  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 paper, we firstly analyze and
summarize these obstacles, and propose an efficient large-scale WiFi sensing
framework, namely EfficientFi. The EfficientFi works with edge computing at
WiFi APs 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 auto-encoder 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 IoT-cloud-enabled WiFi sensing framework that significantly
reduces communication overhead while realizing sensing tasks accurately. We
utilized human activity recognition 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 1.368Mb/s to 0.768Kb/s with
extremely low error of data reconstruction and achieves over 98% accuracy for
human activity recognition.
