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

2025-06-23

AI Summary

Existing indoor positioning systems suffer from high deployment costs, poor scalability, and reliance on dedicated hardware. To address these limitations, this paper proposes a ubiquitous indoor positioning solution leveraging only off-the-shelf Wi-Fi devices. The method innovatively integrates non-cooperative Wi-Fi time-of-flight (ToF) ranging, pedestrian dead reckoning (PDR), and crowdsourced anchor point construction—requiring no access point (AP) modifications, pre-deployment calibration, or specialized user terminals, thus enabling self-initializing, calibration-free deployment. By fusing multi-source measurements and applying robust position optimization, the system achieves an average localization error of 3.41 m and a median error of 3.06 m across four campus buildings—outperforming comparable deployable systems and approaching outdoor GPS accuracy. The key contribution is the first integration of non-cooperative Wi-Fi ToF with PDR-enabled crowdsourcing for autonomous anchor initialization, establishing a practical, low-cost, and highly adaptable indoor positioning paradigm for large-scale existing Wi-Fi infrastructures.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Enabling indoor localization using existing Wi-Fi devices
• Overcoming impracticality of current indoor localization methods
• Achieving accurate positioning without infrastructure modification

Innovation

• Uses non-cooperative Wi-Fi time-of-flight ranging
• Leverages pedestrian dead reckoning for bootstrapping
• Relies on crowdsourcing to initialize anchor points