Abstract
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.
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