EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression
Jianfei Yang, Xinyan Chen, Han Zou, Dazhuo Wang, Qianwen Xu, Lihua Xie

TL;DR
EfficientFi introduces a scalable WiFi sensing framework that leverages edge and cloud computing, employing neural network-based CSI compression to enable accurate, large-scale human activity recognition with minimal data transmission.
Contribution
This paper presents the first IoT-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while maintaining high sensing accuracy.
Findings
Compresses CSI data from 1.368Mb/s to 0.768Kb/s with low error
Achieves over 98% accuracy in human activity recognition
Demonstrates effectiveness in large-scale WiFi sensing scenarios
Abstract
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…
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