Deep reinforcement learning based neurosense scheme for energy-efficient and secure wireless sensor networks

One of the main challenges facing Wireless Sensor Networks (WSN) is how to achieve energy efficiency while ensuring security. To address this issue, this paper proposes a strategy called NeuroSense, which combines deep reinforcement learning (DRL) techniques to achieve an optimal trade-off between energy efficiency and security for WSNs. Specifically, the core idea of NeuroSense is to learn and optimize data transmission paths in real time using DRL models. By intelligently selecting data transmission paths, the proposed NeuroSense strategy improves energy-aware routing decisions and helps reduce uneven energy depletion across nodes, thereby contributing to longer network operation. In addition, NeuroSense incorporates security-related state information into the DRL decision process and adjusts routing and protection decisions under the considered attack scenarios. Experimental results in simulation show that the proposed NeuroSense strategy achieves better performance than five baseline strategies in total energy consumption, communication delay, and resilience-related evaluation outcomes under the tested settings.