Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization

TL;DR

This paper introduces a multi-head attention neural network framework for indoor localization that effectively handles device heterogeneity, significantly improving accuracy over existing methods in diverse indoor environments.

Contribution

The paper presents a novel multi-head attention neural network approach specifically designed to mitigate device heterogeneity in RSSI-based indoor localization.

Findings

Up to 35% accuracy improvement over state-of-the-art methods

Effective resilience to device-induced RSSI variations

Validated across multiple indoor environments

Abstract

Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proliferation of this technology in the public domain. One such critical challenge is device heterogeneity, i.e., the variation in the RSSI signal characteristics captured across different smartphone devices. In the real-world, the smartphones or IoT devices used to capture RSSI fingerprints typically vary across users of an indoor localization service. Conventional indoor localization solutions may not be able to cope with device-induced variations which can degrade their localization accuracy. We propose a multi-head attention neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework across a variety of indoor environments demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.