# AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems

arXiv: https://arxiv.org/abs/2408.04797 · HTML: https://mlsanity.com/paper/2408.04797/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning](https://mlsanity.com/paper/2107.01192/) (2021)
- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2112.00654/) (2021)
- [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)
- [QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2104.07521/) (2021)
- [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)

## Built on by (most cited first, 3 of 3)

- [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)
- [Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization](https://mlsanity.com/paper/2511.17829/) (2025)

## Abstract (arXiv)

Indoor navigation is a foundational technology to assist the tracking and localization of humans, autonomous vehicles, drones, and robots in indoor spaces. Due to the lack of penetration of GPS signals in buildings, subterranean locales, and dense urban environments, indoor navigation solutions typically make use of ubiquitous wireless signals (e.g., WiFi) and sensors in mobile embedded systems to perform tracking and localization. This article provides an overview of the many challenges facing state-of-the-art indoor navigation solutions, and then describes how AI algorithms deployed on mobile embedded systems can overcome these challenges.