DAStream: Efficient Drift-Adaptive Anomaly Detection for Streaming Data in Resource-Constrained Edge Intelligence

Abstract

Real-time anomaly detection in industrial IoT (IIoT) often requires processing continuous data streams on resource-constrained edge nodes while addressing non-stationary data distributions caused by changes in device operating states. Traditional statistical or distance-based methods typically rely on fixed thresholds or static models, making it hard to maintain stable performance under complex conditions. Deep learning approaches are computationally intensive, making them unsuitable for resource-constrained edge devices. Existing methods struggle to balance dynamic adaptability to data distributions with computational efficiency. This paper proposes DAStream, a streaming anomaly detection method for edge environments. We propose a dual-phase online clustering framework with statistical enhancement to improve model stability. We design an adaptive anomaly scoring method that uses time-varying statistics to capture dynamic drifts in the data distribution, combined with an online-updated normalized deviation metric. We also propose a lightweight self-calibrating discrimination mechanism to enable dynamic decision boundaries. DAStream relies solely on recursive statistical computations, ensuring constant computational complexity and enabling efficient deployment on devices such as FPGAs. Experiments on typical IIoT datasets and an FPGA platform show that DAStream achieves a $\text{6 2. 6 \%}$ increase in throughput and a 40.7% reduction in energy consumption while maintaining detection performance comparable to the state-of-the-art method, validating its effectiveness and feasibility for IIoT edge intelligence scenarios.