Open AccessQTPC: A Q-Learning Transmission Power Control Mechanism for Edge-Cloud Wireless Body Area Networks
Author(s) -
Haoru Su,
Xiaoming Yuan,
Enchang Sun,
Pengbo Si,
Huamin Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012048
Subject(s) - computer science , enhanced data rates for gsm evolution , transmission (telecommunications) , body area network , wireless , throughput , mechanism (biology) , computer network , cloud computing , wireless network , power control , efficient energy use , power (physics) , wireless sensor network , engineering , telecommunications , electrical engineering , philosophy , physics , epistemology , quantum mechanics , operating system
Wireless body area networks collect biological signals from human body and sensors connect wirelessly for various nonmedical and medical applications. Energy efficiency is one of the most essential problems in wireless body area networks since the limited battery capacity. In this paper, a Q-learning transmission power control (QTPC) mechanism for edge-cloud wireless body area networks is proposed. Simulation results show that the proposed scheme improves the network performance in the metrics of energy efficiency and system throughput.