Multiple Metrics Fuzzy Based Clustering Algorithm for Wireless Mesh Client Networks
One of the variants of Wireless Mesh Networks (WMNs) architecture is Mesh Client Networks (MCNs). In the wireless network researches, little attention has been paid to MCNs to improve the quality of service of WMNs. One of the fundamental problems facing wireless network is scalability of routing protocol when the network size is grows which also apply to MCNs. Many researchers has suggested clustering approach to solve scalability problem, however, the approaches have not been able to come up with formidable approach for clusterhead selection that totally guarantee stable cluster structures and reduce clustering overhead at the same time. In this paper, we propose fuzzy logic control approach for the selection of clusterheads by using multiple metrics (MMFBCA). Our method fuzzifies three MCs metrics such as node mobility speed, traffic delivery capacity and cost of service. The simulation results show that stable cluster structures with minimized clustering overhead are generated. The results were compared with two existing weighted clustering algorithm (WCA) and AIMDCF using basic performance parameters such as clustering overheads, number of cluster, cluster size and reaffiliation counts for the evaluation. In the final analysis, MMFBCA performance results are better than WCA and AIMDCF in all scenarios tested.