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

Abstract

Abstract: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.

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning

“…In a series of studies [3], [8], [13], the authors of the IDLab of the University of Antwerp have presented a publicly available Sigfox dataset and have elaborated on the proper configuration of a kNN fingerprinting algorithm. In the publication in which the dataset used in this study was presented to the research community [3], the authors exemplified the usage of the dataset, reporting a 689 meter mean error in this first approach of utilizing the dataset. In a following publication of the IDLab by Janssen et al [13], the authors present a detailed study of numerous distance metrics and data preprocessing (or data representation) methods, using the same dataset and achieving a mean error of 340 meters.…”

“…Aenrouts et al [3] have made publicly available 3 fingerprinting datasets of Low Power Wide Area Networks. Two of these datasets were collected in the urban area of Antwerp, one using Sigfox and another using LoRaWAN.…”

“…Similarly, the Sigfox company is advertising the capabilities of its localization service [2]. A detailed comparative analysis of the most prominent LPWAN technologies can be found in [3] and references therein.…”

“…A disadvantage of fingerprinting techniques is that the creation and maintenance of an up-to-date fingerprint database requires considerable effort and cost. One practical way to record such a dataset outdoors is presented in the work of Aernouts et al [3]. In that work, a dataset is made publicly available and its collection methodology is presented.…”

“…Recent works in the field of indoor and outdoor positioning [3], [4], [5] have underlined the fact that it is indispensable for the publications of the field to favour the reproducibility of the experiments and the comparability of the results. In this spirit, we utilize a publicly available Sigfox dataset [3], and we share with the community the code of the experiments of the current study (DOI:10.5281/zenodo.3228752), and the train/validation/test set split used in said dataset (DOI: 10.5281/zenodo.3228744), to facilitate a consistent comparison of results in future works.…”