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CC BY-NC V4.0, Open Access
Journal article
Machine learning-based LoRa localisation using multiple received signal features
IET Wireless Sensor Systems, Vol.13(4), pp.133-150
2023
Abstract
Low-power localisation systems are crucial for machine-to-machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range-based technique to estimate the distance of a target node from a LoRa gateway using machine-learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range-based distance mapping with trilateration and fingerprint-based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF-based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration-based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint-based direct location estimation approaches.
Details
- Title
- Machine learning-based LoRa localisation using multiple received signal features
- Authors/Creators
- Khondoker Islam - Murdoch UniversityDavid MurrayDean DiepeveenMichael G. K. Jones - Murdoch University, Centre for Crop and Food InnovationFerdous Sohel - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- IET Wireless Sensor Systems, Vol.13(4), pp.133-150
- Publisher
- John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
- Identifiers
- 991005585957507891
- Copyright
- © 2023 The Authors.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology
- Resource Type
- Journal article
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