LoRa RSSI based outdoor localization in an urban area using random neural networks

Winfred Ingabire; Hadi Larijani; Ryan M. Gibson

doi:10.1007/978-3-030-80126-7_72

LoRa RSSI based outdoor localization in an urban area using random neural networks

Winfred Ingabire^*, Hadi Larijani, Ryan M. Gibson

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

29 Downloads (Pure)

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 energyefficient 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.

Original language	English
Title of host publication	Intelligent Computing
Subtitle of host publication	Proceedings of the 2021 Computing Conference
Editors	Kohei Arai
Publisher	Springer
Pages	1032-1043
Number of pages	12
Volume	2
ISBN (Electronic)	9783030801267
ISBN (Print)	9783030801250
DOIs	https://doi.org/10.1007/978-3-030-80126-7_72
Publication status	Published - 7 Jul 2021
Event	Computing Conference 2021 - Virtual, London, United Kingdom Duration: 15 Jul 2021 → 16 Jul 2021 https://saiconference.com/Computing

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	284
ISSN (Print)	2376-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Computing Conference 2021
Country/Territory	United Kingdom
City	London
Period	15/07/21 → 16/07/21
Internet address	https://saiconference.com/Computing

Keywords

IoT, LoRaWAN, RSSI, localization, RNN

ASJC Scopus subject areas

Signal Processing
Control and Systems Engineering
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1007/978-3-030-80126-7_72

LoRa RSSI based outdoor localization in an urban area using random neural networks
This is a post-peer-review, pre-copyedit version of an article published in Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-80126-7_72
Author accepted manuscript, 859 KB

Cite this

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title = "LoRa RSSI based outdoor localization in an urban area using random neural networks",

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{\textquoteright} 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 energyefficient end device localization model based on LoRa Received SignalStrength Indicator (RSSI) is developed using Random Neural Networks (RNN). Various RNN architectures are used to evaluate the proposed model{\textquoteright}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.",

keywords = "IoT, LoRaWAN, RSSI, localization, RNN",

author = "Winfred Ingabire and Hadi Larijani and Gibson, {Ryan M.}",

note = "Acceptance from webpage. AAM: 12m embargo - Author confirmed AAM uploaded to Pure is final author version submitted to the conference (email in Conference Papers mailbox sub folder). ST 19/02/21; Computing Conference 2021 ; Conference date: 15-07-2021 Through 16-07-2021",

year = "2021",

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doi = "10.1007/978-3-030-80126-7_72",

language = "English",

isbn = "9783030801250",

volume = "2",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "1032--1043",

editor = "Kohei Arai",

booktitle = "Intelligent Computing",

url = "https://saiconference.com/Computing",

}

Ingabire, W, Larijani, H & Gibson, RM 2021, LoRa RSSI based outdoor localization in an urban area using random neural networks. in K Arai (ed.), Intelligent Computing: Proceedings of the 2021 Computing Conference. vol. 2, Lecture Notes in Networks and Systems, vol. 284, Springer, pp. 1032-1043, Computing Conference 2021, London, United Kingdom, 15/07/21. https://doi.org/10.1007/978-3-030-80126-7_72

LoRa RSSI based outdoor localization in an urban area using random neural networks. / Ingabire, Winfred; Larijani, Hadi ; Gibson, Ryan M.

Intelligent Computing: Proceedings of the 2021 Computing Conference. ed. / Kohei Arai. Vol. 2 Springer, 2021. p. 1032-1043 (Lecture Notes in Networks and Systems; Vol. 284).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - LoRa RSSI based outdoor localization in an urban area using random neural networks

AU - Ingabire, Winfred

AU - Larijani, Hadi

AU - Gibson, Ryan M.

N1 - Acceptance from webpage. AAM: 12m embargo - Author confirmed AAM uploaded to Pure is final author version submitted to the conference (email in Conference Papers mailbox sub folder). ST 19/02/21

PY - 2021/7/7

Y1 - 2021/7/7

N2 - 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 energyefficient end device localization model based on LoRa Received SignalStrength 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.

AB - 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 energyefficient end device localization model based on LoRa Received SignalStrength 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.

KW - IoT, LoRaWAN, RSSI, localization, RNN

U2 - 10.1007/978-3-030-80126-7_72

DO - 10.1007/978-3-030-80126-7_72

M3 - Conference contribution

SN - 9783030801250

VL - 2

T3 - Lecture Notes in Networks and Systems

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BT - Intelligent Computing

A2 - Arai, Kohei

PB - Springer

T2 - Computing Conference 2021

Y2 - 15 July 2021 through 16 July 2021

ER -

LoRa RSSI based outdoor localization in an urban area using random neural networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

Fingerprint

Cite this