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Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

IDLab—Faculty of Applied Engineering, University of Antwerp—imec, Groenenborgerlaan 171, 2020 Antwerp, Belgium
Sensolus NV, Rijsenbergstraat 148, 9000 Ghent, Belgium
Author to whom correspondence should be addressed.
Received: 16 March 2018 / Revised: 5 April 2018 / Accepted: 5 April 2018 / Published: 10 April 2018
Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT. View Full-Text
Keywords: IoT; LPWAN; Sigfox; LoRaWAN; localization; fingerprinting IoT; LPWAN; Sigfox; LoRaWAN; localization; fingerprinting
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    Doi: 10.5281/zenodo.1193562
    Description: The datasets that are described in the manuscript are published at Zenodo. The DOI that we provided represents all versions of the dataset, and will always resolve to the latest one.
MDPI and ACS Style

Aernouts, M.; Berkvens, R.; Van Vlaenderen, K.; Weyn, M. Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas. Data 2018, 3, 13.

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