  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 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.
