基于北斗和边缘计算的车联网导航技术研究

doi:10.16180/j.cnki.issn1007-7820.2023.01.008

摘要/Abstract

摘要：

随着车辆保有量的不断增长和车联网应用的普及,车辆终端会产生大量需要实时处理的数据消息。在车辆高速移动场景下,传统的车联网导航系统由于车辆差分定位数据存在传输时延,导致车辆定位结果存在一定的偏差,无法及时获得高精度定位结果。基于此,文中提出了一种基于北斗定位和边缘计算的车联网导航技术方案,采用改进的遗传算法进行终端定位请求的资源分配,有效降低整个边缘网络的服务时延,并利用基于边缘节点的优化无损卡尔曼滤波算法来提高车联网节点的定位精度。实验表明,文中所提出的方法能够为大规模车联网终端提供实时精准、低延迟和高精度的定位服务,具有较高的实际应用价值。

关键词: 北斗定位, 高精度差分定位, 车联网, 边缘计算, 负载均衡, 无损卡尔曼滤波, 服务时延, 遗传算法

Abstract:

With the continuous growth of vehicle ownership and the popularization of internet of vehicles applications, vehicle terminals generate large amounts of data messages that need to be processed in real time. In the scene of high-speed vehicle movement, the traditional internet of vehicles navigation system has a transmission delay due to vehicle differential positioning data, which results in a certain deviation in vehicle positioning results and cannot obtain high-precision positioning results in time. Based on this, this study proposes a technology solution for car networking navigation based on BDS positioning and edge computing. An improved genetic algorithm is used to allocate resources for terminal positioning requests, which effectively reduces the service delay of the entire edge network. The optimized unscented Kalman filter algorithm based on edge node is used to improve the positioning accuracy of the car network node. Experiments results show that the method proposed can provide real-time accurate, low-latency and high-precision positioning services for large-scale internet of vehicles terminals, and has high practical application value.

Key words: BDS positioning, high-precision differential positioning, internet of vehicles, edge computing, load balancing, unscented Kalman filter, service delay, genetic algorithm

中图分类号:

TN961

周启平,何伟,贾蕾,郭俊凯,赵建国. 基于北斗和边缘计算的车联网导航技术研究[J]. 电子科技, 2023, 36(1): 51-59.

ZHOU Qiping,HE Wei,JIA Lei,GUO Junkai,ZHAO Jianguo. Research on Internet of Vehicles Navigation Technology Based on BDS and Edge Computing[J]. Electronic Science and Technology, 2023, 36(1): 51-59.

