Crowdsourcing Ubiquitous Indoor Localization with Non-Cooperative Wi-Fi Ranging

TL;DR

PeepLoc is a practical Wi-Fi-based indoor localization system that uses non-cooperative ToF measurements and crowdsourcing to achieve accurate positioning without specialized hardware.

Contribution

It introduces a scalable indoor localization method leveraging existing Wi-Fi infrastructure and crowdsourcing, enabling deployment on standard devices without modifications.

Findings

Mean positional error of 3.41 meters

Median positional error of 3.06 meters

Outperforms existing indoor localization systems

Abstract

Indoor localization opens the path to potentially transformative applications. Although many indoor localization methods have been proposed over the years, they remain too impractical for widespread deployment in the real world. In this paper, we introduce PeepLoc, a deployable and scalable Wi-Fi-based solution for indoor localization that relies only on pre-existing devices and infrastructure. Specifically, PeepLoc works on any mobile device with an unmodified Wi-Fi transceiver and in any indoor environment with a sufficient number of Wi-Fi access points (APs) and pedestrian traffic. At the core of PeepLoc is (a) a mechanism which allows any Wi-Fi device to obtain non-cooperative time-of-flight (ToF) to any Wi-Fi AP and (b) a novel bootstrapping mechanism that relies on pedestrian dead reckoning (PDR) and crowdsourcing to opportunistically initialize pre-existing APs as anchor points within an environment. We implement PeepLoc using commodity hardware and evaluate it extensively across 4 campus buildings. We show PeepLoc leads to a mean and median positional error of 3.41 m and 3.06 m respectively, which is superior to existing deployed indoor localization systems and is competitive with commodity GPS in outdoor environments.