车联网环境下基于节点认知交互的路由算法

doi:10.11772/j.issn.1001-9081.2018061256

计算机应用 ›› 2019, Vol. 39 ›› Issue (2): 518-522.DOI: 10.11772/j.issn.1001-9081.2018061256

车联网环境下基于节点认知交互的路由算法

樊娜¹, 朱光源¹, 康军¹, 唐蕾¹, 朱依水¹, 王路阳¹, 段嘉欣²

1. 长安大学信息工程学院, 西安 710064;
2. 陕西城际铁路有限公司, 西安 710018

收稿日期:2018-06-19 修回日期:2018-08-20 出版日期:2019-02-10 发布日期:2019-02-15
通讯作者: 朱光源
作者简介:樊娜(1978-),女,陕西渭南人,副教授,博士,CCF会员,主要研究方向:无线传感器、网络安全;朱光源(1994-),男,江苏镇江人,硕士研究生,主要研究方向:交通信息控制;康军(1975-),男,陕西西安人,副教授,博士,主要研究方向:大数据、智能交通系统;唐蕾(1984-),女,四川江油人,副教授,博士,主要研究方向:交通信息服务计算;朱依水(1981-),女,陕西宝鸡人,讲师,博士,主要研究方向:服务计算、交通信息服务;王路阳(1988-),男,陕西西安人,讲师,博士,主要研究方向:交通信息服务;段嘉欣(1985-),女,陕西宝鸡人,主要研究方向:交通信息服务。
基金资助:
陕西省留学回国人员科技活动择优资助项目（2017023）；陕西省重点创新团队项目（2017KCT-29）；陕西省重点研发项目（2018GY-032）；中央高校基本科研业务费资助项目（310824171007）。

Routing algorithm based on node cognitive interaction in Internet of vehicles environment

FAN Na¹, ZHU Guangyuan¹, KANG Jun¹, TANG Lei¹, ZHU Yishui¹, WANG Luyang¹, DUAN Jiaxin²

1. School of Information Engineering, Chang'an University, Xi'an Shaanxi 710064, China;
2. Shaanxi Intercity Railway Company Limited, Xi'an Shaanxi 710018, China

Received:2018-06-19 Revised:2018-08-20 Online:2019-02-10 Published:2019-02-15
Supported by:
This work is partially supported by the Selected Science and Technology Project of Oversea Scholars from Shaanxi Province (2017023), the Key Innovation Team Project of Shaanxi Province (2017KCT-29), the Key Research and Development Project of Shaanxi Province (2018GY-032), the Fundamental Research Funds for the Central Universities (310824171007).

摘要/Abstract

摘要： 针对车联网（IoV）环境下消息传输效率低下、网络资源开销较大等诸多问题，提出一种适用于城市交通场景下基于车辆节点认知交互的路由算法。首先，依据信任理论提出节点认知交互度的概念，并在此基础上对车联网中的车辆节点进行分类，赋予它们不同的认知交互度初值；同时还引入车辆节点交互时间、交互频率、车辆节点物理间隔距离、间隔跳数以及消息生存时间等影响因子，进而构建了车辆节点认知交互评估模型。基于该模型计算并更新节点的认知交互度，并通过比较对应车辆节点间的认知交互度值来选取认知交互度相对较高的邻居节点作为中继节点进行消息转发。仿真实验结果表明，与Epidemic和Prophet路由算法相比，所提路由算法有效提高了消息投递率并降低了消息投递时延，同时显著降低了网络资源的开销，有助于提升车联网环境的消息传输质量。

关键词: 车联网, 延迟容忍网络, 路由算法, 消息转发

Abstract: In order to solve the problems such as low transmission efficiency and high network resource overhead in Internet of Vehicles (IoV) environment, a new routing algorithm based on node cognitive interaction, which is suitable for urban traffic environment, was proposed. Firstly, based on trust theory, a concept of cognitive interaction degree was proposed. Then, based on this, the vehicle nodes in IoV were classified and given with different initial values of cognitive interaction degree. Meanwhile, the influence factors such as interaction time, interaction frequency, physical distance, hops between nodes and the Time-To-Live of message were introduced, and a cognitive interaction evaluation model of vehicle nodes was constructed. The cognitive interaction degrees of vehicle nodes were calculated and updated by using the proposed model, and a neighbor node with higher cognitive interaction degree than others could be selected as relay node to forward the messages after the comparison between the nodes. Simulation results show that compared with Epidemic and Prophet routing algorithms, the proposed algorithm effectively increases the message delivery rate and reduces the message delivery delay, while significantly reducing the overhead of network resources and helping to improve the quality of message transmission in IoV environment

Key words: Internet of Vehicles (IoV), Delay Tolerant Network (DTN), routing algorithm, message forwarding

中图分类号:

U491

樊娜, 朱光源, 康军, 唐蕾, 朱依水, 王路阳, 段嘉欣. 车联网环境下基于节点认知交互的路由算法[J]. 计算机应用, 2019, 39(2): 518-522.

