A Deep-Learning-Based Data-Management Scheme for Intelligent Control of Wastewater Treatment Processes Under Resource-Constrained IoT Systems

Effective data management schemes have always been the major demand in universal industrial Internet of Things (IoT) systems, especially in resource-constrained scenarios. In realistic wastewater treatment process (WTP), only limited monitoring data resource can be available due to some digital constraint. Aiming at this practical issue, this work explores utilization of deep neural network (DNN) to deal with such practical issue in the objective situation. Therefore, a deep-learning-based data-management scheme for intelligent control of WTP under resource-constrained IoT systems, is proposed in this article. First, a specific data encoding and preprocessing approach is developed for the objective business scenario. Then, the detailed workflow of a DNN structure is applied to predict key intermediate parameters which can further guide control decision. Finally, a comprehensive series of experiments are conducted on a real-world data set which covers a range of one year. Both efficiency and robustness of the proposal are tested by introducing several performance metrics. The results show that it can have proper prediction effect in such resource-constrained environment, which can facilitate following intelligent control operations.