Intelligent IoT Scheduling Mechanism Based on Data Traffic Prediction

To improve the efficiency of data collection, transmission and application in the electric power Internet of Things(IoT), many researches are devoted to resource allocation and scheduling algorithms. However, few studies focus on the impact of dynamic changes in data volume on decision-making. In this paper, we propose an intelligent IoT scheduling mechanism based on data traffic prediction. First, we propose an IoT data traffic prediction model(IoT-DTP) to accurately predict the future data volume. On this basis, we construct a data-driven IoT scheduling mechanism (PESM), which can realize higher real-time data transmission capability and faster service response. For instance, it can realize efficient data interaction of App launch, release and update in the intelligent IoT software platform. Finally, through theoretical analysis and experimental evaluation, the efficiency of the proposed method is verified.