您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报 (工学版) ›› 2025, Vol. 55 ›› Issue (3): 25-33.doi: 10.6040/j.issn.1672-3961.0.2024.229

• 交通运输工程——智慧交通专题 • 上一篇    

基于位置信息的路侧单元数据传输调度优化策略

时颖1,2,张丹洋2,王桐1,陈义平2,付鑫3*   

  1. 1.哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001;2. 黑龙江科技大学电子与信息工程学院, 黑龙江 哈尔滨 150022;3.长安大学运输工程学院, 陕西 西安 710064
  • 发布日期:2025-06-05
  • 作者简介:时颖(1980— ),女,黑龙江鸡西人,教授,硕士生导师,硕士,主要研究方向为车联网与智能交通. E-mail:s_ying@hrbeu.edu.cn. *通信作者简介:付鑫(1982— ),男,山东日照人,教授,博士生导师,博士,主要研究方向为交通运输系统. E-mail:fuxin@chd.edu.cn
  • 基金资助:
    国家重点研发计划资助项目(2020YFC1512002);国家自然科学基金资助项目(62372131);长安大学中央高校基本科研业务费专项资金资助项目(300102343516);黑龙江省属高校基本科研业务费资助项目(2022-KYYWF-0564,2023-KYYWF-0531)

Data transmission scheduling optimization strategy of roadside unit based on location information

SHI Ying1,2, ZHANG Danyang2, WANG Tong1, CHEN Yiping2, FU Xin3*   

  1. SHI Ying1, 2, ZHANG Danyang2, WANG Tong1, CHEN Yiping2, FU Xin3*(1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China;
    2. School of Electric and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, Heilongjiang, China;
    3. School of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
  • Published:2025-06-05

摘要: 针对如何降低车路协同系统中路侧单元间载带中继数据传输总时延的问题,考虑路侧单元覆盖范围内V2I(vehicle-to- infrastructure)下行数据传输速率自适应变化特性,提出一种基于车辆位置信息的数据传输调度策略。该策略综合考虑数据更新的随机性、位置差异的数据传输速率自适应性,依据数据缓存队列的状态转移构建马尔科夫链模型;同时,考虑路侧单元覆盖范围内存在多辆车的情况,提出基于车辆速度和车辆位置联合权重的车辆优先通信模型,以此为基础确定通信概率服务策略;建立以数据传输总时延最小为目标的非线性优化函数,通过线性化求解获得路侧单元数据传输调度最优策略。仿真结果表明,在车辆到达率、数据到达率变化的条件下,该策略均能有效减少传输总时延,且该策略能抵御数据到达率的变化,具有较好的数据传输稳定性。

关键词: 车路协同系统, 路侧单元, 数据传输, 位置信息, 马尔科夫链, 时延

Abstract: To address the problem of how to reduce the total delay of data transmission with relay between roadside units, considering the adaptive variation characteristics of the V2I downlink data transmission rate within the coverage of roadside units, a data transmission scheduling strategy based on vehicle location information was proposed. This strategy comprehensively considered the randomness of data update and the adaptability of data transmission rate of location difference, and constructed a Markov chain model based on the state transition of a data cache queue. At the same time, considering the existence of multiple vehicles in the roadside unit coverage area, a vehicle priority communication model based on the joint weight of vehicle speed and vehicle position was proposed to determine the communication service strategy. A nonlinear optimization function with the objective of minimizing the total delay of data transmission was established, and the optimal data transmission scheduling strategy of the roadside unit was obtained by linearization. The simulation results showed that the LTS strategy could effectively reduce the total transmission delay under the condition of vehicle arrival rate and data arrival rate change, and the strategy could resist the change of data arrival rate and had good stability of the data transmission.

Key words: cooperative vehicle-infrastructure system, roadside units, data transmission, location information, Markov chain, time delay

中图分类号: 

