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Smart city article details

Title	Lora Rssi Based Outdoor Localization In An Urban Area Using Random Neural Networks
ID_Doc	35618
Authors	Ingabire W.; Larijani H.; Gibson R.M.
Year	2021
Published	Intelligent Computing - Proceedings of the 2021 Computing Conference
DOI	http://dx.doi.org/10.1007/978-3-030-80126-7_72
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Author Keywords	IoT; Localization; LoRaWAN; RNN; RSSI

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