Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning
Sandeep Raskar, Gulshan Dhasmana, M. Lakshminarayana, Harshal Patil, Bhagya Shree S, L. Natrayan
Abstract
Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations.