TY - JOUR

T1 - No Need of Data Pre-processing

T2 - A General Framework for Radio-Based Device-Free Context Awareness

AU - Wei, Bo

AU - Li, Kai

AU - Luo, Chengwen

AU - Xu, Weitao

AU - Zhang, Jin

AU - Zhang, Kuan

PY - 2021/11/30

Y1 - 2021/11/30

N2 - Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers’ attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for eachradio-based application. Furthermore, they use oneadditional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

AB - Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers’ attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for eachradio-based application. Furthermore, they use oneadditional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

U2 - 10.1145/3467980

DO - 10.1145/3467980

M3 - Journal article

VL - 2

JO - ACM Transactions on Internet of Things

JF - ACM Transactions on Internet of Things

IS - 4

M1 - 29

ER -