IEEE Open Journal of Vehicular Technology最新文献_第2页

Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-20 DOI: 10.1109/OJVT.2024.3502803

Parisa Parhizgar;Mehdi Mahdavi;Mohammad Reza Ahmadzadeh;Melike Erol-Kantarci

{"title":"Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management","authors":"Parisa Parhizgar;Mehdi Mahdavi;Mohammad Reza Ahmadzadeh;Melike Erol-Kantarci","doi":"10.1109/OJVT.2024.3502803","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3502803","url":null,"abstract":"This paper investigates resource management in device-to-device (D2D) networks coexisting with cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network's energy needs, we formulate an online optimization problem. This formulation considers factors such as the quality of service (QoS) for both CUEs and D2Ds, available power, information freshness, and environmental sensing requirements. Due to the mixed-integer nonlinear nature and online characteristics of the problem, we propose a deep reinforcement learning (DRL) approach to solve it effectively. Numerical results show that the proposed joint scheduling and resource management strategy, utilizing the soft actor-critic (SAC) algorithm, reduces the average AoI by 20% compared to other baseline methods.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"52-67"},"PeriodicalIF":5.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource Allocation for Intelligent Reflecting Surface Enabled Target Tracking in Integrated Sensing and Communication Systems

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-19 DOI: 10.1109/OJVT.2024.3502153

Guilu Wu;Haoyu Liu;Junkang You;Xiangshuo Zhao;Han chen

{"title":"Resource Allocation for Intelligent Reflecting Surface Enabled Target Tracking in Integrated Sensing and Communication Systems","authors":"Guilu Wu;Haoyu Liu;Junkang You;Xiangshuo Zhao;Han chen","doi":"10.1109/OJVT.2024.3502153","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3502153","url":null,"abstract":"Intelligent reflecting surface (IRS) is a promising enabler for achieving communication quality of service (QoS) and enhancing sensing QoS in Integrated Sensing and Communication (ISAC) systems. It has been regarded as one of the most attractive solutions for facilitating vehicle applications in internet of vehicles (IoV) by utilizing ISAC technologies. In this paper, the trajectory of target vehicle goes through no obstacle blocking stage and obstacle blocking stage successively in ISAC systems. And the performance trad-off is pursued in the sensing QoS and the communication QoS of the target vehicle. The achievable rate and posterior Cramer-Rao lower bounds (PCRLBs) are defined to reflect communication QoS and sensing QoS, respectively. In this process, the trade-off strategy on QoS for communication and IRS assisted sensing is explored in IoV. Hence, an optimization problem is designed to ensure communication capability of the target while ensuring its sensing ability. The joint semidefinite relaxation (SDR) and alternating optimization (AO) method is proposed to obtain the optimal solution on resource allocation (RA) and IRS phase shift. Simulation results verify the effectiveness of the proposed method in terms of performance trade-off between communication QoS and sensing QoS.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1-12"},"PeriodicalIF":5.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Quadrotor Path Planning Through Deep Reinforcement Learning With Monocular Depth Estimation

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-19 DOI: 10.1109/OJVT.2024.3502296

Mahdi Shahbazi Khojasteh;Armin Salimi-Badr

{"title":"Autonomous Quadrotor Path Planning Through Deep Reinforcement Learning With Monocular Depth Estimation","authors":"Mahdi Shahbazi Khojasteh;Armin Salimi-Badr","doi":"10.1109/OJVT.2024.3502296","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3502296","url":null,"abstract":"Autonomous navigation is a formidable challenge for autonomous aerial vehicles operating in dense or dynamic environments. This paper proposes a path-planning approach based on deep reinforcement learning for a quadrotor equipped with only a monocular camera. The proposed method employs a two-stage structure comprising a depth estimation and a decision-making module. The former module uses a convolutional encoder-decoder network to learn image depth from visual cues self-supervised, with the output serving as input for the latter module. The latter module uses dueling double deep recurrent Q-learning to make decisions in high-dimensional and partially observable state spaces. To reduce meaningless explorations, we introduce the Insight Memory Pool alongside the regular memory pool to provide a rapid boost in learning by emphasizing early sampling from it and relying on the agent's experiences later. Once the agent has gained enough knowledge from the insightful data, we transition to a targeted exploration phase by employing the Boltzmann behavior policy, which relies on the refined Q-value estimates. To validate our approach, we tested the model in three diverse environments simulated with AirSim: a dynamic city street, a downtown, and a pillar world, each with different weather conditions. Experimental results show that our method significantly improves success rates and demonstrates strong generalization across various starting points and environmental transformations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"34-51"},"PeriodicalIF":5.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Optimal Placement and Runtime Migration of Time-Sensitive Services of Connected and Automated Vehicles

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-12 DOI: 10.1109/OJVT.2024.3496583

Osama Elgarhy;Yannick Le Moullec;Luca Reggiani;Muhammad Moazam Azeem;Tarik Taleb;Muhammad Mahtab Alam

