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Analysis of RSSI Fingerprinting in LoRa Networks
National University of Sciences and Technology (NUST), Pakistan.
National University of Sciences and Technology (NUST), Pakistan.
National University of Sciences and Technology (NUST), Pakistan.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.ORCID iD: 0000-0003-3717-7793
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2019 (English)In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, 2019, p. 1178-1183Conference paper, Published paper (Refereed)
Abstract [en]

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment

Place, publisher, year, edition, pages
IEEE, 2019. p. 1178-1183
Keywords [en]
Curve fitting, LoRa, path loss, positioning, RSSI fingerprinting, spreading factor
National Category
Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:miun:diva-37131DOI: 10.1109/IWCMC.2019.8766468ISI: 000492150100200Scopus ID: 2-s2.0-85073899834ISBN: 978-1-5386-7747-6 (electronic)OAI: oai:DiVA.org:miun-37131DiVA, id: diva2:1349012
Conference
15th International Wireless Communications & Mobile Computing Conference, 24-28 June, 2019, Tangier, Morocco
Funder
Knowledge FoundationAvailable from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-12-20Bibliographically approved

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