Authors : Amruta Patil

Volume/Issue : Volume 11 - 2026, Issue 4 - April

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Google Scholar : https://tinyurl.com/2vcyjdxm

Scribd : https://tinyurl.com/yuh8z75a

DOI : https://doi.org/10.38124/ijisrt/26apr1839

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Wireless Sensor Networks (WSNs) are widely used in IoT-based applications but suffer from limited energy resources and dynamic network conditions. Traditional routing protocols and cost-based adaptive methods lack real-time learning capability [1]. This paper proposes a Hybrid Reinforcement Learning Clustering Routing Algorithm (HRL-CRA) that integrates clustering with Q-learning for intelligent routing. The proposed model selects cluster heads based on residual energy and node degree, while routing decisions are dynamically learned using reinforcement learning [2].

Keywords : Sensor Networks, Wireless Sensor Networks, Energy-Efficient Routing, Optimizing Algorithm, Clustering, Adaptive Routing, IoT Networks, Big Data, Optimisation, Network Optimisation.

References :

1. S. Priyadarshi et al., “AI-Based Energy Efficient Routing in WSN,” Nature Scientific Reports, 2025.
2. X. Zhang, Y. Liu, “Reinforcement Learning-Based Routing Optimization,” IEEE Access, 2026.
3. R. Singh et al., “Energy Optimization in Wireless Sensor Networks,” Elsevier Journal, 2024.
4. M. Lata, J. Kang, “Clustering-Based Routing in IoT Networks,” MDPI Electronics, 2025.
5. H. Song et al., “Deep Reinforcement Learning for Routing,” MDPI Electronics, 2024.
6. A. Chaudhari et al., “Q-Learning Based Routing Protocol,” IJSSIS, 2025.
7. W. Heinzelman et al., “Energy-Efficient Communication Protocols,” IEEE Transactions, 2002.
8. K. Sharma et al., “Energy Harvesting in WSN,” IEEE Sensors Journal, 2024.