# A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments

arXiv: https://arxiv.org/abs/2405.09960 · HTML: https://mlsanity.com/paper/2405.09960/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 16 May 2024.

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

- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)
- [Reliable Deep Learning based Localization with CSI Fingerprints and Multiple Base Stations](https://mlsanity.com/paper/2111.11839/) (2021)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Federated Learning based Hierarchical 3D Indoor Localization](https://mlsanity.com/paper/2303.00450/) (2023)
- [Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning](https://mlsanity.com/paper/2205.11428/) (2022)
- [A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN](https://mlsanity.com/paper/1908.05085/) (2019)
- [Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization](https://mlsanity.com/paper/2108.06884/) (2021)

## Built on by (most cited first, 2 of 2)

- [UBiGTLoc: A Unified BiLSTM-Graph Transformer Localization Framework for IoT Sensor Networks](https://mlsanity.com/paper/2601.10743/) (2026)
- [Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks](https://mlsanity.com/paper/2601.22322/) (2026)

## Abstract (arXiv)

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments.