Energy-Efficient Distributed Improved Greedy Clustering In WSN
S. Divyabharathi, S. Veni · 2023
Since they can independently gather data from their surroundings, Wireless Sensor Networks (WSNs) have garnered a lot of interest in many different contexts. However, the sensor nodes' limited energy resources provide a significant obstacle, making energy economy a top priority in the design and operation of WSNs. By grouping sensor nodes into clusters and facilitating efficient data collection and transmission, clustering methods have emerged as useful solutions to extend the lifespan of the network. In this context, this research proposes an innovative approach called Energy-Efficient Distributed Improved Greedy Clustering (EEDIGC) for WSNs. The EEDIGC algorithm integrates the principles of both Greedy clustering and Distributed clustering algorithms to optimize energy consumption. Using a decentralized and centralized protocol, sensor nodes in the proposed technique cluster together depending on their remaining battery life and their distance from the cluster leaders. By using the Improved Greedy method, we may reduce the amount of power needed for cluster creation while still achieving optimum cluster size distributions and leader selection. Moreover, EEDIGC incorporates data aggregation and adaptive clustering techniques, enhancing the network's overall energy efficiency. In terms of energy economy, network longevity, and data transmission reliability, simulation findings show that EEDIGC is superior to other clustering methods. The algorithm's distributed nature reduces the overhead associated with centralized control, making it suitable for large-scale WSN deployments. Furthermore, EEDIGC adapts to dynamic network conditions, ensuring robust performance even in challenging environments.