Missing data estimation and IoT‐based flyby monitoring of a water distribution system: Conceptual and experimental validation

• Published in: International journal of communication systems 2023-08, Vol.36 (12), p.n/a
• Main Authors: Sankaranarayanan, S., Swaminathan, G., Radhakrishnan, T.K., Sivakumaran, N.
• Format: Article
• Language: English
• Subjects: Algorithms; customized Kalman filter; distribution system; Estimation; Fault location; flyby monitoring; Internet of Things; IoT; Kalman filters; Missing data; Sampling; Sensors; Water distribution; Water engineering; Water supply
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4135

Summary Achieving uninterrupted water supply to the consumer node is considered to be the significant aspect for urban water distribution system (WDS). Under circumstances such as sensor failures or large sampling interval, the intermittent vital data are ignored, which lead to the missing data problem. In this work, an enhanced version of Kalman filter (KF) is proposed termed to be customized KF (CKF). The proposed CKF is equipped to handle the state‐ and input‐dependent noises, which are amplified based on the input and states in WDS. These noises corrupt the measured response from the WDS added to the conventional sensor and model uncertainties. In this case, a real‐world‐existing WDS is considered to test the credibility of the proposed algorithm. This reduces the complexity of the computation and the sampling rate of the measured head level, and the flows are considered to be random. The study indicates that the proposed CKF performs better in estimating the missing data voids with the noise‐corrupted measurements. To have the continuous monitoring accessible in remote sections, the vital parameters are monitored through Internet of Things (IoT) in android platform. The estimated data from the MATLAB and the sensor data of the consumer demands are monitored through user‐defined android application. This technique provides the advantage of monitoring the WDS in flyby condition and also even in the presence of any sensor failures, which also helps to localize the fault location. Is constructed for monitoring and estimating the missing data instances in the water distribution system. The missing data are the undesirable effect in the WDS. IoT‐based android application is created for the flyby monitoring, which helps to have a cloud storage access and continuous monitoring. Customized Kalman filter includes the effect of state‐ and input‐dependent uncertainties in picture, which are generally prevailing in the WDS due to water hammer phenomenon.