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JCM 2025 Vol.20(4): 457-474
Doi: 10.12720/jcm.20.4.457-474

Dynamic Slot Allocation in Wireless Body Area Networks: Exploring Q-learning Approaches

Abdu I. Adamu1, Darmawaty M. Ali1,*, Saidatul Izyanie B. Kamarudin2, Suzi S. Sarnin1, Wan Haszerila W. Hassan1, Mansir Abubakar2, and Alwatben B. Rashed3
1Wireless Communication Technology Group (WiCOT), Faculty of Electrical Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Selangor, Malaysia
2Faculty of Computer Science and Mathematics, Universiti Teknologi MARA (UiTM), Selangor, Mal
3Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
Email: 2023402878@student.uitm.edu.my (A.I.A.); darma504@uitm.edu.my (D.M.A.); saidatulizyanie@uitm.edu.my (S.I.B.K.); suzis045@uitm.edu.my (S.S.S.); 2022603854@student.uitm.edu.my (W.H.W.H.); mansir@uitm.edu.my (M.A.); batoolawtban@gmail.com (A.B.R.)
*Corresponding author

Manuscript received March 12, 2025; revised April 20, 2025, accepted May 12, 2025; published August 1, 2025.

Abstract—Real-time monitoring through wearable and implanted devices is made possible by Wireless Body Area Networks (WBANs), which have emerged as a key component of modern healthcare. These networks provide substantial advantages for patient treatment by enabling ongoing health data collection. The requirement for fast throughput, low packet delay, Packet Delivery Ratio (PDR), and energy efficiency under dynamic network conditions makes creating a Medium Access Control (MAC) protocol crucial. A Q-Learning-Based MAC Protocol (QL-MAC) designed for slot allocation in WBANs is proposed in this paper. QL-MAC improves network performance across important metrics by dynamically optimizing slot allocation using Reinforcement Learning (RL). By adjusting to different network densities and traffic patterns, the protocol guarantees steady gains in communication. QL-MAC Outperforms Adaptive MAC (ADT-MAC), Dynamic Medical Traffic Management MAC (DMTM-MAC), Traffic Aware MAC (TA-MAC), Multi-Constraints MAC (McMAC), and IEEE 802.15.6 MAC protocols. Experimental results show that QL-MAC achieves higher throughput, reduces latency, maintains a better PDR, and has lower energy consumption, even as network density increases. The benefits of QL-MAC make it especially appropriate for applications where reliable communication and energy efficiency are critical, like chronic disease management and remote patient monitoring. This study also reaffirms the role of machine learning in optimizing communication protocols for next-generation healthcare systems. The results highlight the potential of RL-based approaches to address the unique challenges of WBANs, such as dynamic channel conditions and resource contention. QL-MAC ensures dependable and energy-efficient communication by intelligently managing slot allocation, opening the way for advanced healthcare applications.


Keywords—Wireless Body Area Networks (WBANs), medium access control, slot allocation, modern healthcare, energy efficiency, reinforcement learning, Q-learning

Cite: Abdu I. Adamu, Darmawaty M. Ali, Saidatul Izyanie B. Kamarudin, Suzi S. Sarnin, Wan Haszerila W. Hassan, Mansir Abubakar, and Alwatben B. Rashed, “Dynamic Slot Allocation in Wireless Body Area Networks: Exploring Q-learning Approaches," Journal of Communications, vol. 20, no. 4, pp. 457-474, 2025.



Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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