Objectives: The major objective is enhancing the network lifetime and overall performance of the system and reducing the energy consumption of the nodes by using Improved Fuzzy Logic based Clustering (IFLC) algorithm.
Methods: In this paper, a Sugeno-type Fuzzy Inference based Clustering (SFIC) algorithm is proposed along with regression based threshold estimation for improving the network lifetime as well as reducing the energy consumption.
Findings: In wireless sensor networks (WSN), the sensor nodes are densely arranged in an aggressive environment for monitoring, detecting, and analyzing the physical phenomenon and large amount of energy is consumed during information transmission. Therefore, the replacement of battery is not possible regularly and maintaining the network lifetime is also difficult. Hence, these issues are the major challenges in WSN.
Applications/improvements: The experimental results show that the proposed sugeno-type fuzzy inference system based clustering algorithm has better performance than the other clustering algorithms.