摘要
针对移动物(车)联网的路由问题,通过对车辆的运动特点及造成链路断裂的原因进行的详细分析,我们建立了链路维持时间模型,并将维持时间作为设计路由算法的重要参数. Q-Learning作为一种启发式机器学习策略,能够通过与周围环境交互来动态地调整路由路径.基于此,我们设计了一种自适应的路由新算法.它将学习任务分散在每一个车辆节点中,通过周期性的与周围节点交换信标信息来维护可靠的路由路径.利用NS-2模拟器对该算法的性能进行了评估,结果表明,在不同的网络场景中,该算法在递交率、端到端的延时以及平均跳数等方面均表现出很好的效果.
In order to solve the routing problem of mobile IOT(IOV),based on our analyzing the details about motion characteristics of the vehicle and the reasons that cause links down,we set up link model of the duration time and using the duration time as key parameter to design the new routing method.Q-Learning as a kind of heuristic machine learning strategy is able to dynamically adjust the routing path through interaction with the surrounding environment.So a kind of new routing algorithm with adaptivity for mobile IOT based on Q-learning has been presented in this paper.It distributes the learning task into each vehicle node and maintains the reliable routing path by continuously exchanging the beacon information with the neighbor nodes.With the NS-2 simulator,the performance of the algorithm is tested.The results show that it has better performances on delivery,end-to-end delay and average hops in many mobile applications.
作者
张德干
葛辉
刘晓欢
张晓丹
李文斌
ZHANG De-gan;GE Hui;LIU Xiao-huan;ZHANG Xiao-dan;LI Wen-bin(Tianjin Key Lab of Intelligent Computing&Novel Software Technology,Tianjin University of Technology,Tianjin 300384,China;Key Laboratory of Computer Vision and System(Tianjin University of Technology),Ministry of Education, Tianjin University of Technology,Tianjin 300384,China;Institute of Institute of Scientific and Technical Information of China,Beijing 100038,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2018年第10期2325-2332,共8页
Acta Electronica Sinica
基金
国家自然科学基金(No.61571328)
天津市重大科技专项(No.15ZXDSGX00050
No.16ZXFWGX00010)
天津市科技支撑重点项目(No.17YFZCGX00360)
天津市自然科学基金(No.15JCYBJC46500)
天津市科技创新和131人才团队(No.TD12-5016
2015-23
No.TD13-5025)
关键词
机器学习
移动物联网
拓扑
动态
路由
machine learning
mobile IOT
Topology
dynamic
routing
