LoRa-based outdoor localization and tracking using unsupervised symbolization

Khondoker Ziaul Islam; David Murray; Dean Diepeveen; Michael G.K. Jones; Ferdous Sohel

doi:10.1016/j.iot.2023.101016

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LoRa-based outdoor localization and tracking using unsupervised symbolization

Journal article

Open access

Peer reviewed

LoRa-based outdoor localization and tracking using unsupervised symbolization

Khondoker Ziaul Islam, David Murray, Dean Diepeveen, Michael G.K. Jones and Ferdous Sohel

Internet of Things, Vol.25, 101016

2024

DOI: https://doi.org/10.1016/j.iot.2023.101016

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Abstract

LoRa localization

RSSI

Path loss

D-Markov machine

Chinese restaurant process

Maximum entropy partitioning

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

Details

Title: LoRa-based outdoor localization and tracking using unsupervised symbolization
Authors/Creators: Khondoker Ziaul Islam - Murdoch University
David Murray
Dean Diepeveen
Michael G.K. Jones - Murdoch University
Ferdous Sohel - Murdoch University, Centre for Crop and Food Innovation
Publication Details: Internet of Things, Vol.25, 101016
Publisher: Elsevier B.V.
Identifiers: 991005625167907891
Murdoch Affiliation: Centre for Crop and Food Innovation; School of Information Technology; State Agricultural Biotechnology Centre
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.13 Telecommunications; 4.13.696 Wireless Localization
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
ESI research areas: Computer Science