A Learning-based Power Control Scheme for Edge-based eHealth IoT Systems

• Haoru Su,
• Xiaoming Yuan,
• Yujie Tang,
• Rui Tian,
• Enchang Sun,
• Hairong Yan,

Vol. 15, No. 12, December 31, 2021
10.3837/tiis.2021.12.007, Download Paper (Free):

• IoT
• eHealth
• Body area networks
• Power control
• low energy

Abstract

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

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