# Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2205.11428 · HTML: https://mlsanity.com/paper/2205.11428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 2 of 2)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)

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

- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion](https://mlsanity.com/paper/2411.17781/) (2024)
- [A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments](https://mlsanity.com/paper/2405.09960/) (2024)

## Abstract (arXiv)

Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map, leading to inaccurate position estimates. As such, in this work, we propose a novel LoRa RSSI fingerprinting approach that takes into account the SF. The performance evaluation shows the prominence of our proposed approach since we achieved an improvement in localization accuracy by up to 6.67% compared to the state-of-the-art methods. The evaluation has been done using a fully connected deep neural network (DNN) set as the baseline. To further improve the localization accuracy, we propose a deep reinforcement learning model that captures the ever-growing complexity of LoRa networks and copes with their scalability. The obtained results show an improvement of 48.10% in the localization accuracy compared to the baseline DNN model.