Vehicular Communications最新文献

{"title":"Decentralized multi-hop data processing in UAV networks using MARL","authors":"Indu Chandran,&nbsp;Kizheppatt Vipin","doi":"10.1016/j.vehcom.2024.100858","DOIUrl":"10.1016/j.vehcom.2024.100858","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have become integral to numerous applications, prompting research towards enhancing their capabilities. For time-critical missions, minimizing latency is crucial; however, current studies often rely on sending data to ground station or cloud for processing due to their limited onboard capacities. To leverage the networking capabilities of UAVs, recent research focuses on enabling data processing and offloading within the UAV network for coordinated decision-making. This paper explores a multi-hop data offloading scheme designed to optimize the task processing and resource management of UAVs. The proposed distributed strategy uses multi-agent reinforcement learning, where UAVs, each with varying computational capacities and energy levels, process and offload tasks while managing energy consumption and latency. The agents, represented as actor-critic models, learn and adapt their actions based on current state and environment feedback. The study considers a consensus-based method to update learning weights, promoting cooperative behavior among the agents with minimum interaction. Through multiple training episodes, the agents improve their performance, with the overall system achieving faster convergence with high rewards, demonstrating the viability of decentralized data processing and offloading in UAV networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100858"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652866","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":"Prediction-based data collection of UAV-assisted Maritime Internet of Things","authors":"Xiaoluoteng Song ,&nbsp;Xiuwen Fu ,&nbsp;Mingyuan Ren ,&nbsp;Pasquale Pace ,&nbsp;Gianluca Aloi ,&nbsp;Giancarlo Fortino","doi":"10.1016/j.vehcom.2024.100854","DOIUrl":"10.1016/j.vehcom.2024.100854","url":null,"abstract":"<div><div>In maritime data collection scenarios, due to the constraints of wireless communication and environmental factors such as wave motion, sea surface ducting effects, and sea surface curvature, floating sensor nodes are unable to establish direct data transmission links with the base station. The advent of unmanned aerial vehicle (UAV)-assisted Maritime Internet of Things (MIoT) provides a feasible solution to this challenge. However, in existing maritime environments, floating sensor nodes drift due to ocean currents, posing significant challenges for long-distance data transmission while maintaining a low age of information (AoI). Consequently, we introduce a prediction-based UAV-assisted data collection mechanism for MIoT. In this scheme, we first select convergence nodes responsible for gathering data from floating sensor nodes and forwarding it to passing UAVs. We then propose a dynamic clustering algorithm to allocate task areas to UAVs, with each area assigned to a single UAV for data collection from floating sensor nodes. To ensure stable data offloading by UAVs, we develop a UAV relay pairing algorithm to establish reliable air-to-air relay paths and provide two data offloading modes: distal UAV and proximate UAV. Owing to the drift of floating sensor nodes influenced by ocean currents, we employ a deep echo state network to predict the positions of floating sensor nodes and utilize a multi-agent deep deterministic policy gradient to solve the UAVs trajectory planning problem. Under this mechanism, the UAVs can adaptively adjust its flight path while exploring floating sensor nodes in dynamically changing ocean sensor node scenarios. Extensive experiments demonstrate that the proposed scheme can adapt to dynamic ocean environments, achieving low-AoI data collection from floating sensor nodes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100854"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652865","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":"Hybrid mutual authentication for vehicle-to-infrastructure communication without the coverage of roadside units","authors":"Huizhi Tang,&nbsp;Abdul Rauf,&nbsp;Qin Lin,&nbsp;Guoqing Dou,&nbsp;Changshuai Qin","doi":"10.1016/j.vehcom.2024.100850","DOIUrl":"10.1016/j.vehcom.2024.100850","url":null,"abstract":"<div><div>The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100850"},"PeriodicalIF":5.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652864","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":"Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness","authors":"Haitao Li,&nbsp;Jiawei Huang","doi":"10.1016/j.vehcom.2024.100853","DOIUrl":"10.1016/j.vehcom.2024.100853","url":null,"abstract":"<div><div>The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100853"},"PeriodicalIF":5.