期刊
IEEE INTERNET OF THINGS JOURNAL
卷 6, 期 6, 页码 9375-9385出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2931628
关键词
Fingerprinting; Internet of Things (IoT); localization; machine learning; received signal strength indicator (RSSI); ultra narrow band (UNB)
资金
- FWO SBO Project SAMURAI
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 deviceto-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.
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