A new fuzzy based clustering algorithm for wireless mobile Ad-Hoc sensor networks

Clustering is a significant mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on criteria like battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without disturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than other existing mechanisms.