FAN Na, ZHU Guangyuan, KANG Jun, TANG Lei, ZHU Yishui, WANG Luyang, DUAN Jiaxin. Routing algorithm based on node cognitive interaction in Internet of vehicles environment[J]. Journal of Computer Applications, 2019, 39(2): 518-522.

参考文献

[1] FLOREA A R, COSTEA I M. Analisys of improvements the urban transport conditions by using electronic intelligent transports systems case study:urban transportation[C]//Proceedings of the 2014 International Conference on Electronics, Computers and Artificial Intelligence. Piscataway, NJ:IEEE, 2014:73-78.
[2] WANG T, CAO Y, ZHOU Y, et al. A survey on geographic routing protocols in Delay/Disruption Tolerant Networks (DTNs)[J]. International Journal of Distributed Sensor Networks, 2016(6):3174670.
[3] DOERING M, PÖGEL T, WOLF L. DTN routing in urban public transport systems[C]//CHANTS'10:Proceedings of the 5th ACM Workshop on Challenged Networks. New York:ACM, 2010:55-62.
[4] FALL K, FARRELL S. DTN:an architectural retrospective[J]. IEEE Journal on Selected Areas in Communications, 2008, 26(5):828-836.
[5] VOYIATZIS A G. A survey of delay- and disruption-tolerant networking applications[J]. Journal of Internet Engineering, 2012, 5(1):331-344.
[6] WU J, YANG S, DAI F. Logarithmic store-carry-forward routing in mobile Ad Hoc networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2007, 18(6):735-748.
[7] BENAMAR M, AHNANA S, SAIYARI F Z, et al. Study of VDTN routing protocols performances in sparse and dense traffic in the presence of relay nodes[J]. Journal of Mobile Multimedia, 2014, 10(1/2):78-93.
[8] VAHDAT A. Epidemic routing for partially-connected Ad Hoc networks, Handbook of Systemic Autoimmune Diseases[R]. Durham:Duke University, Department of Computer Science, 2000.
[9] TORNELL S M, CALAFATE C T, CANO J-C, et al. DTN protocols for vehicular networks:an application oriented overview[J]. IEEE Communications Surveys & Tutorials, 2017, 17(2):868-887.
[10] SPYROPOULOS T, PSOUNIS K, RAGHAVENDRA C S. Spray and wait:an efficient routing scheme for intermittently connected mobile networks[C]//WDTN'05:Proceedings of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking. New York:ACM, 2005:252-259.
[11] AL-HINAI A, ZHANG H, CHEN Y, et al. TB-SnW:trust-based spray-and-wait routing for delay-tolerant networks[J]. Journal of Supercomputing, 2014, 69(2):593-609.
[12] LINDGREN A, DORIA A, SCHELéN O. Probabilistic routing in intermittently connected networks[C]//SAPIR 2004:Proceedings of the 2004 International Workshop on Service Assurance with Partial and Intermittent Resources. Berlin:Springer-Verlag, 2004:239-254.
[13] QIN S, FENG G, ZHANG Y. How the contact-probing mechanism affects the transmission capacity of delay-tolerant networks[J]. IEEE Transactions on Vehicular Technology, 2011, 60(4):1825-1834.
[14] SHINKO I, ODA T, SPAHO E, et al. A simulation system based on ONE and SUMO simulators:performance evaluation of First Contact, Prophet and Spray-and-Wait DTN protocols[C]//BWCCA'15:Proceedings of the 201510th International Conference on Broadband and Wireless Computing, Communication and Applications. Washington, DC:IEEE Computer Society, 2015:137-142.
[15] LIM H H, DONG Y S, YUN W C. An improved PRoPHET routing protocol through the restriction of message duplication[J]. Journal of Korean Institute of Information Technology, 2016, 14(6):81.
[16] GAITO S, MAGGIORINI D, ROSSI G P, et al. Bus switched networks:An Ad Hoc mobile platform enabling urban-wide communications[J]. Ad Hoc Networks, 2012, 10(6):931-945.

车联网环境下基于节点认知交互的路由算法

Routing algorithm based on node cognitive interaction in Internet of vehicles environment

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