Moving-update Kalman Algorithm in Low-cost Node-Red IoT Network for Estimating Flood Water Level

Abstract

Flooding is one of the most common natural disasters in Vietnam. Although a hydrological monitoring system has been developed in Vietnam, the adoption of a Flood Warning and Monitoring System (FWMS) is still limited. A practical issue is that the river water levels is rarely flat, but undulating with flood water ripples, which makes the measurement inaccurate. In this paper, we will design a recursive Kalman estimation for fluctuating flood water level in the Node-Red IoT network. Indeed, the low complexity of the popular Kalman filter algorithm is very suitable for a low-cost IoT system like Node-Red. In our experiments, the accuracy of our Kalman algorithm is far superior to the standard Moving Average (MA) algorithm. To our knowledge, this is the first time that the Kalman filter has been used in a practical Node-Red IoT experiment. We will show that our novel Moving-update Kalman algorithm, which combines MA and Kalman methods, can track data recursively without prior knowledge of noise’s variance. Our novel algorithm is of linear complexity and, hence, fast enough for low cost IoT and FWMS systems in developing countries like Vietnam. We also included the industrial Message Queuing Telemetry Transport (MQTT) protocol in IoT network in our Node-Red system, which means our designed Node-Red proposal is capable of transferring data to any FWMS network via internet. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.