging in providing stable and high goodput due to the inappropriate timeout timers and uneven delivery rules. In this paper, we propose a PPO-assisted packet reordering mechanism (PPORM) to achieve optimal control of packet delivery. We first transform the goodput maximization problem into the optimal timeout threshold of RB and the optimal delivery moment of each packet. Secondly, we introduce a Proximal Policy Optimization-assisted Timeout Threshold Updating (TTU) algorithm to dynamically adjust the threshold in response to real-time changes in network conditions. Further, we present a Multifactor Smooth Delivery (MSD) algorithm to regulate the optimal queuing delay for each packet and enhance the stability of the real-time throughput as much as possible. Experimental results show that PPORM improves goodput by <span><math><mn>6.94</mn><mtext>%</mtext><mo>∼</mo><mn>45.57</mn><mtext>%</mtext></math></span> and improves stability by <span><math><mn>32.5</mn><mtext>%</mtext><mo>∼</mo><mn>49.58</mn><mtext>%</mtext></math></span> compared with other baseline algorithms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100894"},"PeriodicalIF":5.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378270","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}