  • U491.2
[1] ABBOUD K, OMAR H A, ZHUANG W H. Interworking of DSRC and cellular network technologies for V2X communications: a survey[J]. IEEE Trans-actions on Vehicular Technology,2016, 65(12): 9457-9470.
[2] NAIR A, TANWAR S. Resource allocation in V2X communication: state-of-the-art and research challenges[J]. Physical Communication, 2024, 64(6):102351.
[3] GUO C T, LIANG L, LI G Y. Resource allocation for low-latency vehicular communications: an effective capacity perspective[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(4): 905-917.
[4] ZHANG Q X, MENG H, FENG Z Y, et al. Resource scheduling of time-sensitive services for B5G/6G connected automated vehicles[J]. IEEE Internet of Things Journal, 2023, 10(16): 14820-14833.
[5] FARDAD M, MUNTEAN G M, TAL I. Latency-aware V2X operation mode coordination in vehicular network slicing[C] // 2023 IEEE 97th Vehicular Technology Conference(VTC2023-Spring). Florence, Italy: IEEE, 2023:1-6.
[6] HU B T, DU J B, CHU X L,et al. Enabling low-latency applications in vehicular networks based on mixed fog/cloud computing systems[C] // 2022 IEEE Wireless Communications and Networking Conference(WCNC), Austin, USA: ACM, 2022: 722-727.
[7] MOKHTARI S, NOURI N, ABOUEI J, et al. Relaying data with joint optimization of energy and delay in cluster-based UAV-assisted VANETs[J]. IEEE Internet of Things Journal, 2022, 9(23): 24541-24559.
[8] NKENYEREYE L, NKENYEREYE L, PHAM Q V, et al. Efficient RSU selection scheme for fog-based vehicular software-defined network[J]. IEEE Transactions on Vehicular Technology, 2021, 70(11): 12126-12141.
[9] KHAYAT G, MAVROMOUSTAKIS C X, MASTORAKIS G, et al. VANET clustering based on weighted trusted cluster head selection[C] //International Wireless Communications and Mobile Computing(IWCMC). Limassol, Cyprus:IEEE, 2020: 623-628.
[10] HOU J, CHEN G, HUANG J, et al. Large-scale vehicle platooning: advances and challenges in scheduling and planning techniques[J]. Engineering, 2023, 28: 26-48.
[11] ZHANG W H, FENG M J, KRUNZ M, et al. Latency prediction for delay-sensitive V2X applications in mobile cloud/edge computing systems[C] // IEEE Global Communications Conference(GLOBECOM), Taipei, China: IEEE, 2020: 1-6.
[12] YAO L, WANG J, WANG X, et al.V2X routing in a VANET based on the hidden Markov model[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(3): 889-899.
[13] SHI Y, FENG C, WANG T, et al. The optimal packets scheduling for buffer-aid energy harvesting RSUs in cooperative vehicle infrastructure system[J]. Wireless Communications and Mobile Computing, 2022: 4147373.
[14] LUO Q Y, LI C L, LUAN T H, et al. Collaborative data scheduling for vehicular edge computing via deep reinforcement learning[J]. IEEE Internet of Things Journal, 2020, 7(10): 9637-9650.
[15] MAFUTA A D, MAHARAJ B T J, ALFA A S. Decentralized resource allocation-based multiagent deep learning in vehicular network[J]. IEEE Systems Journal, 2022, 17(1): 87-98.
[16] XU W C, ZHOU H B, SHI W S, et al. Throughput analysis of in-vehicle internet access via on-road WiFi access points[C] //2017 IEEE 86th Vehicular Tech-nology Conference(VTC-Fall). Toronto, Canada:IEEE, 2017: 1-5.
[17] AHMED Z, NAZ S, AHMED J. Minimizing tran-smission delays in vehicular ad hoc networks by optimized placement of road-side unit[J]. Wireless Networks, 2020, 26(4): 2905-2914.
[18] 代亮, 张金龙, 秦雯. 面向交通能源融合的路侧单元传输控制优化策略[J]. 控制与决策, 2023, 38(12):3354-3362. DAI Liang, ZHANG Jinlong, QIN Wen. Optimization strategy of roadside units transmission control for transportation-energy integration[J]. Control and Decision, 2023, 38(12): 3354-3362.
[19] KIM K, LEE J, LEE W. A MAC protocol using road traffic estimation for infrastructure-to-vehicle communi-cations on highways[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1500-1509.
[20] KHABBAZ M J, FAWAZ W F, ASSI C M. A simple free-flow traffic model for vehicular intermittently connected networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(3): 1312-1326.
[21] 代亮, 张亚楠, 钱超, 等. 基于车辆载带中继的路边单元突发业务分组调度最优策略[J].自动化学报, 2021, 47(5): 1098-1110. DAI Liang, ZHANG Yanan, QIAN Chao, et al. Optimal packet scheduling strategy for roadside units' bursty traffic based on relaying vehicles[J]. ACTA Automatica Sinica, 2021, 47(5): 1098-1110.
[22] ZHOU H B, LIU B, HOU F, et al. Spatial coordinated medium sharing: optimal access control management in drive-thru Internet[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5): 2673-2686.
[23] XU W C, ZHOU H B, SHI W S, et al. Throughput analysis of in-vehicle Internet access via on-road WiFi access points[C] //2017 IEEE 86th Vehicular Techn-ology Conference(VTC-Fall), Toronto, Canada: IEEE, 2017: 1-5.
[24] WU J, CHEN W. Low-Latency and energy-efficient wireless communications with energy harvesting[J]. IEEE Transactions on Wireless Communications, 2022, 21(2): 1244-1256.
[25] 林峰, 丁鹏举, 梁吉申, 等. 车联网中协作数据分发方案研究[J]. 计算机工程, 2021, 47(8): 29-36. LIN Feng, DING Pengju, LIANG Jishen, et al. Research on collaborative data distribution scheme in Internet of vehicles[J]. Computer Engineering, 2021, 47(8): 29-36.
[1] 张双圣,强静,刘喜坤,刘汉湖,朱雪强. 基于贝叶斯-微分进化算法的污染源识别反问题[J]. 山东大学学报(工学版), 2018, 48(1): 131-136.
[2] 秦利国,何潇,周东华. 一种时延多智能体系统的分布式编队[J]. 山东大学学报(工学版), 2017, 47(5): 79-88.
[3] 赵英弘,何潇,周东华. 一类含有传感器故障的网络化系统容错估计[J]. 山东大学学报(工学版), 2017, 47(5): 71-78.
[4] 王晓燕1,王康周2,祁忠斌1,江志斌3,李娜3. 不耐烦顾客生产服务系统建模与优化[J]. 山东大学学报(工学版), 2014, 44(2): 55-63.
[5] 洪晓芳1,2,王玉振1*, 魏爱荣1. 一类执行器饱和非线性Hamilton网络控制系统H∞控制器设计[J]. 山东大学学报(工学版), 2014, 44(1): 49-56.
[6] 石海东,胡冬梅,王晓东. 分布式天线系统中信道时延扩展的统计分析[J]. 山东大学学报(工学版), 2012, 42(1): 133-142.
[7] 王心一1,杜光2*. 降采样固定时延估算法在声回波对消系统中的应用[J]. 山东大学学报(工学版), 2011, 41(3): 42-45.
[8] 鉴萍,李歧强 . 力反馈遥操作机器人系统的内模控制[J]. 山东大学学报(工学版), 2006, 36(5): 49-53 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!