Resource Allocation in Body Area Networks for Energy Harvesting Healthcare Monitoring

Leng, Shiyang; Yener, Aylin

doi:10.1007/978-3-319-58280-1_20

Cited by 3 publications

(2 citation statements)

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“…To the best of our knowledge, the research on resource allocation for EH-WBANs is in its infancy, despite having some pioneering studies [26][27][28][29][30][31][32][33][34][35]. Mohammadi et al [26] proposed a link adaption mechanism to maximize energy efficiency in Institute of Electrical and Electronics Engineers (IEEE) 802.15.6 impulse radio ultra-wideband (IR-UWB) WBANs.…”

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confidence: 99%

“…An optimal energy management strategy for solar-powered WSNs was proposed in the work of Niyato et al [31]; the authors concentrated on the sleep and wakeup scheduling for energy conservation. However, different from EH-powered WSNs, EH-WBANs mainly harvest energy from human body bio-energy sources [32]. These human body bio-energy sources can be categorized into bio-chemical and bio-mechanical energy sources.…”

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confidence: 99%

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Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Xie

Zhang

et al. 2019

Sensors

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Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

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