Statistical Modeling of the Battery Recharging Time in RF Energy Harvesting for IoT Applications

被引：0

作者：

Mouapi, Alex ^{[1
]}

Hakem, Nadir ^{[1
]}

Kandil, Nahi ^{[1
]}

机构：

[1] Univ Quebec Abitibi Temiscamingue UQAT, Lab Rech Telebec Commun Souterraines LRTCS, Val Dor, PQ, Canada

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING | 2020年

关键词：

IoT; BRT; lognormal Shadowing; Smart buildings;

D O I：

10.1109/IEEECONF35879.2020.9330240

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Radiofrequency waves are increasingly seen as a promising solution for powering Wireless Sensor Nodes (WSN) dedicated to the Internet of Things (IoT) applications. However, by considering the RF source as a WSN power solution, Battery Recharging Time (BRT) becomes a critical performance metric, especially when the Quality of Service (QoS) is a requirement. In the literature, very few works propose an analysis of BRT based on an RF Energy Harvesting system. In this paper, the characterization of the BRT as a function of the harvestable power is analyzed, and modeling of the BRT is then proposed. The results are based on the ambient power density level measured in a building. It is obtained that the BRT undergoes as the received ambient power, a large scale fading effect shadowing more specifically the lognormal Shadowing.

引用

页码：1183 / 1184

页数：2

共 5 条

[1]

Altinel D, 2014, IEEE WCNC, P636, DOI 10.1109/WCNC.2014.6952122

[2] A new approach to design of RF energy harvesting system to enslave wireless sensor networks [J].