ECOMSNet – An edge computing-based sensory network for real-time water level prediction and correction. (September 2020) - Yang, Tsun-Hua; Wang, Chia-Wei; Lin, Sheng-Jhe
Abstract: Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), for water level monitoring and prediction. A sensor-embedded algorithm integrates the direct step method (DSM) with a microgenetic algorithm (MGA). This algorithm predicts the water surface profile and corrects it once water level observations are available. It also meets efficiency requirements to accommodate sensor computation limitations. The errors in the predicted water surface profiles in channels with gradually varied flows are 5% in a laboratory flume experiment and below 10% in a field experiment. The ECOMSNet is an achievement of edge computing-based Internet of Things. It shows potential to increase emergency response efficiency. However, the system requires further refinement and testing if it is to adequately address rapidly varied unsteady flow in a scaled-up implementation. Highlights: An innovative decentralized early warning system (EWS) applies edge computing technique to provide real-time water level forecasts and corrections at local sensors. Through integration with IoT technology such as on-site microcomputer-based sensors, the edge computing-based IoT, or ECIoT, can collect and process data and then generate decision-supporting information simultaneously, without delay. Real-time correction can be done seamlessly to improve the performance since observations and calculations are done at the edge of the system.