Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning

TL;DR

This paper introduces a novel LoRa localization method that incorporates spreading factor information and employs deep reinforcement learning, significantly enhancing accuracy over traditional RSSI fingerprinting techniques.

Contribution

It proposes a new SF-aware RSSI fingerprinting approach and a deep reinforcement learning model to improve LoRa localization accuracy and scalability.

Findings

Up to 6.67% improvement over state-of-the-art methods.

48.10% accuracy increase with deep reinforcement learning.

Effective handling of LoRa network complexity and scalability.

Abstract

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.