Localization in long-range ultra narrow band IoT networks using RSSI

Hazem Sallouha, Alessandro Chiumento, Sofie Pollin

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

57 Citations (Scopus)
17 Downloads (Pure)


Internet of things wireless networking with long-range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Communications (ICC)
Number of pages6
ISBN (Electronic)978-1-4673-8999-0
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 21 May 201725 May 2017


ConferenceIEEE International Conference on Communications, ICC 2017
Abbreviated titleICC 2017


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