{"title":"Towards Optimal Placement and Runtime Migration of Time-Sensitive Services of Connected and Automated Vehicles","authors":"Osama Elgarhy;Yannick Le Moullec;Luca Reggiani;Muhammad Moazam Azeem;Tarik Taleb;Muhammad Mahtab Alam","doi":"10.1109/OJVT.2024.3496583","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3496583","url":null,"abstract":"In this paper, the goal is to reduce the time needed for the placement and migration of services of Connected Automated Vehicles (CAVs) using precise hybrid positioning method. First, to place a service in a Multi-access Edge Computing (MEC) node, there should be sufficient resources in the served MEC node; otherwise, the service would be placed on the neighboring MEC node or even on the core node, resulting in higher delays. We start by modeling our problem with the aid of traffic theory to analytically obtain the necessary number of resources for achieving the desired delay. Second, to reduce the migration process delay, the migration should begin before the vehicle reaches the MEC node. Thus, an AI lane-based scheme is proposed to predict candidate nodes for migration based on precise positioning. Precise positioning data is acquired from a Real-Time Kinematic Global Navigation Satellite System (RTK- GNSS) measurement campaign. The obtained imbalanced raw data is treated and used in the prediction scheme, and the resulting prediction accuracy achieves 99.3%. Finally, we formulate a service placement and migration delay optimization problem and propose an algorithm to solve it. The algorithm shows a latency reduction of approximately 50% compared to the core placement and up to 29% compared to the benchmark prediction algorithm. Moreover, the simulation results for the proposed service placement and migration algorithm show that in case the MEC resource calculations are not used, the delay is 2.2 times greater than when they are used.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"13-33"},"PeriodicalIF":5.3,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing AAV-to-Ground Communication Security With the Proceed-Hover-Return (PHR) Approach 利用 "继续-悬停-返回"（PHR）方法提高无人飞行器与地面通信的安全性

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-08 DOI: 10.1109/OJVT.2024.3494740

Yulin Zhou;Aziz Altaf Khuwaja;Hua Yan

{"title":"Enhancing AAV-to-Ground Communication Security With the Proceed-Hover-Return (PHR) Approach","authors":"Yulin Zhou;Aziz Altaf Khuwaja;Hua Yan","doi":"10.1109/OJVT.2024.3494740","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3494740","url":null,"abstract":"Downlink communications with multiple ground targets using autonomous aerial vehicles (AAVs) as a base station is critical across various fields. However, the presence of potential eavesdroppers introduces significant challenges that must be addressed to ensure the effectiveness and security of these operations. In this work, we investigate the AAV-to-ground communications in the presence of an eavesdropper, focusing on enhancing physical layer security by maximizing the secrecy rate. We propose a Proceed-Hover-Return (PHR) approach, which optimizes the AAV's trajectory, flight time, speed, and hovering duration to achieve the highest possible secrecy rate during communication with ground users. Our numerical results demonstrate the effectiveness of the PHR scheme, which consistently outperforms the fixed-time baseline approach, with particularly significant improvements for users located farther from the eavesdropper. This work provides essential insights into designing secure and efficient AAV communication systems in scenarios involving multiple ground users and potential eavesdroppers.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1746-1755"},"PeriodicalIF":5.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Performance and Radiation Regulation Compliance for a Small Sub-Array Based Ka Band Antenna

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-11-07 DOI: 10.1109/OJVT.2024.3494040

Aparna P. T. Adithyababu;Stefano Caizzone;Ramon Martinez Rodríguez-Osorio

引用次数: 0

OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing OptiFlow：利用改进的集群路由优化智能交通系统中的交通流

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-30 DOI: 10.1109/OJVT.2024.3488084

Roopa Tirumalasetti;Sunil Kumar Singh

{"title":"OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing","authors":"Roopa Tirumalasetti;Sunil Kumar Singh","doi":"10.1109/OJVT.2024.3488084","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3488084","url":null,"abstract":"Intelligent Transport Systems (ITS) rely heavily on Vehicular Ad hoc Networks (VANET) to facilitate effective communication, especially Vehicle-to-Everything (V2X) communication. However, current research has identified challenges in node management, security, and routing within VANET, calling for bespoke solutions to address these issues. This study introduces an innovative cluster-based routing strategy using Enhanced Slap Swarm Optimization (ESSO) and Evaluation with Mixed Data Multi-criteria Decision-Making (EVAmix MCDM) Method tailored to optimize routing in V2X communication. Unlike existing meta-heuristic methods, which often face slow convergence, premature convergence, and local optima stability, the proposed approach demonstrates striking results. Notably, it enhances throughput by 6278 kbps, elevates the Packet Delivery Ratio (PDR) by 95.77\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000, and reduces end-to-end delay by 1856ms in the 300th iteration, outperforming existing cluster routing methodologies. Our findings suggest a substantial leap toward surmounting the existing challenges in V2X communication. This innovative solution advances the field and sets a course for real-time applications. This approach allows vehicles to continually monitor, adjust their position, and control their speed on highways, enhancing safety and traffic control.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1727-1745"},"PeriodicalIF":5.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Modeling of Interest Forwarding in Information Centric Vehicular Networks 以信息为中心的车载网络中兴趣转发的高效建模

