Open Access

ARTICLE

A Material Identification Approach Based on Wi-Fi Signal

Chao Li¹, Fan Li^1,2, Wei Du³, Lihua Yin^1,*, Bin Wang⁴, Chonghua Wang⁵, Tianjie Luo¹

1 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510700, China
2 PCL Research Center of Cyberspace Security, Peng Cheng Laboratory, Shenzhen, 518052, China
3 Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, 72701, USA
4 College of Electrical Engineering, Zhejiang University, Hangzhou, 310058, China
5 China Industrial Control Systems Cyber Emergency Response Team, Beijing, 100040, China

* Corresponding Author: Lihua Yin. Email:

Computers, Materials & Continua 2021, 69(3), 3383-3397. https://doi.org/10.32604/cmc.2021.020765

Received 07 June 2021; Accepted 22 July 2021; Issue published 24 August 2021

Abstract

Material identification is a technology that can help to identify the type of target material. Existing approaches depend on expensive instruments, complicated pre-treatments and professional users. It is difficult to find a substantial yet effective material identification method to meet the daily use demands. In this paper, we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier, which can significantly reduce the cost and guarantee a high level accuracy. In practical measurement of Wi-Fi based material identification, these two features are commonly interrupted by the software/hardware noise of the channel state information (CSI). To eliminate the inherent noise of CSI, we design a denoising method based on the antenna array of the commercial off-the-shelf (COTS) Wi-Fi device. After that, the amplitude ratios and phase differences can be more stably utilized to classify the materials. We implement our system and evaluate its ability to identify materials in indoor environment. The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%. It can also identify similar liquids with an overall accuracy higher than 95%, such as various concentrations of salt water.

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Keywords

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