Enhancing Security in Energy-efficient Wireless Sensor Networks Using Deep Learning

Abstract

Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. The transmission nature of wireless communiqué and the scarcity of energy supplies make energy-efficacy and security major considerations in WSNs. Consequently, there has been a lot of focus on how to make WSNs more energy efficient while simultaneously making them more secure. To address this issue, this study presents a novel approach to improving WSN security and energy efficiency—the DeepNR strategy—based on deep reinforcement learning (DRL). To be more precise, the DeepNR approach suggests building a deep-neuralnetwork( DNN) to adaptively learn the state information in order to approximate the Q-value. Additionally, it accomplishes accurate network prediction and decision-making by designing DRLbased multi-level decision-making to learn and optimize data communication channels in realtime. As network conditions and attack patterns evolve, DeepNR modifies its approach accordingly using deep learning models. By increasing network data speed by 25%, network lifespan by 30%, and security measures by 20%, experimental results reveal that the suggested DeepNR exceeds the traditional techniques.