A Biologically Inspired Low Energy Clustering Method for Large Scale Wireless Sensor Networks

Recently, the low energy clustering problem has been studied by numerous researchers and engineers. Such a problem is important in improving low energy consumption. In this paper, we propose an improved chaotic parallel monkey algorithm (ICPMA), a randomized swarm optimization algorithm for low energy clustering in large scale wireless sensor network, motivated by chaotic theory and parallel theory. We first establish a mathematical model for the low energy clustering problem. Based on the monkey algorithm, the new optimization method has many advantages due to blend both the chaotic theory as well as parallel theory. Simulations are conducted to compare and evaluate the energy efficiency of ICPMA with shuffled frog leaping algorithm (SFLA), artificial fish swarm algorithm (AFSA) as well as particle swarm optimization (PSO). In our experiments, we obtain that the novel ICPMA approach, when implemented into LSWSNs, is able to offer better performance and an improving energy efficiency compared to SFLA, AFSA and PSO approach.