Reinforcement learning based energy-neutral operation for hybrid EH powered TBAN

Read:: - [x] Zhang et al. (2023) - Reinforcement learning based energy-neutral operation for hybrid EH powered TBAN ➕2024-04-08 !!2 rd citation link todoist ✅ 2024-04-08 Print::  ❌ Zotero Link:: Zotero Files:: attachment Reading Note:: Web Rip:: url:: https://www.sciencedirect.com/science/article/pii/S0167739X22003600

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Abstract

The aging population, outbreak of new infectious diseases and shortage of medical resources promote rapid development of telemedicine. Wireless textile body area network (TBAN), which combines functional textile and wireless body area network (WBAN), is gaining great attention as an efficient medium of remote medical care. This is because of its unique materials and application scenario, as well as its convenience and friendliness to the elderly. Moreover, it is an effective application for integrating edge computing with next generation of wearable technology. Nonetheless, it is unavoidable that TBAN has to deal with reliability and energy issues. Given these deficiencies and challenges, this paper focuses on the feasibility of achieving wearable energy neutral operation (ENO) in TBAN while maintaining robustness. In addition to adding user posture factors regarding network specifics, we combine hybrid energy harvesting (EH) techniques and duty cycle schemes. A hybrid radio frequency (RF) energy and Triboelectric nanogenerator (TENG) EH-assisted TBAN system is built in this work. We analyze and discuss the delay, data rate and packet error rate (PER) under five typical daily activities (standing, sitting, lying, walking, and running). To optimize the ENO problem, two reinforcement learning (Q-learning and Deep Q-Network (DQN)) based algorithms are proposed. According to numerical results, both algorithms ultimately lead to stable power levels compared to the continuous decline of battery power without optimization. DQN-based optimization performs better than Q-Learning. For instance, 14% and 56% improvements in PER and battery power, respectively.

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Wireless textile body area network (TBAN) tp

wireless body area network (WBAN) tp

energy neutral operation (ENO) tp

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Page 312

We build a TBAN model considering multi-posture with hybrid energy harvester. Devices are powered by a mixture of captured RF and tribological electrical energy along with rechargeable battery. • Hybrid EH strategy and duty cycle technology are employed to overcome the energy efficiency and reliability challenges that exist in TBAN. Five common daily activities are discussed separately to analyze the energy consumption and acquisition as well as the network reliability of TBAN. • Reinforcement learning based methods are applied to solve the proposed ENO problem in TBAN. We achieve a tradeoff between energy consumption and collection to achieve long-term use of the equipment. Q-Learning and DQN based algorithms are proposed to verify its feasibility.

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Energyharvesting (EH) Page 312