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Location-based services (LBS) such as LoRa geolocation are important aspects of IoT applications. In this article, we propose a hierarchical clustering-based technique for urban vehicle localization using received signal strength indicator (RSSI) measurements in a public LoRaWan network. The solution relies on a two-layer hierarchy: the first layer consists of a K -Means clustering to partition a large urban area into several regions based on geographical coordinates of the datapoints. A coarse localizer utilizes kernel density estimation to model the received signal distribution of each gateway and determine in which the most probable regions of interests the vehicle is located, followed by a finer localization step at the second layer. For each region, reference points are grouped based on the similarity between the gateway coverage vectors. A spatial kernel-based fingerprint method that adopts spatial co-location patterns between neighbors is introduced to provide support for furthe

Hierarchical clusteringEhicle localization