Enhancing the network lifetime of a wireless sensor network using modified k-means Firefly Optimization

Abstract

Developing technology including the Internet of Things, digital services, smart microgrids, and machine-to-machine systems encourage the implementation of self-configuring, automated systems in massive wireless sensor networks (WSNs). Effective Power utilization is critical in order to keep a network operational for the maximum possible time. Sensor nodes, which are small, battery-powered devices, are used in WSNs. As a result, one of the significant research issues in improving the lifespan of a WSN is resource management. A modified k-means (Mk-means) algorithm for clustering has been proposed to select Cluster Head (CH) to minimize energy usage of the nodes. Several optimization methods have been suggested in this area to extend the WSN lifetime. Following that, the Firefly Algorithm (FFA) is employed to generate the optimal routing through the CHs to a Base Station (BS), where multiple fitness functions such as residual energy, distance, and routing traffic are taken into account to optimize the FFA. As a result, information transmission among intermediate CHs in a hierarchy cluster-based design aids in lowering node energy usage. The proposed MKMFA (modified k-means Firefly Algorithm) technique’s performance is evaluated with K-means AODV (Ad-hoc On-demand Distance Vector) and IPC-KMAN(Improved Performance Clustering Using Modified K-Means Algorithm in Mobile Adhoc Networks) using network lifetime, throughput, Load Balancing Factor (LBF), and Packet Delivery Ratio (PDR). This work is based on recent advances in WSNs, which include application fields, design parameters, and lifetime prediction designs.