论文标题
分布式层次边缘计算中的物联网数据的上下文伴用异常检测
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
论文作者
论文摘要
深度神经网络（DNN）的进展极大地支持了异常IOT数据的实时检测。但是，物联网设备几乎无法负担得起复杂的DNN型号，并且将异常检测任务卸载到云中会产生长时间的延迟。在本文中，我们为分布式层次边缘计算（HEC）系统提出并构建了一种自适应异常检测方法，以解决该问题，以解决单变量和多元IOT数据。首先，我们构建了具有增加复杂性的多个异常检测DNN模型，并将每个模型与从底部到顶部的HEC中的一个层相关联。然后，我们根据从每个输入数据中提取的上下文信息设计了一种自适应方案，可以随时选择这些模型之一。该模型选择是作为一个以单步马尔可夫决策过程为特征的上下文强盗问题，并使用加固学习政策网络解决。我们构建HEC测试台，实施我们的建议方法，并使用真实的物联网数据集对其进行评估。该演示表明，与将检测任务的将检测任务相比，我们提出的方法可显着将检测延迟（例如，单变量数据集）显着减少71.4％）。我们还将其与其他基线方案进行了比较，并证明它实现了最佳准确性 - 折衷。我们的演示也可以在线获得：https：//rebrand.ly/91A71
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff. Our demo is also available online: https://rebrand.ly/91a71
