Markov Decision Process Based Energy Aware MAC Protocol for IoT WBAN Systems

Damilola D. Olatinwo*, Adnan M. Abu-Mahfouz, Gerhard P. Hancke, Hermanus C. Myburgh

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)

Abstract

Internet of Things (IoT) enabled WBAN systems play a crucial role in healthcare monitoring, enhancing patients’ well-being at an affordable cost. However, their primary challenge lies in energy constraints. To optimize the limited energy resources and extend the lifetime of devices within the network, a multi-channel MAC protocol with Markov decision process (MDP-HYMAC) is proposed. This protocol improves energy efficiency, throughput, and network lifetime, and minimizes delays by using separate channels for communication between biomedical devices and access points (APs). In addition, a Markov decision process is employed to stochastically model the systems’ transition states and explore optimal communication strategies between biomedical devices and APs. Furthermore, an adaptive power allocation scheme, a time-slot allocation scheme, and a back-off strategy are designed to minimize time-slot wastage, energy consumption, and delays. The proposed protocol outperforms the baseline methods by achieving significant improvements in energy efficiency from 4% to 22%. The findings of this research strongly indicate that the proposed protocol has the potential to significantly improve the performance of WBAN systems, particularly in the context of sustainable healthcare monitoring.

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Original languageEnglish
Pages (from-to)27981-27997
JournalIEEE Sensors Journal
Volume24
Issue number17
Online published25 Jul 2024
DOIs
Publication statusPublished - 1 Sept 2024

Research Keywords

  • Body area networks
  • channel efficiency
  • Delays
  • energy efficiency
  • Energy efficiency
  • healthcare monitoring
  • IEEE 802.15 Standard
  • Internet of Things
  • Media Access Control
  • multi-channel MAC protocol design
  • Protocols
  • reinforcement learning
  • wireless body area network
  • Wireless communication

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