A novel evolutionary approach for load balanced clustering problem for wireless sensor networks

• The proposed work is a novel GA based load balanced clustering scheme for WSNs.
• The algorithm works for both equal and unequal load of the sensor nodes.
• Initial population generation and mutation are very strategic to make the algorithm faster.
• Experimental results demonstrate the superiority over existing algorithms.

Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximizing lifetime of Wireless Sensor Networks (WSNs). However, in a cluster based WSN, the leaders (cluster heads) bear some extra load for various activities such as data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. Load balanced clustering is known to be an NP-hard problem for a WSN with unequal load of the sensor nodes. Genetic Algorithm (GA) is one of the most popular evolutionary approach that can be applied for finding the fast and efficient solution of such problem. In this paper, we propose a novel GA based load balanced clustering algorithm for WSN. The proposed algorithm is shown to perform well for both equal as well as unequal load of the sensor nodes. We perform extensive simulation of the proposed method and compare the results with some evolutionary based approaches and other related clustering algorithms. The results demonstrate that the proposed algorithm performs better than all such algorithms in terms of various performance metrics such as load balancing, execution time, energy consumption, number of active sensor nodes, number of active cluster heads and the rate of convergence.