Attending to Routers Aids Indoor Wireless Localization

2026-02-18

AI Summary

This study addresses the challenge of effectively integrating signal information from multiple access points in machine learning–based indoor Wi-Fi localization, a limitation that hinders positioning accuracy in multi-router environments. To overcome this, the authors propose a novel attention mechanism—integrated for the first time into a standard localization architecture—that dynamically weights the contributions of individual routers, thereby enabling a form of weighted triangulation for signal aggregation. The approach significantly enhances the utilization efficiency of multi-source Wi-Fi signals while preserving model generality. Experimental evaluation on a public dataset demonstrates that the proposed method improves localization accuracy by over 30% compared to baseline models, substantiating its effectiveness and innovation.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• wireless localization
• Wi-Fi signals
• router weighting
• indoor positioning
• information aggregation

Innovation

• attention mechanism
• wireless localization
• Wi-Fi positioning
• router weighting
• triangulation