Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks

Abstract

PurposeThis paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime.Design/methodology/approachThis work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm.FindingsSimulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime.Research limitations/implicationsIn this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only.Practical implicationsThe main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations.Social implicationsThe research highly gains impacts toward mobile-based applications.Originality/valueA new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.