An Energy Efficient Stable Clustering Approach Using Fuzzy Type-2 Neural Network Optimization Algorithm For Wireless Sensor Networks

摘要

The advancement in communication and sensor technologies makes it possible to develop low-cost circuitry networks to sense and transmit the state of surroundings called Wireless Sensor Networks (WSNs). However, due to their severe computation and energy constraints, WSN's have unique design challenges. Energy-efficient cluster-based routing and security are still receiving immense interest from the researchers today. The most appropriate approach to prolonging WSN's performance parameters is clustering. Effective CH selection algorithm and optimized routing protocol are important for designing efficient solutions for WSNs to overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime. In this paper, a Neural Network optimization algorithm using a fuzzy type-2 based clustering approach is proposed to extend the lifetime of the network. The proposed approach has been tested in terms of the number of alive nodes per round, network lifetime, and stability period of the network. Simulation outcomes show that the proposed protocol outperforms competitive clustering algorithms in terms of energy consumption, stability duration and lifetime of the network.