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

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.

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

“…Localization is an important aspect of GPS-alternative positioning systems and has considerable importance in general communications areas [1]. The development of new radio access standards prompted the exploration of new techniques to improve the location accuracy, which is based on the signals available from the wireless devices that comprise these standards [2][3][4][5][6].…”

A Survey of LoRaWAN for IoT: From Technology to Application

“…LoRaWAN is used for different application types ranging from health and wellbeing monitoring [ 12 , 13 ], agriculture monitoring [ 14 , 15 , 16 , 17 ], wireless sensor networks [ 18 , 19 ], traffic monitoring [ 20 ], localization [ 21 , 22 , 23 ], smart city applications [ 24 ] up to smart grids and tele-measurements [ 25 , 26 , 27 , 28 ]. It is used mainly for non-latency-sensitive applications and for applications where large-scale deployments are needed.…”

“…For fingerprinting algorithms to work, large datasets need to be available. In [ 21 ], a large-scale LoRaWAN dataset collected in rural and urban areas has been made available. In addition, authors implemented a simple fingerprinting algorithm, k nearest neighbors ( kNN ) [ 31 ] fingerprinting, to evaluate the accuracy of localization.…”

A New on-Demand Recharging Strategy Based on Cycle-Limitation in a WRSN

“…In the new CRCM, the data transmission distance was within the single-hop distance. Therefore, we only discussed the Free Space Model to structure the sensor node energy consumption model as follows [30]:…”