Energy-Efficient Routing and Security Enhancements in Internet of Things Integrated Wireless Sensor Networks Using Reinforcement Learning

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : M. Rajasekar, S. P. Sasirekha

10.14445/23488549/IJECE-V12I11P120

How to Cite?

M. Rajasekar, S. P. Sasirekha, "Energy-Efficient Routing and Security Enhancements in Internet of Things Integrated Wireless Sensor Networks Using Reinforcement Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 11, pp. 240-251, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I11P120

Abstract:

The rapid advancement of Information and Communication Technology (ICT) has revolutionized daily life through the emergence of smart, connected systems across homes, healthcare, transportation, and urban environments. Wireless Sensor Networks (WSNs) form the foundation of Internet of Things (IoT) deployments, enabling real-time data exchange and intelligent automation. However, WSNs face significant challenges, including limited battery life, low processing power, and heightened vulnerability to security threats. These limitations reduce the operational lifespan of the network and compromise the reliability and safety of data transmission. Addressing these issues requires energy-efficient and secure routing protocols to maintain high performance under resource-constrained and threat-prone conditions. This study introduces Energy-Secure Reinforcement Learning for WSNs(ESRL-WSNs), an intelligent routing protocol that applies Reinforcement Learning (RL) to optimize path selection based on energy status, network dynamics, and security conditions. ESRL-WSNs employs a reward-driven approach to enable sensor nodes to learn optimal routes autonomously, adapting to changes in topology and resource availability. Traditional routing strategies-flat-based, hierarchical, and location-based—are evaluated and benchmarked against the ESRL-WSNs framework through comprehensive simulations across diverse network configurations and traffic loads. Experimental results indicate that ESRL-WSNs reduce energy consumption by 32% and improve data delivery rates by 27% compared to standard protocols. Additionally, the model exhibits robust adaptability and resilience in node failures and security threats. Integrating Reinforcement Learning into WSN routing offers a forward-looking solution for achieving sustainable, secure, and efficient communication in IoT-integrated systems.

Keywords:

Wireless Sensor Networks, Information and Communication Technology, Internet of Things, Energy-Efficient Routing, Reinforcement Learning, Smart Routing Protocols, Network Resilience, IoT Security.

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