Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Trade-Offs
Hazem Sallouha, Alessandro Chiumento, Sreeraj Rajendran, Sofie Pollin

TL;DR
This paper presents a novel RSSI-based localization method for UNB IoT networks like Sigfox, leveraging GPS-enabled sensors and machine learning to improve accuracy without extra hardware.
Contribution
The work introduces a new localization approach using existing GPS sensors and machine learning, validated with real city data, enhancing accuracy and efficiency in long-range IoT networks.
Findings
Achieves 80% classification accuracy in city-wide localization.
Improves localization error probability by 40% with multilateration.
Higher class separation increases classification accuracy up to 92%.
Abstract
Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the…
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