The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this paper, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network. We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices, implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
翻译:深心神经网络的进步大大加强了对IoT应用中的异常数据的实时检测。然而,复杂-准确-不透明-不相干进化的两难困境依然存在:复杂的DNN模型提供更准确性,但典型的IOT设备无法承担计算负荷,而卸载到云层的补救也长期拖延。在本文件中,我们通过提出一个适应性异常检测计划,用等级边缘计算(HEC)来应对这一挑战。具体地说,我们首先建立多重异常检测 DNN模型,其复杂性越来越大,并将其中的每个模型与相应的 HEC层联系起来。然后,我们设计一个适应性模型选择计划,作为背景型号问题来制定,并通过使用强化学习政策网络加以解决。我们还采用了平行政策培训方法,利用分布模型加快培训进程。我们用真正的IOT设备来建立HEC测试台,用单向和多变量的IOT数据集来实施和评估我们的背景带带宽的方法。 与基线和状态模型相比,我们的适应性模型只能用最佳的精确度和最准确性模型来打击最佳的FDRED数据库。