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Title	A Cloud-Based Data Storage And Visualization Tool For Smart City Iot: Flood Warning As An Example Application
ID_Doc	663
Authors	Leal Sobral V.A.; Nelson J.; Asmare L.; Mahmood A.; Mitchell G.; Tenkorang K.; Todd C.; Campbell B.; Goodall J.L.
Year	2023
Published	Smart Cities, 6, 3
DOI	http://dx.doi.org/10.3390/smartcities6030068
Abstract	Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia. © 2023 by the authors.
Author Keywords	AWS; cloud computing; cost analysis; data management; environmental monitoring; Internet of Things; LoRaWAN; smart cities

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