Title: An IoT-based scalable river level monitoring platform

Authors: Juan Acosta; Diego Mendez; Daniel Moreno; German Montanez; Luis Trujillo; Mauricio Escobar; Ignacio Gonzalez

Addresses: Departments of Electronics and Industrial Engineering, Pontificia Universidad Javeriana, Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT), Bogotá, Colombia ' Departments of Electronics and Industrial Engineering, Pontificia Universidad Javeriana, Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT), Bogotá, Colombia ' Departments of Electronics and Industrial Engineering, Pontificia Universidad Javeriana, Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT), Bogotá, Colombia ' Departments of Electronics and Industrial Engineering, Pontificia Universidad Javeriana, Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT), Bogotá, Colombia ' Departments of Electronics and Industrial Engineering, Pontificia Universidad Javeriana, Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT), Bogotá, Colombia ' Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare, Rionegro, Colombia ' Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare, Rionegro, Colombia

Abstract: Due to extreme natural conditions, there is a clear necessity of cost-effective and scalable solutions to continuously monitor the environmental conditions in order to take precautionary actions. In this paper, we present the design and implementation of an IoT-based solution for river level monitoring. The implemented system integrates 31 stations with communication capabilities to monitor the status of the Negro and Nare rivers. An embedded system has been designed and implemented from scratch into a single PCB, integrating ultrasonic sensors and power management. In order to consolidate and visualise the data, a cloud platform has been implemented, which is also in charge of generating alerts to local authorities and the population in general. The stations have an autonomy of 22 days in case of complete solar panel damage. The experimental results show that the river dynamics are perfectly captured in real-time allowing the authorities to timely warn the population.

Keywords: embedded system; early warning system; floods; river level.

DOI: 10.1504/IJSNET.2022.10046922

International Journal of Sensor Networks, 2022 Vol.38 No.4, pp.263 - 272

Received: 10 Jun 2021
Accepted: 11 Jun 2021

Published online: 03 May 2022 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article