Vehicular Communications最新文献

RIS-aided jellyfish search optimization for multiuser wireless networks improvement 多用户无线网络改进的 RIS 辅助水母搜索优化

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-10 DOI: 10.1016/j.vehcom.2024.100863

Zahraa Tarek, Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen, Ahmed S. Alwakeel

{"title":"RIS-aided jellyfish search optimization for multiuser wireless networks improvement","authors":"Zahraa Tarek, Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen, Ahmed S. Alwakeel","doi":"10.1016/j.vehcom.2024.100863","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100863","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RISs) provide a promising avenue for enhancing performance and implementation efficiency in multiuser wireless communication systems by enabling the manipulation of radio wave propagation. In this paper, an Augmented Jellyfish Search Optimization Algorithm (AJFSOA) is specifically designed to optimize the achievable rate in RIS-equipped systems. AJFSOA distinguishes itself from previous approaches through the integration of a novel quasi-reflection operator, which aids in escaping local optima, and an adaptive neighborhood search mechanism that improves the algorithm's exploitation capabilities. These enhancements enable AJFSOA to efficiently refine promising solutions near the current best solution. Unlike prior research, our work explores two objective models: maximizing the average achievable rate for all users to ensure balanced system performance and maximizing the minimum achievable rate for individual users to improve worst-case scenarios. The comprehensive analysis demonstrates that AJFSOA effectively increases system capacity and supports a larger number of users simultaneously. An extensive testing is performed on communication systems with twenty and fifty users, comparing AJFSOA's performance against existing algorithms, including the standard JFSOA, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Differential Evolution (DE). Results show that AJFSOA outperforms the other algorithms significantly, with improvements of 26.59%, 9.75%, 14.71%, 0.29% and 13.52% over JFSOA, PSO, ACO, GA and DE, respectively, for the first objective model, and 21.66%, 10.6%, .17.44%, 2.71% and 22.36% for the second model. These findings highlight the distinct advantages and superior performance of the presented AJFSOA in efficient optimizing multiuser wireless networks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"42 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825354","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 review of smart vehicles in smart cities: Dangers, impacts, and the threat landscape 回顾智能城市中的智能车辆：危险、影响和威胁状况

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-07 DOI: 10.1016/j.vehcom.2024.100871

Brooke Kidmose

引用次数: 0

Federated learning on the go: Building stable clusters and optimizing resources on the road 移动中的联合学习：在旅途中构建稳定的集群并优化资源

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-06 DOI: 10.1016/j.vehcom.2024.100870

Sawsan AbdulRahman, Safa Otoum, Ouns Bouachir

{"title":"Federated learning on the go: Building stable clusters and optimizing resources on the road","authors":"Sawsan AbdulRahman, Safa Otoum, Ouns Bouachir","doi":"10.1016/j.vehcom.2024.100870","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100870","url":null,"abstract":"With the proliferation of Internet of Things, leveraging federated learning (FL) for collaborative model training has become paramount. It has turned into a powerful tool to analyze on-device data and produce real-time applications while safeguarding user privacy. However, in vehicular networks, the dynamic nature of vehicles, coupled with resource constraints, gives rise to new challenges for efficient FL implementation. In this paper, we address the critical problems of optimizing computational and communication resources and selecting the appropriate vehicle to participate in the process. Our proposed scheme bypasses the communication bottleneck by forming homogeneous groups based on the vehicles mobility/direction and their computing resources. Vehicle-to-Vehicle communication is then adapted within each group, and communication with an on-road edge node is orchestrated by a designated Cluster Head (CH). The latter is selected based on several factors, including connectivity index, mobility coherence, and computational resources. This selection process is designed to be robust against potential cheating attempts, which prevents nodes from avoiding the role of CH to conserve their resources. Moreover, we propose a matching algorithm that pairs each vehicular group with the appropriate edge nodes responsible for aggregating local models and facilitating communication with the server, which subsequently processes the models from all edges. The conducted experiments show promising results compared to benchmarks by achieving: (1) significantly higher amounts of trained data per iteration through strategic CH selection, leading to improved model accuracy and reduced communication overhead. Additionally, our approach demonstrates (2) efficient network load management, (3) faster convergence times in later training rounds, and (4) superior cluster stability.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"10 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825356","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

