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 proceedingConference contribution


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 languageEnglish
Title of host publicationSAI Computing Conference 2021
Number of pages13
Publication statusAccepted/In press - 18 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


  • IoT, LoRaWAN, RSSI, localization, RNN

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