LAUSR

WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device free, cost effective and privacy preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this article, we first analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely, EfficientFi. The EfficientFi works with edge computing at WiFi access points and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi channel state information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized autoencoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first Internet of Things-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition (HAR) and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368 Mb/s to 0.768 kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for HAR.