The stability for CACC system with time delays and reconstitution information of vehicles for compensating delays based on Bi-LSTM 基于 Bi-LSTM 的具有时间延迟和补偿延迟的车辆重组信息的 CACC 系统的稳定性

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-06 DOI: 10.1016/j.vehcom.2024.100868

Chenmin Zhang, Yonggui Liu, Zeming Li

{"title":"The stability for CACC system with time delays and reconstitution information of vehicles for compensating delays based on Bi-LSTM","authors":"Chenmin Zhang, Yonggui Liu, Zeming Li","doi":"10.1016/j.vehcom.2024.100868","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100868","url":null,"abstract":"The vehicle platoon using the cooperative adaptive cruise control (CACC) transmits information between vehicles via communication networks to increase the control performance. However, time delays are inevitable during the network transmission of information, which influence the stability of the CACC vehicle system. This paper proposes a method for compensating information affected by time delays based on a Bi-LSTM model. First, the third-order dynamics of the CACC vehicle systems are established, and the control strategies are proposed with the leading, preceding and following vehicles. The conditions of local stability and string stability for the CACC vehicle systems without time delays are derived based on the Routh-Hurwitz stability criterion and the frequency domain methods, which reveal the relationship between the model parameters and the controller parameters. For the CACC vehicle systems with time delays, the maximum time delays that ensure the local stability and string stability are achieved using the similar methods accordingly. However, the stability of the CACC vehicle systems is destroyed, when the time delay exceeds the maximum value. To deal with the impact of time delays, the bidirectional long short term memory (Bi-LSTM) model is adopted to predict and reconstitute the information affected by time delays. Furthermore, the relevant parameters are set and the real vehicle data is used for calculation and simulation. The simulation results confirm the local and string stability can be ensured, and further show the boundary of the maximum time delay may reach 0.45<ce:italic>s</ce:italic> for the CACC vehicle systems in this paper. In order to highlight superiority of Bi-LSTM, by comparing LSTM and KF with BiLSTM, the simulation results show Bi-LSTM has the highest correlation coefficient and the smallest root mean square error, which verify that Bi-LSTM reconstructing information affected by time delays is more effective than KF and LSTM.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"22 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825359","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

VeTraSPM: Novel vehicle trajectory data sequential pattern mining algorithm for link criticality analysis VeTraSPM：用于链路临界度分析的新型车辆轨迹数据序列模式挖掘算法

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-06 DOI: 10.1016/j.vehcom.2024.100869

Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

{"title":"VeTraSPM: Novel vehicle trajectory data sequential pattern mining algorithm for link criticality analysis","authors":"Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen","doi":"10.1016/j.vehcom.2024.100869","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100869","url":null,"abstract":"This paper presents VeTraSPM (Vehicle Trajectory Data Sequential Pattern Mining), a novel algorithm designed to address the limitations of existing sequential pattern mining methods when applied to vehicle trajectory data. Current algorithms fail to capture essential characteristics such as directional flow on one-way roads (e.g., “AB” is valid but not “BA”), connectivity constraints at junctions, and the repetition of links within sequences. VeTraSPM overcomes these gaps by accurately extracting frequent patterns and confident rules while leveraging vertical projection for efficient memory and space management, enabling it to handle large datasets. Furthermore, the algorithm incorporates partitioning and parallelization techniques, further enhancing its scalability for real-world traffic environments. Three new metrics—FqMS, CMS, and SIS—are introduced to assess link criticality based on the consistent occurrence of links across movement patterns at various levels. The efficiency of VeTraSPM is demonstrated through a comparative analysis with baseline algorithms, showcasing its superior performance. The visualization of the proposed metrics offers valuable insights into link importance, supporting proactive traffic management strategies. A case study using real-world datasets from Luxembourg and Monaco validates its scalability and practical value in enhancing the resilience of urban traffic networks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"16 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825357","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

