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LoRa RSSI Based Outdoor Localization in an Urban Area Using Random Neural Networks

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Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 284))

Abstract

The concept of the Internet of Things (IoT) has led to the interconnection of a significant number of devices and has impacted several applications in smart cities’ development. Localization is widely done using Global Positioning System (GPS). However, with large scale wireless sensor networks, GPS is limited by its high-power consumption and more hardware cost required. An energy-efficient localization system of wireless sensor nodes, especially in outdoor urban environments, is a research challenge with limited investigation. In this paper, an energy-efficient end device localization model based on LoRa Received Signal Strength Indicator (RSSI) is developed using Random Neural Networks (RNN). Various RNN architectures are used to evaluate the proposed model’s performance by applying different learning rates on real RSSI LoRa measurements collected in the urban area of Glasgow City. The proposed model is used to predict the 2D Cartesian position coordinates with a minimum mean localization error of 0.39 m.

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Acknowledgment

This work was funded by the Commonwealth Scholarships in the UK in partnership with the Government of Rwanda.

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Authors and Affiliations

  1. School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK

    Winfred Ingabire, Hadi Larijani & Ryan M. Gibson

  2. Department of Electrical and Electronics Engineering, University of Rwanda-College of Science and Technology, Kigali, Rwanda

    Winfred Ingabire

Authors
  1. Winfred Ingabire
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  2. Hadi Larijani
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  3. Ryan M. Gibson
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Corresponding author

Correspondence to Winfred Ingabire .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Ingabire, W., Larijani, H., Gibson, R.M. (2021). LoRa RSSI Based Outdoor Localization in an Urban Area Using Random Neural Networks. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_72

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