• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science

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An improved stable AODV protocol
scheme based on fuzzy neural networks

HUANG Baohua1,MO Jiawei2,L Qi1   

  1. (1.School of Computer and Electronic Information,Guangxi University,Nanning 530004;
    2.Department of Electrical and Computer Engineering,Lushan College,
    Guangxi University of Science and Technology,Liuzhou 545005,China)
  • Received:2017-08-16 Revised:2018-02-28 Online:2018-11-25 Published:2018-11-25

Abstract:

One of the important characteristics of vehicular ad hoc networks is the high mobility of nodes. It is significant to select stable links for transmitting data in routing protocols to solve the problem of frequent link breakage caused by free movement of nodes. To solve this problem, we propose an improved ondemand distance vector routing protocol (AODV) scheme with link stability, namely GFAODV (AODV with GASAFNN). The algorithm uses fuzzy neural networks to calculate the node information in the initialization and selection stages to get node stability. Then we evaluate the link quality, and select the stable link which has fewest hops based on link stability and hop count. In the routing maintenance phase, the parameters of the fuzzy neural network are optimized in real time according to the actual environment by using the genetic simulated annealing algorithm, which guarantees the consistency between calculated node stability and the actual situation. Experiments show that the GFAODV routing protocol outperforms the AODV in terms of average delay, packet delivery rate and routing overhead.

 

 

Key words: vehicular ad hoc networks, link stability, fuzzy neural network, AODV protocol