基于雾计算和自评估的VANET聚类与协作感知

doi:10.11896/jsjkx.200500154

计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 55-62.doi: 10.11896/jsjkx.200500154

所属专题：群智感知计算

基于雾计算和自评估的VANET聚类与协作感知

刘丹

大连东软信息学院物联网工程系辽宁大连116023

收稿日期:2020-05-29 修回日期:2020-07-21 出版日期:2020-10-15 发布日期:2020-10-16
通讯作者: 刘丹(liudan_j@neusoft.edu.cn)
基金资助:
国家自然科学基金(61772101);辽宁省自然科学基金(20180550021);大连市科技计划项目(2017RQ021)

Fog Computing and Self-assessment Based Clustering and Cooperative Perception for VANET

LIU Dan

Department of Internet of Things Engineering,Dalian Neusoft Information University,Dalian,Liaoning 116023,China

Received:2020-05-29 Revised:2020-07-21 Online:2020-10-15 Published:2020-10-16
About author:LIU Dan,born in 1982,master,asso-ciate professor.Her main researchinterests include Internet of vehicle,crowd sensing and Internet of Things data processing.
Supported by:
National Natural Science Foundation of China (61772101),Natural Science Foundation of Liaoning Province (20180550021) and Dalian Science and Technology Plan Project (2017RQ021)

摘要/Abstract

摘要： 集群是提高车联网群智感知质量和降低成本的有效方法,但如何在车辆高机动性的同时提高集群稳定性是一个具有挑战性的问题。基于VANET(Vehicular Ad-Hoc Network)的通信特点,文中提出了基于雾计算和自评估的VANET聚类算法FCSAC(Fog Computing and Self-Assessment Clustering),将VANET分为多个集群,集群内车辆协作感知结果由主簇头(Master Cluster Head,MCH)发给雾节点;引入车辆移动率(Velocity Mobility Rate,VMR)来改进簇头选举方法,该参数是根据移动性指标来计算的,以满足VANET动态变化的需求;通过定义缩放函数和加权机制来量化评估车辆的加入对集群稳定性的影响。同时,选举辅助群头(Slave Cluster Head,SCH)来增强集群的稳定性。其次,为提高拥堵区域感知的准确性,在雾计算的基础上通过主簇头间的有序链式协作交通态势感知,形成局部交通态势感知准确、全面的视图。最后,使用Veins车联网仿真平台评估所提算法的性能。结果表明,与CBRSDN(Cluster based Routing for Sparse and Dense Networks)算法和SACBR(Self-Assessment Cluster based Routing)算法相比,所提算法在集群稳定性方面表现优越,并且有效提高了VANET的吞吐量;与FCM(Fuzzy C-Means)算法相比,其交通分流能力更好,并减少了网络通信的消耗。

关键词: 车联网, 车载自组织网络, 集群, 群智感知, 雾计算, 协作感知

Abstract: Clustering is an effective method to improve the perception quality of Vehicular Crowd Sensing (VCS) and reduce costs.However,how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem.Based on the communication characteristics of VANET,a clustering algorithm based on Fog Computing and Self-Assessment (FCSAC) is proposed,which divides VANET into many clusters,and each cluster selects a Master Cluster Head(MCH) for data dissemination.The results of vehicle cooperative perception in the cluster are given to the fog nodes by MCH,the vehicle mobility rate (VMR) is introduced to improve Master Cluster Head(MCH) election method,this parameter is calculated based on mobility metrics to satisfy the need for VANET great mobility.Then,this paper evaluates the impact of vehicle joining on cluster stability by defining scaling functions and weighting mechanisms.FCSAC strengthens clusters’ stability through the election of a Slave Cluster Head (SCH) in addition to the MCH.In order to improve the accuracy,timeliness,and effectiveness of traffic information,on the basis of fog computing,via chain collaboration traffic perception between the MCH,an accurate and comprehensive view of the local traffic perception is formed.Finally,the Veins simulation platform is used to eva-luate the performance.The results show that,compared with the CBRSDN algorithm and SACBR algorithm,the proposed algorithm performs better in terms of cluster stability,and effectively improves the throughput of VANET.Compared with the Fuzzy C-Means (FCM) algorithm,it has better traffic diversion capability and reduces the consumption of network communication.

Key words: Clustering, Cooperative perception, Crowd sensing, Fog computing, Internet of vehicles, Vehicular-hoc networks

中图分类号:

TP393

刘丹. 基于雾计算和自评估的VANET聚类与协作感知[J]. 计算机科学, 2020, 47(10): 55-62. https://doi.org/10.11896/jsjkx.200500154

LIU Dan. Fog Computing and Self-assessment Based Clustering and Cooperative Perception for VANET[J]. Computer Science, 2020, 47(10): 55-62. https://doi.org/10.11896/jsjkx.200500154

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基于雾计算和自评估的VANET聚类与协作感知

Fog Computing and Self-assessment Based Clustering and Cooperative Perception for VANET

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