Primary-Attribute-Migration-Based Anomalous Event Detection in Digital-Twin-Enabled Device-Edge–Cloud Network

Detection of anomalous event at the edge of network has attracted wide attention from both academic and industrial fields recently. During the detection process, several primary sensing attributes are jointly utilized to determine whether an anomalous event occurs or not. However, as the primary attributes of some Internet of Things (IoT) devices are easy missing due to the natural wear and they cannot be timely and accurately accessed, the event detection efficiency is very low. In view of this, our work introduces a digital twin (DT)-assisted detection technology for anomaly identification in a device-edge–cloud architecture. Specifically, for an edge server with missing primary attributes, the probability of anomalous event occurring on it can be calculated by analyzing the primary attribute fusion values of its adjacent edge servers. As a result, it is unnecessary to carry on detection in advance on the edge servers with a low anomaly occurring probability, efficiently reducing the detection cost. For the remaining edge servers with a high probability, the primary attributes with high accuracy are migrated by considering the difference on the historical value variant trend and the fusion effect. Based on this, a decision tree will be built in the integrated DT model for anomalous event detection in advance. Further, the cloud collects other relevant attributes to build a random forest for the final identification and judgment of anomalous events. Experimental results show that our method achieves a higher detection performance in terms of energy consumption, detection time, and accuracy by at least 37.1%, 39.5%, and 1.82% compared to the baselines.