(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Clustering in VANET is important for recent days due to more number of vehicle present in urban area. In future Intelligent Transportation System (ITS), VANET is capable of providing safety related applications, Internet accessing and various user applications for drivers and passengers. Clustering based topology an efficient data interaction among vehicles, can be applied to the groups of vehicle nodes in geographical vicinity together that supports direct interaction between the intercluster data interaction through cluster heads (CHs). In existing method, so many algorithms developed Related to clustering based VANET communication, but due to high mobility of vehicle and their parameters like position, velocity and acceleration is improve the complexity and reduce the performance. In this research, we propose an adaptive self-learning approach based hybrid clustering. In hybrid clustering, the process divided in three steps. In first step Type1, it used for Road side unit (RSU) act as a static cluster head. In second step, divide the multiple zones with whole area and apply zone sensitive based clustering algorithm. It is used for predict the efficient centroid/weight value for each Vehicles. Finally in the third step, from the above algorithm, in neuro- fuzzy training samples we got. Neuro fuzzy prediction based cluster head selection was applied. Percentages of cluster-Head for each zone assign the purpose of reduces the end-to-end delay and reduce congestion. In simulation results shows that demonstrate the efficiency of proposed algorithm compare to existing method.