# Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization

arXiv: https://arxiv.org/abs/2205.08069 · HTML: https://mlsanity.com/paper/2205.08069/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [On the Relationship between Self-Attention and Convolutional Layers](https://mlsanity.com/paper/1911.03584/) (2019)
- [CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning](https://mlsanity.com/paper/2107.01192/) (2021)
- [Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2112.00654/) (2021)
- [QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2104.07521/) (2021)

## Built on by (most cited first, 13 of 13)

- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization](https://mlsanity.com/paper/2302.09443/) (2023)
- [STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization](https://mlsanity.com/paper/2312.10312/) (2023)
- [SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization](https://mlsanity.com/paper/2403.01348/) (2024)
- [CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization](https://mlsanity.com/paper/2311.06361/) (2023)
- [SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks](https://mlsanity.com/paper/2407.11091/) (2024)
- [AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems](https://mlsanity.com/paper/2408.04797/) (2024)
- [DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones](https://mlsanity.com/paper/2506.15554/) (2025)
- [Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates](https://mlsanity.com/paper/2506.15559/) (2025)
- [GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices](https://mlsanity.com/paper/2507.11053/) (2025)
- [Indoor Localization for Autonomous Robot Navigation](https://mlsanity.com/paper/2502.20731/) (2025)
- [Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization](https://mlsanity.com/paper/2511.17829/) (2025)
- [ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization](https://mlsanity.com/paper/2603.19594/) (2026)

## Abstract (arXiv)

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.