CABMD: A new clustering algorithm based on membership degree of node for mobile ad hoc networks
Seyed Mehdi Moosavi -, Marjan Kuchaki Rafsanjani, Zabihallah Pargam · Journal of Information and Optimization Sciences · 2018
In this paper, we propose clustering algorithm based on the membership degree of node for mobile ad hoc networks (CABMD). The aim of CABMD focuses on cluster-head election and cluster formation. The cluster - head election is based on quality of node that is calculated by a combination of characteristics such as connectivity, energy and mobility of nodes. The node has the best quality among all of the nodes is elected as first cluster-head and forms its cluster based on the membership degree of the node that is related to its quality and distance between the cluster-head and node. This procedure follows for election of next cluster-heads and formation of their clusters until all of the nodes in the network are elected as cluster-heads or cluster-members. This study uses the characteristics of CEMCA algorithm and tries to improve its performance by a new method. We apply membership degree for clustering, so that the performance of clusters in the network will be improved. We have simulated our algorithm (CABMD) by NS-2 in order to measure the performance of it. Our results will be compared with the Weighted Clustering Algorithm (WCA), connectivity, energy and mobility driven weighted clustering algorithm (CEMCA) and connectivity, residual battery power, average mobility, and distance algorithm (CBMD).