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参考文献 22

[1]	Li J, Zhang J, Zhang B, et al. Operation and development of Beidou navigation satellite system[C]. Prague: Proceeding of the International Association of Institutes of Navigation World Congress, 2015.
[2]	Hosseini N, Jamal H, Matolak D W, et al. UAV command and control,navigation and surveillance:A review of potential 5G and satellite systems[C]. Yellowstone: Proceedings of the IEEE Aerospace Conference, 2019.
[3]	Wang S, Zhao Y, Xu J, et al. Edge server placement in mobile edge computing[J]. Journal of Parallel and Distributed Computing, 2019, 127(5):160-168. doi: 10.1016/j.jpdc.2018.06.008
[4]	王毅, 陈启鑫, 张宁, 等. 5G通信与泛在电力物联网的融合: 应用分析与研究展望[J]. 电网技术, 2019, 43(5): 1575-1585.
	Wang Yi, Chen Qixin, Zhang Ning, et al. Application analysis and research prospects of integration of 5G communication and the ubiquitous power internet of things[J]. Telecom Power Technology, 2019, 43(5):1575-1585.
[5]	戴伟, 李明峰, 吴继忠. 北斗卫星导航系统伪距差分定位技术研究[J]. 全球定位系统, 2015, 40(2):36-39.
	Dai Wei, Li Wei, Wu Jizhong. Research on the technology of BDS pseudorange differential positioning[J]. GNSS World of China, 2015, 40(2):36-39.
[6]	张秀斌. 北斗CORS实时动态差分定位精度实验分析[J]. 测绘与空间地理信息, 2016, 39(8):66-68.
	Zhang Xiubin. Analysis of Beidou CORS real time dynamic positioning accuracy experiment[J]. Geomatics & Spatial Information Technology, 2016, 39(8):66-68.
[7]	Kang C. A differential dynamic positioning algorithm based on GPS/Beidou[C]. Xi'an: Proceedings of the Sixth International Conference on Green Intelligent Transportation System and Safety, 2016.
[8]	张胜, 程婕, 向南. 北斗地基增强站与5G通信基站共址可行性研究[J]. 信息通信, 2019(12):49-51.
	Zhang Sheng, Cheng Jie, Xiang Nan. Feasibility study on co-location of Beidou foundation enhancement station and 5G communication base station[J]. Information and Communications, 2019(12):49-51.
[9]	黄梅青, 李宁. 向5G演进的基于边缘计算的车联网应用[J]. 电信科学, 2019, 35(S2):169-175.
	Huang Meiqing, Li Ning. Application of internet of vehicles based on edge computing evolved to 5G[J]. Telecommunications Science, 2019, 35(S2):169-175.
[10]	Zhang Y, Lan X, Ren J, et al. Efficient computing resource sharing for mobile edge-cloud computing networks[J]. IEEE/ACM Transactions on Networking, 2020, 28(3):1227-1240. doi: 10.1109/TNET.2020.2979807
[11]	赵海涛, 朱银阳, 丁仪, 等. 车联网中基于移动边缘计算的内容感知分类卸载算法研究[J]. 电子与信息学报, 2020, 42(1):20-27.
	Zhao Haitao, Zhu Yinyang, Ding Yi, et al. Research on content-aware classification offloading algorithm based on mobile edge calculation in the internet of vehicles[J]. Journal of Electronics and Information Technology, 2020, 42(1):20-27.
[12]	Chen M, Hao Y. Task offloading for mobile edge computing in software defined ultra-dense network[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(3):587-597. doi: 10.1109/JSAC.2018.2815360
[13]	夏桂梅, 曾建潮. 一种基于轮盘赌选择遗传算法的随机微粒群算法[J]. 计算机工程与科学, 2007, 29(6):51-54.
	Xia Guimei, Zeng Jianchao. A stochastic particle swarm optimization algorithm based on the genetic algorithm of roulette wheel selection[J]. Computer Engineering & Science, 2007, 29(6):51-54.
[14]	丁家会, 张兆军. 基于个体排序的自适应遗传算法[J]. 电子科技, 2020, 33(3):6-11.
	Ding Jiahui, Zhang Zhaojun. Adaptive genetic algorithm based on individual ordering[J]. Electronic Science and Technology, 2020, 33(3):6-11.
[15]	Fan Q, Ansari N. Application aware workload allocation for edge computing based IoT[J]. IEEE Internet of Things Journal, 2018, 5(3):2146-2153. doi: 10.1109/JIOT.2018.2826006
[16]	Yassine A, Singh S, Hossain M S, et al. IoT big data analytics for smart homes with fog and cloud computing[J]. Future Generation Computer Systems, 2019, 91(2):563-573. doi: 10.1016/j.future.2018.08.040
[17]	王鹏旭, 吕志伟. 北斗混合星座DOP值与定位性能分析[J]. 全球定位系统, 2016, 41(6):6-10.
	Wang Pengxu, Lü Zhiwei. Mixed Beidou constellation DOP value and positioning performance analysis[J]. GNSS World of China, 2016, 41(6):6-10.
[18]	殷佳, 管昕洁, 白光伟. 基于移动边缘计算的任务迁移和协作式负载均衡机制[J]. 计算机科学, 2019, 46(12):126-131. doi: 10.11896/jsjkx.181202453
	Yin Jia, Guan Xinjie, Bai Guangwei. Task offloading and cooperative load balancing mechanism based on mobile edge computing[J]. Computer Science, 2019, 46(12):126-131. doi: 10.11896/jsjkx.181202453
[19]	赵洪山, 田甜. 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术, 2014, 38(1):188-192.
	Zhao Hongshan, Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology, 2014, 38(1):188-192.
[20]	李燕, 鲁昌华, 张国强, 等. 自适应UKF在北斗伪距定位中的研究[J]. 电子测量与仪器学报, 2019, 33(2):125-131.
	Li Yan, Lu Changhua, Zhang Guoqiang, et al. Research on the Beidou pseudo ranges positioning based on adaptive UKF algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(2):125-131.
[21]	Wang L H, Teng H K, Hua Y G. Sensors access scheme design based on internet of things gateways[C]. Zhangjiajie: Proceedings of the Fifth International Conference on Intelligent Systems Design and Engineering Applications, 2014.
[22]	余翔, 刘一勋, 石雪琴, 等. 车联网场景下的移动边缘计算卸载策略[J]. 计算机工程, 2020, 46(11):29-34.
	Yu Xiang, Liu Yixun, Shi Xueqin, et al. Mobile edge computing offloading strategy under internet of vehicles scenario[J]. Computer Engineering, 2020, 46(11):29-34.

对比算法	进化代数
对比算法	50	100	150	200
常规遗传算法	0.889 4	0.875 6	0.885 5	0.980 3
改进遗传算法	0.972 5	0.973 2	0.982 4	0.999 5