Dynamic slot allocation in wireless body area networks: Exploring Q-Learning approaches
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 re...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Journal Of Communications
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29552/2/023650309202511816.pdf http://eprints.utem.edu.my/id/eprint/29552/ https://www.jocm.us/2025/JCM-V20N4-457.pdf |
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| Summary: | 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 nextgeneration 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. |
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