Evaluation of Real-Life LoRaWAN Localization: Accuracy Dependencies Analysis Based on Outdoor Measurement Datasets

Svertoka, Ekaterina; Marghescu, Ion; Rusu-Casandra, Alexandru; Bürget, Radim; Hošek, Jiří; Mašek, Pavel; Ometov, Aleksandr

doi:10.1109/comm54429.2022.9817302

Cited by 4 publications

(1 citation statement)

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“…In the online phase, the operational fingerprint (whose location is not known) is compared to all the reference fingerprints to estimate its position. Due to its simplicity, it is also gaining relevance for outdoor positioning in the IoT context using LoRaWAN or SigFox [17], [18], [19], [20].…”

Section: Scalable and Efficient Clustering Formentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

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Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.

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Section: Scalable and Efficient Clustering Formentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

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LoRaWAN: Lost for Localization?

et al. 2022

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Nowadays, the flexible localization solution for various devices for work safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and the Interent of Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, e.g., Wi-Fi, BLE, UWB are used for indoor areas and various GNSS-based ones for outdoor. This work focuses on investigating the LoRaWAN (868 MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial Internet of Things (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing a tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the

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