Toward Practical Lightweight Passive Human Tracking Using WiFi Sensing

Abstract

With the wide adoption of versatile IoT devices, device providers may desire to locate users around those devices to plan context-aware intelligence, which may improve the quality of daily life. As most IoT devices are Wi-Fi enabled, the Wi-Fi-based indoor positioning system is supposed to achieve this future scene. However, the state-of-art Wi-Fi indoor positioning systems face challenges when being practically deployed as they may have to trade-off between, say accuracy and computational overhead. In light of this, this article mainly introduces PLP-Track, a Practical Lightweight Passive indoor tracking system based on CSI (channel state information) fingerprints. To settle the low granularity of fingerprints in distinguishing different positions, we propose a fingerprint preprocessing algorithm based on unsupervised learning and incorporate this algorithm with a state-space model to enable lightweight real-time tracking. Our implementation and evaluation of commodity WiFi devices demonstrate that PLP-Track can achieve indoor localization with high accuracy and low computation cost.