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-28 DOI: 10.1109/OJVT.2024.3487245

Surya Samantha Beri;Nitul Dutta

{"title":"Efficient Modeling of Interest Forwarding in Information Centric Vehicular Networks","authors":"Surya Samantha Beri;Nitul Dutta","doi":"10.1109/OJVT.2024.3487245","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3487245","url":null,"abstract":"Vehicular networks (V-Nets) of the current era is widely used for information sharing and entertainment. Considering the popularity and demand for faster data communications, V-Nets are evolving towards an information-centric approach, namely the Information Centric Vehicular Networks (ICVN). In ICVN, nodes such as vehicles, roadside units, pedestrian devices, and sensors, generate interest packets and forward them to appropriate cache stores to retrieve desired data. Hence, an efficient interest forwarding algorithm in ICVN considerably enhances the content retrieval performance and make supports road safety, traffic management, environmental monitoring in real-vehicular environment. However, designing such efficient scheme is critical due to the involvement of various communication scenarios like vehicle-to-vehicle (V2V), vehicle-to-roadside unit (V2R), vehicle-to-in-vehicle sensors (V2S), vehicle-to-mobile infrastructure (V2I), and vehicle-to-personal device (V2P) and it demands a comprehensive and connected vehicular network. In this paper, we propose an interest forwarding scheme for ICVN with the above said realistic scenario. The interest forwarding process is governed by the Secretary Selection Problem (SSP) which guides a vehicular node to select the next level forwarder for locating the desired content. The goal is to select the best neighboring vehicle as the next forwarder thereby maximizing the chances of obtaining the requested data. The proposed scheme, named as Selection Based Interest Forwarding (SBIF) which uses Secretary Selection Problem (SSP) for governing the forward process to select the next level forwarder for locating the desired content. This scheme is analyzed mathematically and implemented in \u0000<inline-formula><tex-math>$ndn$</tex-math></inline-formula>\u0000 simulator. Performance is compared with existing state of the art schemes and observation shows that SBIF outperforms existing approaches considered for comparison.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1680-1691"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Deep Reinforcement Learning Based Optimizing Joint 3D Trajectories and Phase Shifts in RIS-Assisted UAV-Enabled Wireless Communications 基于多代理深度强化学习优化 RIS 辅助无人机无线通信中的联合 3D 轨迹和相位偏移

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-24 DOI: 10.1109/OJVT.2024.3486197

Belayneh Abebe Tesfaw;Rong-Terng Juang;Hsin-Piao Lin;Getaneh Berie Tarekegn;Wendenda Nathanael Kabore

{"title":"Multi-Agent Deep Reinforcement Learning Based Optimizing Joint 3D Trajectories and Phase Shifts in RIS-Assisted UAV-Enabled Wireless Communications","authors":"Belayneh Abebe Tesfaw;Rong-Terng Juang;Hsin-Piao Lin;Getaneh Berie Tarekegn;Wendenda Nathanael Kabore","doi":"10.1109/OJVT.2024.3486197","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3486197","url":null,"abstract":"Unmanned aerial vehicles (UAVs) serve as airborne access points or base stations, delivering network services to the Internet of Things devices (IoTDs) in areas with compromised or absent infrastructure. However, urban obstacles like trees and high buildings can obstruct the connection between UAVs and IoTDs, leading to degraded communication performance. High altitudes can also result in significant path losses. To address these challenges, this paper introduces the deployment of reconfigurable intelligent surfaces (RISs) that smartly reflect signals to improve communication quality. It proposes a method to jointly optimize the 3D trajectory of the UAV and the phase shifts of the RIS to maximize communication coverage and ensure satisfactory average achievable data rates for RIS-assisted UAV-enabled wireless communications by considering mobile multi-user scenarios. In this paper, a multi-agent double-deep \u0000<italic>Q</i>\u0000-network (MADDQN) algorithm is presented, which each agent dynamically adjusts either the positioning of the UAV or the phase shifts of the RIS. Agents learn to collaborate with each other by sharing the same reward to achieve a common goal. In the simulation, results demonstrate that the proposed method significantly outperforms baseline strategies in terms of improving communication coverage and average achievable data rates. The proposed method achieves 98.6% of a communication coverage score, while IoTDs are guaranteed to have acceptable achievable data rates.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1712-1726"},"PeriodicalIF":5.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks 下一代交通网络中由数字孪生驱动的绿色交通管理

IF 5.3

IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-22 DOI: 10.1109/OJVT.2024.3484956

Kubra Duran;Lal Verda Cakir;Achille Fonzone;Trung Q. Duong;Berk Canberk

{"title":"Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks","authors":"Kubra Duran;Lal Verda Cakir;Achille Fonzone;Trung Q. Duong;Berk Canberk","doi":"10.1109/OJVT.2024.3484956","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3484956","url":null,"abstract":"Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u0000 20% decrease in emissions by presenting an effective carbon tracing framework.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1650-1662"},"PeriodicalIF":5.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0