Thank You Reviewers

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-05 DOI: 10.1016/j.vehcom.2024.100872

引用次数: 0

Dimensioning space-air-ground integrated networks for in-flight 6G slice orchestration

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-05 DOI: 10.1016/j.vehcom.2024.100866

László Toka, Endre Angelus Papp, Tibor Cinkler, István Gódor, László Hévizi

{"title":"Dimensioning space-air-ground integrated networks for in-flight 6G slice orchestration","authors":"László Toka, Endre Angelus Papp, Tibor Cinkler, István Gódor, László Hévizi","doi":"10.1016/j.vehcom.2024.100866","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100866","url":null,"abstract":"In this study, we present an in-depth analysis of communication services for commercial airline passengers, focusing on the challenges posed by increasing internet traffic demand. We explore the integration of satellite, airborne, and terrestrial networks, emphasizing the roles of Low Earth Orbit (LEO) satellites, High-Altitude Platform Station (HAPS), and Terrestrial Aviation Network (TAN)-based services. Our contribution includes a theoretical model for optimizing resource allocation and capacity planning in non-terrestrial wireless networks, using a bipartite graph approach and linear programming techniques. The model shows adaptability and efficiency, providing key insights through numerical analysis. Leveraging a detailed air traffic dataset, a machine learning-based aggregation method, and real-world network parameters, our research addresses current challenges, such as scalable network capacity dimensioning in high-density airspaces and meeting the demand for quality of service by robust resource provisioning, and advances the design of communication networks for Space–Air–Ground Integrated Network (SAGIN). Numerical results from European airspace suggest that complementing TAN and LEO satellite networks with HAPS-based services will be essential as airline passengers adopt ground-level internet usage patterns.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"5 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790085","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

STC-GraphFormer: Graph spatial-temporal correlation transformer for in-vehicle network intrusion detection system STC-GraphFormer：用于车载网络入侵检测系统的图时空关联变换器

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-05 DOI: 10.1016/j.vehcom.2024.100865

Gaber A. Al-Absi, Yong Fang, Adnan A. Qaseem

{"title":"STC-GraphFormer: Graph spatial-temporal correlation transformer for in-vehicle network intrusion detection system","authors":"Gaber A. Al-Absi, Yong Fang, Adnan A. Qaseem","doi":"10.1016/j.vehcom.2024.100865","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100865","url":null,"abstract":"The integration of several developing technologies and their applications with Internet of Vehicles (IoVs) techniques has been improved. Utilizing these emerging technologies renders the in-vehicle network more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. The CAN protocol provides an efficient and broadcast-based protocol for facilitating serial data exchange between ECUs. However, it lacks provisions for security measures such as authentication and encryption. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. This paper proposes STC-GraphFormer, an innovative spatial-temporal model that utilizes a Graph Convolutional Network (GCN) and a transformer. The spatial GCN layers are utilized to construct and acquire local spatial features, while the temporal transformer layers are employed to capture the long-term global temporal dependencies. By employing this integrated approach, STC-GraphFormer can learn complex spatial-temporal correlations within the IVN data, enabling it to detect and classify malicious intrusions. The proposed STC-GraphFormer has been validated using five real in-vehicle CAN datasets that cover a wide range of attacks that have not been previously investigated together. The finding results indicate that the STC-GraphFormer is more efficient than the SOTA approaches. It demonstrates excellent performance, with Car-hacking (0.99983), IVN intrusion detection (0.9991), CAN Dataset for intrusion detection “OTIDS” (0.9992), CAR hacking: attack & defense challenge (0.9901), and Survival analysis (0.9982), with a minimal false alarm rate and the highest achievable F1 scores for various types of attacks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"30 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825358","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

Offloading in V2X with road side units: Deep reinforcement learning 在 V2X 中使用路侧装置进行卸载：深度强化学习

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-05 DOI: 10.1016/j.vehcom.2024.100862