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652889","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":"Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization","authors":"Maria Christina Blessy A ,&nbsp;Brindha S","doi":"10.1016/j.vehcom.2023.100660","DOIUrl":"10.1016/j.vehcom.2023.100660","url":null,"abstract":"<div><p><span><span><span>Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced </span>intelligent transportation systems<span>. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. </span></span>Clustering algorithms<span><span> are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and </span>packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and </span></span>higher clustering<span> efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.</span></p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100660"},"PeriodicalIF":6.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135389454","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":"Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems","authors":"Xiuwen Fu ,&nbsp;Xiong Huang ,&nbsp;Qiongshan Pan","doi":"10.1016/j.vehcom.2023.100719","DOIUrl":"10.1016/j.vehcom.2023.100719","url":null,"abstract":"<div><p><span>In Internet of Things<span><span><span> (IoT) systems, sensor nodes<span> are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of </span></span>unmanned aerial vehicles<span> (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal </span></span>data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy </span></span>replenishment<span>. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism<span> and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.</span></span></p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100719"},"PeriodicalIF":6.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138823036","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":"Traffic prediction assisted wavelength allocation in vehicle-to-infrastructure communication: A fiber-wireless network based framework","authors":"Akshita Gupta ,&nbsp;Abhishek Pratap Singh ,&nbsp;Arunima Srivastava ,&nbsp;Vivek Ashok Bohara ,&nbsp;Anand Srivastava ,&nbsp;Martin Maier","doi":"10.1016/j.vehcom.2023.100713","DOIUrl":"10.1016/j.vehcom.2023.100713","url":null,"abstract":"<div><p><span>The advent of the next generation of connected and autonomous cars offers immense opportunities for both users as well as service providers. In particular, fiber-wireless (FiWi) based vehicle-to-infrastructure (V2I) network can facilitate some of the stringent requirements of sixth-generation (6G) vehicular networks, including higher capacity, lower delay, and ubiquitous connectivity. FiWi based V2I network integrates the next generation </span>passive optical network<span><span> 2 (NG-PON2) with IEEE 802.11p based V2I network. In this work, we first review the various kinds of vehicular data traffic and their desired key performance indicators<span> (KPIs), namely throughput, delay, and reliability. Depending on the KPI requirements, the V2I traffic is classified among four classes and assigned to different transmission containers (T-CONTs) of the optical network unit (ONU). Further, in order to minimize the delay of the network, we propose a </span></span>machine learning<span> (ML) based T-CONT priority assignment wavelength allocation algorithm that minimizes the number of wavelength switching instances in the PON. The performance of the proposed ML-based wavelength allocation algorithm is compared with the other approaches, namely random and equal T-CONT based wavelength allocation algorithms. Simulation results demonstrate the efficiency of the proposed algorithm vis-a-vis other approaches in terms of end-to-end (e2e) delay, throughput, and reliability.</span></span></p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100713"},"PeriodicalIF":6.7,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138740661","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 management for sum-rate maximization in SCMA-assisted UAV system","authors":"Saumya Chaturvedi ,&nbsp;Vivek Ashok Bohara ,&nbsp;Zilong Liu ,&nbsp;Anand Srivastava ,&nbsp;Pei Xiao","doi":"10.1016/j.vehcom.2023.100714","DOIUrl":"10.1016/j.vehcom.2023.100714","url":null,"abstract":"<div><p><span><span>This work presents a resource management framework for optimizing the sum-rate in a sparse code multiple access (SCMA)-assisted UAV<span><span> downlink system. We formulate two optimization problems for maximizing the overall sum-rate: the first problem addresses UAV 3D deployment and trajectory optimization with energy constraints, while the second focuses on optimizing SCMA </span>subcarrier and </span></span>power allocation<span><span> optimization, subject to factor graph matrix (FGM) constraints and a minimum user data rate. Since the optimization problems are non-convex, the complexity of finding the </span>global optimal solutions is prohibitive. We propose a gradient ascent-based </span></span>iterative algorithm<span> to compute the optimal UAV 3D deployment and trajectory. Further, an effective channel state information-based algorithm is proposed for FGM assignment, followed by a Lagrange<span> dual decomposition method to solve the power allocation problem efficiently. Our research findings demonstrate that the optimization of the UAV trajectory gives improved sum-rate within the specified energy budget. Further, employing CSI-based multiple subcarrier allocation and strategic power allocation can significantly improve system performance compared to the benchmark schemes.