LoRaWAN fingerprinting with K-Means: the relevance of clusters visual inspection

Abstract

LoRaWAN-based positioning is emerging as an alternative positioning solution for battery-constrained IoT devices or GNSS-denied areas in urban environments. The data collected at the LoRaWAN Base Stations, such as the RSSI of received messages, can be merged to generate an RF fingerprint. Unsupervised crowdsourcing can be leveraged to build a large radio map covering a urban area at the expense of introducing noise of around tens of meters when labelling the reference data. As fingerprinting may have a low efficiency in a such a dense radio map, we propose to use K-Means clustering to make the position estimation faster. During our study, we found that clustering can also be used to detect large outliers in the radio map that can be subject to be removed. The rationale is to identify those samples within the cluster that are far from the geometric centroid of the cluster. This paper introduces the analysis of introducing K-Means clustering with outlier detection and the benefits it might bring. Although removing outliers have not had an outstanding increase in the positioning accuracy, the performed analysis has enabled a new metric that is moderately correlated with the positioning error. This correlation may be useful to detect unreliable position estimates and discard them. The results presented in this work, based on two LoRaWAN datasets, show that the average and median positioning error can be improved by 5 % to 10 % by discarding 4 % to 6 % of operational samples.