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

被引：3

作者：

Su, Haoru ^{[1
]}

Yuan, Xiaoming ^{[2
]}

Tanga, Yujie ^{[3
]}

Tian, Rui ^{[1
]}

Sun, Enchang ^{[1
]}

Yan, Hairong ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

[2] Northeastern Univ, Qinhuangdao Branch Campus, Qinhuangdao, Hebei, Peoples R China

[3] Algoma Univ, Sault Ste Marie, ON, Canada

来源：

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2021年 / 15卷 / 12期

基金：

中国国家自然科学基金;

关键词：

IoT; eHealth; Body Area Networks; power control; low energy; BODY AREA NETWORKS; RESOURCE-ALLOCATION; WIRELESS;

D O I：

10.3837/tiis.2021.12.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：4385 / 4399

页数：15

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