Anomaly Detection for MEC Enabled Hierarchical Industrial IoT With Transformer Enhanced Variational Auto Encoder

Abstract

Most existing works in Industrial Internet of Things (IIoT) anomaly detection either depend on computationally intensive models that exceed the capabilities of multiaccess edge computing (MEC) servers, or lightweight models that lack robustness, making them unadaptable in IIoT infrastructures. To address these challenges, we propose THREADS, a hierarchical anomaly detection framework tailored for IIoT applications. The Instance thread utilizes an efficient variational auto encoder to produce instant feedback and offloads most of the workload to MECs. On the other hand, the Shadow thread employs an attention-enhanced transformer discriminator to examine low-confidence results in the cloud. Experimental results on five large-scale datasets show THREADS achieves an average F1-Score of 0.8537 in the hierarchical mode where most of the workloads are handled by MECs, and the random access memory and CPU usage is reduced by up to 29% and 88%, respectively. Meanwhile, THREADS achieves an F1-Score of 0.8563 in a cloud-based mode, consistently outperforming state-of-the-art approaches.