Assessing the Effectiveness of DL-Clustering for Energy Optimization in Wireless Sensor Networks

WSNs are extensively explored for their ability to collect and monitor data across a wide range of applications. However, the sensor nodes’ limited energy resources pose a substantial hurdle to prolonging the network’s longevity. To address this, we propose a Deep Learning-based Clustering Model Approach for optimizing energy utilization in WSNs. The DL-Clustering method uses sophisticated deep learning techniques, specifically RNN, to improve energy efficiency through effective cluster formation, CH selection, and CH maintenance. Our approach increases WSN lifespan and data transmission efficiency by using deep learning and intelligent grouping strategies. When compared to existing approaches such as LEACH, TCEER, TASRP, CARA, and SACC, DL-CM outperforms them in terms of energy efficiency. The results demonstrate the effectiveness of advanced deep-learning approaches in optimizing energy consumption and tackling the constraints faced by constrained energy supplies. This study highlights the ability of DL-Clustering to greatly increase energy optimization for WSNs, maximizing network potential and improving data transmission efficiency.