Adaptive QoS‐Aware Cloud–Edge Collaborative Architecture for Real‐Time Smart Water Service Management

As urban water network being more and more intelligent, it becomes an urgent problem to solve how to execute resource scheduling and data process in an efficient and adaptable way and not affect real time and service quality. In this paper, we present an Adaptive QoS-Aware Cloud–Edge Collaborative Architecture (A-QCEA) empowered by a combined edge computing), cloud infrastructure, IoT perception network, AI-enhegrated optimization mechanism, and network security strategy to facilitate real-time smart water service management system of next generation. It is highly extended based on the cloud-edge-device three-layer IoT model which exists, and a multi-layer dynamic QoS-aware scheduling scheme and an edge-first data processing strategy are put forward. One is using a small inference engine, which is deployed on the edge device to achieve local data preprocessing and anomaly detection on the water quality and flow rate, thus, reducing data upload delay and cloud load. Second, the cloud layer utilizes flexible containerized computing system, and employs deep reinforcement learning model to forecast the IoT task load and dynamically allocate the computing, storage and network resources according to the different QoS level. Subsequently, low-power IoT communications protocols are implemented for ensuring reliable sensor and pumping station data backhaul to the infrastructure. Thereafter, end-to-end data encryption and edge access control methods are employed in the system, to ensure secure control commands transportation for water utilities. Lastly, in the scheduling core, we innovatively designed a fusion model with graph attention network(GAT) and time-aware LSTM to achieve the context-aware collaborative perception and resource prediction based on multi-source data, and offer the intelligent support for QoS level optimization. Experimental results demonstrate that the developed architecture achieves a remarkable improvement in task response time, resource utilization, and service continuity in the water environment.