Widhi Yahya, Ying-Dar Lin, Faysal Marzuk, Piotr Chołda, Yuan-Cheng Lai

{"title":"Offloading in V2X with road side units: Deep reinforcement learning","authors":"Widhi Yahya, Ying-Dar Lin, Faysal Marzuk, Piotr Chołda, Yuan-Cheng Lai","doi":"10.1016/j.vehcom.2024.100862","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100862","url":null,"abstract":"Traffic offloading is crucial for reducing computing latency in distributed edge systems such as vehicle-to-everything (V2X) networks, which use roadside units (RSUs) and access network mobile edge computing (AN-MEC) with ML agents. Traffic offloading is part of the control plane problem, which requires fast decision-making in complex V2X systems. This study presents a novel ratio-based offloading strategy using the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize offloading ratios in a two-tier V2X system, enabling computation at both RSUs and the edge. The offloading optimization covers both vertical and horizontal offloading, introducing a continuous search space that needs fast decision-making to accommodate fluctuating traffic in complex V2X systems. We developed a V2X environment to evaluate the performance of the offloading agent, incorporating latency models, state and action definitions, and reward structures. A comparative analysis with metaheuristic simulated annealing (SA) is conducted, and the impact of single versus multiple offloading agents with deployment options at a centralized central office (CO) is examined. Evaluation results indicate that TD3's decision time is five orders of magnitude faster than SA. For 10 and 50 sites, SA takes 602 and 20,421 seconds, respectively, while single-agent TD3 requires 4 to 24 milliseconds and multi-agent TD3 takes 1 to 3 milliseconds. The average latency for SA ranges from 0.18 to 0.32 milliseconds, single-agent TD3 from 0.26 to 0.5 milliseconds, and multi-agent TD3 from 0.22 to 0.45 milliseconds, demonstrating that TD3 approximates SA performance with initial training.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"29 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825408","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

Redundant task offloading with dual-reliability in MEC-assisted vehicular networks

IF 6.7 2区计算机科学

Vehicular Communications Pub Date : 2024-12-04 DOI: 10.1016/j.vehcom.2024.100867

Yaoxin Duan, Wendi Nie, Victor C.S. Lee, Kai Liu

{"title":"Redundant task offloading with dual-reliability in MEC-assisted vehicular networks","authors":"Yaoxin Duan, Wendi Nie, Victor C.S. Lee, Kai Liu","doi":"10.1016/j.vehcom.2024.100867","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100867","url":null,"abstract":"With the rise and development of intelligent vehicles, the computation capability of vehicles has increased rapidly and considerably. Vehicle-to-Vehicle (V2V) offloading, in which computation-intensive tasks are offloaded to underutilized vehicles, has been proposed. However, V2V offloading faces the challenges of task transmission reliability and task computation reliability. In V2V offloading, tasks are transmitted via V2V communication, which is volatile and spotty because of rapidly changing network topology and channel conditions between vehicles, resulting in time-varying delays of task transmission and even loss of connectivity. Thus, it is challenging to complete V2V offloading within a given delay constraint. In addition, the realistic diverse vehicular environment always comes with malicious vehicles, which can cause irreparable harm to V2V offloading. Therefore, in this paper, we propose a V2V task offloading scheme called Redundant Task Offloading with Dual-Reliability (RTODR), aiming to minimize task offloading costs while ensuring both task transmission reliability and task computation reliability in a Mobile Edge Computing (MEC)-assisted vehicular network. Specifically, for a computation task, a V2V connection is considered reliable only if the task can be successfully transmitted via the V2V connection within the deadline of the task. To ensure task computation reliability, task computation results from a trusty service vehicle are considered to be reliable. Then we formally model a Minimizing Task Offloading Cost with Dual-reliability (MTOCD) problem, which is mathematically formulated as a multi-objective optimization problem. Afterward, we propose a heuristic redundant task offloading algorithm, named Dual-Reliability Offloading (DRO), to solve the problem. Finally, comprehensive experiments have been conducted to demonstrate that RTODR achieves lower costs compared with other approaches.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"82 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825409","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