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Mobile Information Systems
Volume 2017, Article ID 8713873, 14 pages
Research Article

A Novel Low-Cost Real-Time Power Measurement Platform for LoWPAN IoT Devices

1Shanghai Institute of Microsystem and Information Technology, Key Laboratory of Wireless Sensor Network and Communication, Chinese Academy of Sciences (CAS), Shanghai, China
2Shanghai Research Center for Wireless Communications (WiCO), Shanghai, China
3Shanghai Advanced Research Institute, Chinese Academy of Sciences (CAS), Shanghai, China

Correspondence should be addressed to Wuxiong Zhang; hs.ociw@gnahz.gnoixuw

Received 25 November 2016; Accepted 29 January 2017; Published 22 February 2017

Academic Editor: Jeongyeup Paek

Copyright © 2017 Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the rapid development of technology and application for Internet of Things (IoT), Low-Power Wireless Personal Area Network (LoWPAN) devices are more popularly applied. Evaluation of power efficiency is important to LoWPAN applications. Conventional method to evaluate the power efficiency of different LoWPAN devices is as follows: first measure the current of the devices under working/idle/sleep state and then make an average and estimation of the lifetime of batteries, which deeply relied on the accuracy of testing equipment and is not that accurate and with high cost. In this work, a low-cost, real-time power measurement platform called PTone is proposed, which can be used to detect the real-time power of LoWPAN devices (above 99.63%) and be able to determine the state of each module of DUT system. Based on the PTone, a novel abnormal status diagnosis mechanism is proposed. The mechanism can not only judge abnormal status but also find accurate abnormal status locating and classify abnormal status accurately. According to the method, each state of Device Under Test (DUT) during wireless transmission is estimated, different abnormal status can be classified, and thus specific location of abnormal module can be found, which will significantly shorten the development process for LoWPAN devices and thus reduce costs.