Multi-layer Clustering Topology Design in Densely Deployed Wireless Sensor Network using Evolutionary Algorithms

Due to the resource constraint and dynamic parameters, reducing energy consumption has become the most important issue of the wireless sensor network (WSN) topology design. All the proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters, which may lead to reduction in efficiency and performance. In fact, in the WSN topology, increasing a cluster layer is a trade-off between time complexity and energy efficiency. In this work, regarding the most important WSN design parameters, a novel dynamic multi-layer hierarchy clustering approach is proposed using evolutionary algorithms for densely deployed WSNs. Different evolutionary algorithms such as genetic algorithm, imperialist competitive algorithm, and Particle Swarm Optimization (PSO) are used to find an efficient evolutionary algorithm for implementation of the proposed clustering method. The results obtained demonstrate the PSO performance, which is more efficient compared to the other algorithms in order to provide a maximum network coverage, an efficient cluster formation, and a network traffic reduction. The simulation results of the multi-layer WSN clustering design through PSO algorithm show that this novel approach reduces the energy communication significantly and increases the lifetime of network up to 2.29 times with providing full network coverage (100%) till 350 rounds (56% of network lifetime) compared to the WEEC and LEACH-ICA clustering.