Design and Implementation of a Hybrid Genetic Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks

A Heterogeneous Wireless Sensor Network (HWSN) consists of sensor nodes with varying characteristics, particularly in communication ranges and energy resources. This diversity allows for a more flexible and efficient network architecture, tailored to meet the specific demands of different applications. Clustering which categorizes nodes as Cluster Heads (CHs) and members, is a crucial method to provide energy-efficient data transmission for HWSNs. Chosen CHs (cluster leaders) are responsible for data aggregation and backbone routing; hence CH selection is of utmost importance. In this study, we propose a Hybrid Genetic Algorithm (HGA) to solve Minimum Dominating Set (MDS) based CH selection problem. HGA explores the solution space through mutation, selection, and crossover, while local deterministic improvements enhance quality. The main objective of the proposed approach is to minimize the number of nodes selected as CHs while all nodes are connected to at least one leader to meet MDS constraint. Additionally, another parameters such as energy constraints, communication coverage and intra-cluster distance are considered for HWSNs. Through extensive simulation studies, we reveal that our proposed HGA performs well for various setups.