Utilization of Tabu Search Algorithm in IoT-Equipped WSN for Energy –Efficient Cluster Head Selection

Abstract

The Internet of Things (IoT) has brought transformative changes to various areas of human life, enabling the interconnection of sensor nodes to monitor and manage remote environments for applications like precision agriculture, wildlife conservation, and intelligent forestry. However, the limited battery life of these sensor nodes reduces network longevity, necessitating ongoing maintenance. To address this, energy conservation and provisioning are critical. Among these, clustering plays a vital role in maximizing energy efficiency and extending network lifespan. Despite various clustering methods proposed to enhance energy conservation, the improper selection of Cluster Heads (CH) often leads to an energy-hole issue, degrading network performance. CHs are pivotal in managing communication within clusters and eliminating duplicate data transmission, thereby optimizing energy use. To overcome this challenge, we propose a Tabu Search-based technique for energy-efficient CH selection in IoT-enabled wireless sensor networks(WSN). Our approach involves two phases: cluster formation using Euclidean distance and CH selection through Tabu Search. Simulated using Python, the proposed Tabu Search algorithm's performance is benchmarked against existing methods such as SWARAM, HSWO, EECHIGWO, and EECHS-ARO. Results show that our approach significantly improves network performance and energy efficiency.