Zero-Calibration Device-free Localization for the IoT based on Participatory Sensing

TL;DR

RadioGrapher automates device-free fingerprint calibration in IoT environments using participatory sensing, eliminating manual site surveys and achieving high localization accuracy comparable to traditional methods.

Contribution

Introduces RadioGrapher, a novel system that automates device-free fingerprint calibration in IoT environments through crowd-sensing and Fresnel zone analysis.

Findings

High accuracy in fingerprint construction

Median localization accuracy comparable to manual methods

No calibration overhead required

Abstract

Device-free localization (DFL) is an emerging technology for estimating the position of a human or object that is not equipped with any electronic tag, nor participate actively in the localization process. Similar to device-based localization, the initial phase in DFL is to build the fingerprint database which is usually done manually using site surveying. This process is tedious, time-consuming, and vulnerable to environmental dynamics. Motivated by the recent advances in the Internet of Things (IoT), this paper introduces RadioGrapher; a system that automates the process of device-free fingerprint calibration in IoT environments. RadioGrapher leverages the device-based locations of entities in the area of interest in a crowd-sensing manner, aided with Fresnel zones of the wirelessly connected IoT devices to automatically construct a device-free fingerprint. Experimental evaluation of RadioGrapher in an IoT testbed using multiple entities shows that it can construct DFL fingerprints with high accuracy. Moreover, its median localization accuracy is comparable to that of manual fingerprinting. This comes with no calibration overhead, highlighting the promise of RadioGrapher as a crowdsourcing device-free fingerprint constructor in IoT environments.