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A Secure AODV Protocol Improvement Scheme Based on Fuzzy Neural Network

  • Tongyi Xie
  • Jiawei MoEmail author
  • Baohua Huang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 255)

Abstract

Aiming at the possible attacks of malicious nodes in VANET (Vehicle ad hoc network). It is very important to select security nodes in the routing protocols for routing activities. A secure AODV (Ad hoc On-demand Distance Vector Routing) improvement scheme is proposed, namely SGF-AODV (Security AODV with GASA-FNN). This algorithm uses fuzzy neural network to compute node information about routing activities and obtains the trust value of nodes to evaluate the security of nodes. The algorithm considers node security and network environment equally, defends against malicious node attack and balances node utilization rate. In the routing maintenance phase, the parameters of the fuzzy neural network are optimized in real time using the genetic simulated annealing algorithm for the actual environment to ensure that the calculated node trust value is in line with the actual situation. Experiments show that, SGF-AODV relative to AODV, the average delay, packet loss rate, routing overhead are improved.

Keywords

Vehicular ad hoc networks Node security Fuzzy neural network AODV protocol 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61262072.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Research DepartmentGuangxi Institute of EducationNanningChina
  2. 2.Department of Electrical and Computer EngineeringLushan College of Guangxi University of Science and TechnologyLiuzhouChina
  3. 3.School of Computer and Electronic InformationGuangxi UniversityNanningChina

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