EMD-EmLSTM: A QoS Analysis and Prediction Method for Industrial Internet of Things

The industrial IoT communication environment is complex and diverse. There are many running industrial production equipment in the intelligent factory based on the Industrial Internet of Things. At the same time, a large number of servers are deployed in different areas of the factory to control the production of industrial equipment and user interfacing. These servers work together to provide a variety of services to industrial equipment and users. In intelligent factory communication architecture, multiple servers can provide similar or identical services to a single user or industrial device. However, the characteristics of the intelligent factory network based on the Industrial Internet of Things (IIoT) are dynamic and changeable. And due to space and time factors, the dynamic QoS properties of each server node in the industrial IoT are unstable. These factors make it difficult for users to select the service that meets their needs in the candidate service set and increase the task processing latency of communication nodes. To address the above issues, we focus on QoS analysis and prediction methods for industrial IoT and propose an enhanced QoS prediction model based on EMD-mLSTM (EMD-EmLSTM). This model combines EMD (Empirical Mode Decomposition) with LSTM (Long Short-Term Memory) methods. We also design the multivariate data input model to mine the potential association of QoS time series data from a finer granularity level. In the data preprocessing section, we propose an abnormal data processing method, which uses the isolated forest algorithm to detect outliers in the dataset. Meanwhile, a residual correction prediction model based on LSTM is constructed. This auxiliary model can correct the prediction results by residual prediction. It can help the model to enhance the QoS prediction accuracy. The experimental results show that the EMD-EmLSTM model can achieve single-step and multi-step prediction of dynamic QoS attributes of industrial IoT. The prediction effect is better than other methods. Moreover, our proposed method is able to select the optimal service from the set of candidate services. It effectively reduces the node task processing delay and ensures the best quality of service in a certain time period of the network nodes.