REACH: A Reinforcement Learning-Based Protocol for Adaptive Cluster Head Selection in Wireless Sensor Networks

Novi Trisman Hadi; Supriyanto Supriyanto; I Wayan Rangga  Pinastawa; Radinal Setyadinsa

doi:10.52436/1.jutif.2025.6.5.4754

Authors

Novi Trisman Hadi Faculty of Computer Science, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Supriyanto Informatics Study Program, Universitas Sultan Ageng Tirtayasa, Indonesia
I Wayan Rangga Pinastawa Faculty of Computer Science, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Radinal Setyadinsa Faculty of Computer Science, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.4754

Keywords:

Adaptive Clustering, Energy Efficiency, Latency, Routing, REACH, WSN

Abstract

Wireless Sensor Networks (WSNs) are widely used in critical applications such as environmental monitoring and the Internet of Things (IoT), where energy efficiency and minimal latency are critical for network robustness and effectiveness. Conventional clustering and routing methods often struggle to adapt to fluctuating network conditions, resulting in suboptimal energy usage and increased latency. This study introduces REACH, an adaptive clustering and routing algorithm that leverages reinforcement learning to optimize energy consumption and reduce latency in WSNs. The proposed protocol dynamically selects cluster heads based on real-time network characteristics, including node density and energy levels, enhancing adaptability and robustness. Simulation results using MATLAB show significant improvements, with energy consumption reduced by 35% and latency reduced by 40% compared to traditional protocols such as LEACH and HEED. These findings suggest that reinforcement learning can significantly improve the performance of WSNs by extending the network lifetime and minimizing data transmission delay. This research contributes to the development of intelligent network protocols, offering practical insights into the integration of reinforcement learning for sustainable and scalable WSN design.

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