Improving security and stability of ad hoc on-demand distance vector with fuzzy neural network in vehicular ad hoc network - Jiawei Mo; Baohua Huang; Xiaolu Cheng; Caixia Huang; Feng Wei (International Journal of Distributed Sensor Networks)
Stability and security are the key directions of VANET (vehicular ad hoc network) research. In order to solve the related problems in VANET, an improved AODV (ad hoc on-demand distance vector) routing protocol based on fuzzy neural network, namely, GSS-AODV (AODV with genetic simulated annealing, security and stability), is proposed. The improved scheme of the protocol analyzes the data in the movement process of the mobile node in VANET, extracts the parameters that affect the link stability, and uses the fuzzy neural network optimized by genetic simulated annealing to calculate the node stability. The improved scheme extracts the main parameters that affect the security of the nodes. After normalization, the fuzzy neural network based on genetic simulated annealing algorithm is used for fuzzy processing, and the node trust value of each node is evaluated. The improved scheme uses node stability and node trust value to control each routing process and dynamically adjusts parameters of the algorithm. The experimental results show that the improved scheme is stable and secure.