Fuzzy Logic based Cluster Head selection by enhancing Aggregated Relative Velocity in VANET

Abstract

Vehicular Ad hoc Network is one of major area in the technology of Mobile Ad hoc Network. Making a vehicular network more robust and scalable is one of main issues in Vehicular Ad hoc Network. Since vehicles are high amount of mobility and unpredictable situation, it’s a major issue to manage vehicles in the certain network. So we have to find a way to communicate in stable and robust method of cluster head selection. Clustering is of crucial significance in order to cope with the dynamic features of the Vehicular Ad hoc Network topologies. Dividing in different cluster and assigning one head for certain number of cluster member is the method of the whole process. The challenge we face is how to select the reliable and efficient vehicle for to be a cluster head. And what factors to use in order to differentiate the best node among others. The prominent parameters speed, acceleration, distance, aggregated relative velocity and direction information are taken into account as inputs of the proposed cluster head selection algorithm. Those inputs will be fuzzified by the fuzzy logic by depending on the rule initiated we can give them the value to be a head or a member. Fuzzy logic can handle multi-valued truth by using membership function which will measure the degree of inputs. This mechanism supports in effectively way to get and assign the cluster head. In general, the finding in this research is better decision making system than recent works. By tolerating the instability of vehicular ad hoc network behavior. Recent works in such problem suggested fuzzy logic base decision making means. But we also enhance the parameter of the inputs to our fuzzy inference engine which is called aggregated relative velocity. Using MATLAB software we illustrate what have been done. SIMULINK is the package that facilitates the simulation method