Localization in ultra narrow band iot networks: Design guidelines and tradeoffs

Hazem Sallouha, Alessandro Chiumento, Sreeraj Rajendran, Sofie Pollin

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
3 Downloads (Pure)

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 article, 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 nodes (GSNs) in the network to split the wide coverage into classes, enabling RSSI-based fingerprinting of other sensors nodes (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 city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.
Original languageEnglish
Pages (from-to)9375-9385
Number of pages11
JournalIEEE Internet of Things Journal
Volume6
Issue number6
DOIs
Publication statusPublished - 2019
Externally publishedYes

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