Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Abstract

Wireless sensor networks depend on the effective functioning of the nodes in the network, which is concerned regarding the energy that is essential for the extended network life-time. Clustering plays a major role in enabling energy efficiency, which extends the life-time of the network. Thus, the paper introduces a cluster head (CH) selection phenomenon based on the algorithm, Taylor kernel fuzzy C-means (Taylor KFCM), which is the modification of the kernel-based fuzzy c-means (KFCM) algorithm in the Taylor series. The developed algorithm chooses the cluster head using the selection phenomenon, acceptability factor, which is computed using the energy, distance, and trust. In other words, a node acts as a CH when the fitness constraints of minimal distance, maximal trust, and maximal energy are attained. The simulation environment is established using 50, 100, and 200 nodes with 5 and 10 CHs and the effectiveness of the proposed CH selection is revealed through the analysis depending on the metrics, throughput, energy, delay, and the number of alive nodes. The proposed Taylor kernel fuzzy C-means acquired a maximal throughput, energy, and alive nodes of 0.2857, 0.0947, and 31, and minimal delay and routing overhead of 0.1219, 0.0418 respectively.