Attending to Routers Aids Indoor Wireless Localization

TL;DR

This paper introduces an attention mechanism for Wi-Fi-based indoor localization that weights routers differently, significantly improving accuracy over traditional methods.

Contribution

It proposes incorporating attention layers into localization models to better weight router contributions, enhancing performance in diverse environments.

Findings

Over 30% accuracy improvement over benchmarks

Attention weighting improves convergence and localization precision

Evaluation on open datasets confirms effectiveness

Abstract

Modern machine learning-based wireless localization using Wi-Fi signals continues to face significant challenges in achieving groundbreaking performance across diverse environments. A major limitation is that most existing algorithms do not appropriately weight the information from different routers during aggregation, resulting in suboptimal convergence and reduced accuracy. Motivated by traditional weighted triangulation methods, this paper introduces the concept of attention to routers, ensuring that each router's contribution is weighted differently when aggregating information from multiple routers for triangulation. We demonstrate, by incorporating attention layers into a standard machine learning localization architecture, that emphasizing the relevance of each router can substantially improve overall performance. We have also shown through evaluation over the open-sourced datasets and demonstrate that Attention to Routers outperforms the benchmark architecture by over 30% in accuracy.