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.
The authors acknowledge the Indian Council of Medical Research (ICMR), Government of India, for supporting ongoing research activities related to secure and intelligent wearable healthcare monitoring systems under the project “Seizure-Sentry: Wearable Epilepsy Prediction Device for Enhanced Patient Safety through Timely Alerts and Personalized Seizure Management.” The present work shares methodological relevance in terms of adaptive secure communication, anomaly detection, and energy-efficient sensor network design. (Project ID: IIRPSG-2024-01-01197). The acknowledged ICMR project is referenced only to indicate the broader translational relevance of the proposed secure communication framework. No human participants, patient data, clinical samples, or experimental data from the ICMR project were used in this study.
Department of Electronics and Communication Engineering, Parul Institute of Engineering and Technology, Faculty of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India
Computer Science and Engineering, Lincoln University College, Petaling Jaya, Selangor Darul Ehsan, 47301, Malaysia
Lincoln University College, Petaling Jaya, Selangor Darul Ehsan, 47301, Malaysia
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology Chitkara University, Rajpura, 140401, Punjab, India
Department of Computer Science Engineering, Faculty of Computer Science & Engineering, I.T.S. Engineering College, Knowledge Park - III, Greater Noida, 201308, UP, India
Department of Electronics and Communication Engineering, Manipal University Jaipur, Jaipur-303007, Rajasthan, India
Department of Information Technology, National Engineering College, K.R.Nagar, Kovilpatti, 628503, Tamilnadu, India
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Correspondence to Ramji Gupta .
The authors declare no competing interests.
This study does not involve human participants, human tissue samples, animal experiments, or identifiable personal data. The proposed Adaptive Deep Reinforcement Learning–Based Secure Routing framework was developed and validated exclusively through computer-based simulations. Therefore, institutional ethical approval and informed consent were not required for this study.
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Gupta, R., Gupta, S.K., Singh, S.B. et al. Adaptive deep reinforcement learning–based secure routing for wireless sensor networks. Sci Rep (2026).
Received : 18 December 2025
Accepted : 13 July 2026
Published : 27 July 2026
DOI :
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