Original scientific paper
https://doi.org/10.24138/jcomss-2025-0154
Optimised Q-learning for Dynamic Slot Assignment in Medium Access Control Protocol for Wireless Body Area Networks
Abdu Ibrahim Adamu
orcid.org/0009-0003-8794-1435
; Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
*
Wan Haszerila Wan Hassan
; Universiti Teknikal Malaysia Melaka, Malaysia
Darmawaty Mohd Ali
; Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
Wan Norsyafizan Wan Muhamad
; Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
Alwatben Batoul Rashed
; Qassim University, Buraydah, Saudi Arabia
Mansir Abubakar
; Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
* Corresponding author.
Abstract
Wireless Body Area Networks (WBANs) enable
continuous health monitoring through implanted and wearable
sensors, but their performance hinges on an efficient Medium
Access Control (MAC) scheme. Conventional protocols struggle
to balance throughput, latency, and energy use, key requirements
for medical data delivery. This paper introduces QLDSA-MAC,
a Q-learning-driven dynamic slot-allocation MAC protocol that
continuously adapts time slots to current traffic conditions. The
agent maintains a Q-table of state–action values and selects
slot assignments that maximize a composite reward reflecting
throughput, delay, and energy consumption. Extensive simulations
compare QLDSA-MAC with Time Division Multiple Access
(TDMA-MAC), Concurrent MAC (C-MAC), and the IEEE
802.15.6 standard. Results show that QLDSA-MAC consistently
delivers the highest throughput and the lowest packet delay across
a range of traffic loads. It also reduces energy consumption,
extending node lifetime in power-constrained scenarios. These
gains demonstrate that reinforcement-learning (RL) methods
can address WBAN challenges more effectively than fixed-rule
MAC designs. Overall, QLDSA-MAC offers a practical path
toward reliable, low-latency, and energy-efficient communication
in healthcare WBAN deployments.
Keywords
WBANs; Medium Access Control; Dynamic Slot Allocation; Reinforcement Learning; QLDSA-MAC
Hrčak ID:
348529
URI
Publication date:
31.3.2026.
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