</span></span></p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100714"},"PeriodicalIF":6.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138657656","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":"Measurement-based fading characteristics analysis and modeling of UAV to vehicles channel","authors":"Yue Lyu ,&nbsp;Wei Wang ,&nbsp;Yuzhe Sun ,&nbsp;Ibrahim Rashdan","doi":"10.1016/j.vehcom.2023.100707","DOIUrl":"10.1016/j.vehcom.2023.100707","url":null,"abstract":"<div><p><span>With the rapid development of unmanned aerial vehicle<span> (UAV) and autonomous driving technology, </span></span>wireless communication<span><span><span> between UAV and vehicles has become one of the hotspots in the research of intelligent transportation systems (ITS). Particularly, link-level UAV-based communication requires correlation characteristics of </span>propagation channel<span>. In the current channel measurement, the transmitter or receiver of the ground is fixed, which ignores the high dynamics of the vehicle and the complexity of the environment in the ITS scene. Therefore, it is necessary to conduct a dynamic channel measurement and analysis for UAV. In this paper, we carry out an UAV-to-Vehicle (U2V) measurement campaign in S- and C-band for multiple scenarios of low-altitude UAV and mobile vehicles propagation and provide a comprehensive investigation of channel fading characteristics. Based on the measurement data, the statistics of large-scale fading (path loss, shadow fading and its autocorrelation) and small-scale fading (amplitude distribution) for several typical measurement scenarios are extracted first, which are compared with other air-to-ground (A2G) and standard terrestrial propagation scenarios to analyze the U2V propagation characteristics<span> in various scenarios. A comprehensive analysis and comparative study of all considered channel parameters extracted is then performed to reflect the physical laws behind the measurements. The analysis results reveal that the Log-distance model outperforms the considered typical models in terms of predicting the path loss, and the proposed autocorrelation model shows better performance than traditional models. The quantitative results are essential for modeling and realizing reliable communications in U2V </span></span></span>wireless systems and analyzing the performance for UAV-enabled ITS.</span></p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100707"},"PeriodicalIF":6.7,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138481230","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":"Performance analysis of IEEE 802.11p preamble insertion in C-V2X sidelink signals for co-channel coexistence","authors":"Alessandro Bazzi ,&nbsp;Stefania Bartoletti ,&nbsp;Alberto Zanella ,&nbsp;Vincent Martinez","doi":"10.1016/j.vehcom.2023.100710","DOIUrl":"10.1016/j.vehcom.2023.100710","url":null,"abstract":"<div><p>Spectrum scarcity is one of the main challenges of future wireless technologies. When looking at vehicle-to-everything (V2X), this is amplified as spectrum sharing could impact road safety and traffic efficiency. It is therefore of particular importance to study solutions that allow the coexistence, in the same geographical area and in the same channels, of what are today the main V2X access technologies, namely IEEE 802.11p and long term evolution (LTE)-V2X sidelink Mode 4. In this paper, in addition to studying the impact of mutual interference, which is found to have a strong impact especially on the former and under congested channel conditions, a mitigation solution is extensively studied. The solution is based on the insertion of the IEEE 802.11p preamble at the beginning of each LTE-V2X sidelink transmission. The proposal, which is also under discussion within the standardization bodies, requires no changes to the IEEE 802.11p protocol stack and minor changes to LTE-V2X sidelink. This solution is directly applicable to upcoming IEEE 802.11bd and extendable to new radio (NR)-V2X sidelink. The paper shows, through analysis and simulations in free-flow and dense scenarios, that the proposal enables mitigation of collisions caused by co-channel coexistence under low and high load conditions. The improvement is guaranteed even in cases of congestion when combined with additional countermeasures. Regarding the latter aspect, in particular, different approaches are compared, demonstrating that acting on the congestion control mechanisms is a simple but effective solution.</p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"45 ","pages":"Article 100710"},"PeriodicalIF":6.7,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214209623001407/pdfft?md5=57cee885305a475f5a3d8c18a2181467&pid=1-s2.0-S2214209623001407-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138481231","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}