Attending to Routers Aids Indoor Wireless Localization
This work solves indoor localization accuracy issues for users in diverse environments, but it is incremental as it builds on existing machine learning architectures with attention mechanisms.
The paper tackled the problem of improving Wi-Fi-based indoor localization by addressing suboptimal router information aggregation, resulting in over 30% accuracy improvement compared to benchmarks.
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.