Presenting a New Clustering Algorithm by Combining Intelligent Bat and Chaotic Map Algorithms to Improve Energy Consumption in Wireless Sensor Network

One of the major challenges that wireless sensor networks face is the limited energy of nodes which reduces network’s life time. Clustering is a popular approach to overcome this problem. Also, it is a particular energy efficient mechanism within large scale wireless sensor networks. Most problems of the computer systems like wireless sensor network could not be solved by linear solutions and there is not any deterministic solution for most NP-hard problems and the result of such problems is always optimizing. To solve these problems, applying evolutionary algorithms is recommended. The bat algorithm could find the shortest path between member nodes of the cluster and cluster head. In this paper, to reduce the energy consumption in wireless sensor nodes and also select the suitable cluster heads, the capabilities of combining the evolutionary bat algorithm and chaotic map is used. Applying chaotic map instead of some particular and random parameters in the bat algorithm improves the clustering. The results obtained from the implementation of the proposed method in MATLAB and their comparison with the existing methods such as GA, GAPSO, LEACH and LEACH-T represent significant impact in energy consumption improvement, network lifetime increase and also the number of live nodes increase within different rounds of algorithm execution.