Asynchronous sensor networks for Nodal water demand estimation in water distribution systems based on sensor grouping analysis

Abstract

Real-time hydraulic models are important tools for the management of water distribution systems (WDS). In such systems, high frequency nodal water demands are required for real-time modeling. Due to the limitations of the sensors' technology and power supply, the sensors distributed in the WDS typically upload data at a low frequency. However, replacing all existing low frequency sensors with new high frequency sensors is not cost effective. To solve this problem, an asynchronous data uploading strategy is proposed, which uses current low frequency sensors to estimate nodal water demand at high frequency while maintaining model accuracy. Based on an innovative clustering algorithm, the method splits the information redundancy sensors into multiple groups. Sensors in different groups asynchronously upload data at different time points to estimate nodal water demand. Applications to a simple hypothetical WDS and a realistic WDS demonstrate that the developed approach can efficiently improve the data upload frequency of the sensor network, thus boosting the demand estimation frequency. The developed method is expected to reduce the cost of upgrading sensor networks and increase the efficiency of WDS modeling, thus facilitating the cleaner production and sustainable management of WDS.