Security Augmentation for Deep Reinforcement Learning - Drivenv Strategies in Energy-Efficient Wireless Sensor Networks

Abstract

Owing to the inherent transmission nature of wireless communication and the partial battery capacity of sensor nodes, achieving both energy efficiency and secure communication remains a major challenge in Wireless Sensor Networks (WSNs). Consequently, considerable research attention has been directed toward developing intelligent solutions that can simultaneously enhance network security and reduce energy consumption. To overcome these challenges, this study introduces a novel Deep Reinforcement Learning (DRL) based framework, termed DeepNR, to enhance both security and energy efficiency in Wireless Sensor Networks (WSNs). The proposed DeepNR model utilizes a Deep Neural Network (DNN) to dynamically acquire network state data and effectively estimate the Q-value function. Through continuous monitoring and analysis of network conditions, the framework is capable of making intelligent and adaptive decisions for efficient network operation and management. Moreover, the proposed framework employs a DRL-based multi-tier decision-making approach to dynamically optimize data transmission routes in real time. This adaptive strategy enables accurate network state prediction and efficient routing decisions, resulting in improved communication performance and resource utilization. In addition, DeepNR integrates a real-time defense mechanism capable of responding to detected threats while maintaining normal network operations, thereby significantly strengthening overall network security.

The framework further integrates deep learning techniques to dynamically adjust to varying network conditions and emerging cyber threats. Experimental results indicate that the proposed DeepNR approach surpasses traditional methods, delivering nearly 25% higher network throughput, 20% better security efficiency and approximately 30% longer network lifetime.