E-STG-DiffNet: Multimodal Spatio-Temporal Graph Diffusion for Edge–Cloud Anomaly Detection

Video anomaly detection is critical for information security in IoT-based intelligent surveillance, where heterogeneous sensors continuously measure the physical environment and anomalies manifest as deviations from learned normal behavior. Existing methods, however, still struggle to jointly model long-range spatio-temporal dependencies and to fuse heterogeneous multimodal measurements. Deploying such detectors on resource-constrained edge devices remains a further practical barrier. To address these challenges, we propose E-STG-DiffNet, an edge–cloud collaborative multimodal spatio-temporal graph diffusion network that formulates secure IoT surveillance as a network measurement problem. The system employs a Video Swin Transformer to extract multi-scale visual features with linear complexity. A dynamic adaptive spatio-temporal graph convolutional network (ST-GCN) with trainable node embeddings then fuses the heterogeneous sensor measurements in non-Euclidean space, and a masked latent diffusion model (MLDM) combines masked context reconstruction with stochastic denoising for anomaly scoring. A confidence-aware edge–cloud collaborative inference mechanism, supported by knowledge distillation, enables low-latency deployment. On UCSD Ped2, CUHK Avenue, and ShanghaiTech, E-STG-DiffNet reaches frame-level micro-AUC scores of 98.1%, 90.4%, and 78.3%, respectively, competitive with the strongest recent methods on these benchmarks. All results are averaged over three random seeds. Ablation studies confirm that each core component contributes to the overall performance. In the efficiency analysis, the system sustains 26.3 FPS with a mean end-to-end latency of 43 ms (99th percentile: 88 ms), while only about 10% of frames are offloaded to the cloud as compressed latents, about 0.03% of the raw-video uplink traffic. Because the public benchmarks used in our experiments do not contain real non-visual sensor streams, the temperature, vibration, and acoustic modalities are simulated following an established protocol. The reported results therefore primarily validate the video anomaly detection capability of the framework, and validation on real multimodal sensor networks is left to future work.