Population-based Optimization Model for Energy-Efficient Cluster Formation in Wireless Sensor Networks

The preservation of the lifetime of a wireless sensor network (WSN) depends on the energy efficiency with which the network nodes operate as they collect and transmit data for network decisions. The importance of energy efficiency and conservation calls for the need for energy optimization solutions, among which is the clustering of network nodes to reduce energy consumption. This study aims to present a novel Sine Cosine Algorithm based clustering model (SCC) for optimizing the distances within the clusters to enhance energy preservation and lifetime elongation. The algorithm is used to implement the clustering solution through the steps of cluster formation, cluster head (CH) selection, and data transmission. The performance of the model in terms of network lifetime and the amount of residual energy is evaluated through a MATLAB simulation. The model is compared against other existing models such as Low-Energy Adaptive Cluster Hierarchy (LEACH), Hybrid Energy Efficient Distributed (HEED), and Multihop Balanced Clustering Routing Protocol (MBC). Results demonstrate a superior performance of SCC as compared to the other algorithms, demonstrating its impact on cluster formation and lifetime extension.