Urban vehicle localization in public LoRaWan network

Yan Li*, Johan Barthelemy, Shuai Sun, Pascal Perez, Bill Moran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

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 further fine granularity positioning within each region. The Kullback-Leibler divergence is used to measure similarities between observations and fingerprints, and the final position estimation is based on a weighted kernel regression model. The system is evaluated using a publicly available LoRaWan data set collected in large urban areas in the city of Antwerp, Belgium. We are able to achieve a median error of 158.41 m and a mean error of 346.03 m based on the raw LoRa RSSI data, and it is reported as the best accuracy based on the same data set in the literature.

Original languageEnglish
Pages (from-to)10283-10294
Number of pages12
JournalIEEE Internet of Things Journal
Volume9
Issue number12
DOIs
Publication statusPublished - 15 Jun 2022
Externally publishedYes

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