Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor

Mašek, Pavel; Štůsek, Martin; Svertoka, Ekaterina; Pospisil, Jan; Bürget, Radim; Lohan, Elena Simona; Marghescu, Ion; Hošek, Jiří; Ometov, Aleksandr

doi:10.3390/data6060062

Cited by 12 publications

(15 citation statements)

References 29 publications

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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.

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“…However, its application as a localization solution is still at the infancy. Currently, the literature is still lacking the discussion in this research direction with only a few works [21], [22] or open datasets encouraging researchers [23], [24].…”

Section: Related Workmentioning

confidence: 99%

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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“…The collection process and data structure of this measurement campaign are described in detail in [20]. During 2 days from each of 96 locations associated with the stops of public transport were sent 5 uplink messages (packets) using Field Test Device (FTD) LoRaWAN 868 [21] with a GPS receiver onboard.…”

Section: A the Dataset Collected From Public Stops Throughout The Cit...mentioning

confidence: 99%

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

Svertoka

Marghescu

Rusu-Casandra

et al. 2022

2022 14th International Conference on Communications (COMM)

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Modern outdoor localization is commonly dependent on various Global Navigation Satellite Systems (GNSSs). On the other hand, they are known to be power-hungry and not suitable for resource-constrained devices currently flooding the Industrial Internet of Things (IIoT). Nonetheless, some of those devices may be equipped with Low-Power Wide-Area Network (LPWAN) communication chip that could be utilized for positioning. Current work examines two outdoor datasets collected using LoRaWAN in Brno, Czech Republic, to assess the possibility of applying technology for localization solutions for industrial outdoor scenarios. The main localization approach applied in this is work is k-NN fingerprinting. For the first dataset gathered over the whole city, the minimal mean localization error turned out to be not stable, while accuracy for the second one covering a small rectangular area 8.5 x 70 m is 6.42 m that sounds promising in terms of LoRaWAN-based localization. Moreover, by analyzing data collected in two independent measurements campaigns, this work provides some derivations related to the accuracy dependencies on parameters of the measurement campaign (gateways (GWs), coverage area, the average distance between measurement points). It makes a step towards comparing the results of published papers in this area obtained for different datasets.

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Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor

Cited by 12 publications

References 29 publications

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Scalable and Efficient Clustering for Fingerprint-Based Positioning

LoRaWAN: Lost for Localization?

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

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