Preprint Data Descriptor Version 1 This version is not peer-reviewed

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

Version 1 : Received: 16 March 2018 / Approved: 19 March 2018 / Online: 19 March 2018 (05:38:26 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Data 2018, 3, 13
DOI: 10.3390/data3020013

Abstract

In order to evaluate fingerprint localization methods in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which were collected in large-scale urban and rural areas, their goal is to provide the global research community with a benchmark tool to evaluate fingerprint localization algorithms in large outdoor environments with various properties. An identical collection methodology was used for all datasets: during a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver. The location data was sent to a local server via a Sigfox or LoRaWAN message. Together with network information such as the receiving time of the message, base station IDs’ of all receiving base stations and the Received Signal Strength Indicator (RSSI) per base station, this location data was stored in the appropriate LPWAN dataset. The results of our 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.

Subject Areas

IoT; LPWAN; Sigfox; LoRaWAN; localization; fingerprinting

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