A Secure AODV Protocol Improvement Scheme Based on Fuzzy Neural Network
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.
KeywordsVehicular ad hoc networks Node security Fuzzy neural network AODV protocol
This work was supported by National Natural Science Foundation of China under Grant No. 61262072.
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