Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices

TL;DR

This paper introduces a Siamese neural encoder framework for indoor localization that significantly reduces accuracy degradation over time without retraining, leveraging WiFi signals and smartphone data.

Contribution

The proposed Siamese neural encoder approach addresses temporal signal variability and access point changes, improving long-term indoor localization accuracy without retraining.

Findings

Up to 40% reduction in localization accuracy degradation over time.

No retraining required for the proposed framework.

Effective handling of WiFi signal variability and access point changes.

Abstract

Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. The superior pairing of ubiquitously available WiFi signals with computationally capable smartphones is set to revolutionize the area of indoor localization. However, the observed signal characteristics from independently maintained WiFi access points vary greatly over time. Moreover, some of the WiFi access points visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months). To overcome these challenges, we propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area, without requiring any retraining.