Adaptive deep reinforcement learning–based secure routing for wireless sensor networks

Wireless Sensor Networks (WSNs) play an important role in mission-critical and resource-constrained environments by enabling reliable, real-time monitoring, communication, and intelligent decision-making across dynamic network conditions. Nevertheless, these networks suffers unique security challenges due to hostile deployment environments, resource-constrained nodes, and susceptibility to several cyber-attacks. This paper presents a state of art approach combining Deep Q-Network (DQN) based reinforcement learning with cryptographic security mechanisms to develop an adaptive and secure routing protocol for military wireless sensor networks. The proposed framework addresses key challenges, embracing energy efficiency, intrusion detection, and resilience against sinkhole, denial-of-service, and traffic-analysis attacks. Our hybrid framework combines trust management, lightweight cryptographic techniques, and adaptive routing strategies to elongated network lifetime while ensuring strong security. Simulation outcomes depict that the proposed protocol delivers nearly a 20% enhancement in routing efficiency and achieves 99.46% accuracy in intrusion detection, outperforming conventional methods.