Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth when thousands of IoT devices stream data to the cloud concurrently. In this paper, we propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. Specifically, we first construct three anomaly detection DNN models of increasing complexity, and associate them with the three layers of HEC from bottom to top, i.e., IoT devices, edge servers, and cloud. Then, we design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. The selection is formulated as a contextual bandit problem and is characterized by a single-step Markov decision process, with an objective of achieving high detection accuracy and low detection delay simultaneously. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud. In addition, our evaluation also shows that it outperforms other baseline schemes.

中文翻译：

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分层边缘计算中物联网数据的自适应异常检测

深度神经网络 (DNN) 的进步极大地促进了异常物联网数据的实时检测。然而，由于计算能力和能源供应有限，物联网设备几乎无法负担复杂的 DNN 模型。虽然可以将异常检测任务卸载到云端，但当数以千计的物联网设备同时将数据传输到云端时，它会产生很长的延迟并需要大带宽。在本文中，我们为分层边缘计算（HEC）系统提出了一种自适应异常检测方法来解决这个问题。具体来说，我们首先构建了三个复杂度越来越高的异常检测 DNN 模型，并将它们与 HEC 从下到上的三层关联起来，即 IoT 设备、边缘服务器和云。然后，我们设计了一种自适应方案，根据从输入数据中提取的上下文信息来选择模型之一，进行异常检测。该选择被表述为上下文老虎机问题，其特点是单步马尔可夫决策过程，目标是同时实现高检测精度和低检测延迟。我们使用真实的物联网数据集评估我们提出的方法，并证明与将检测任务卸载到云相比，它减少了 84% 的检测延迟，同时保持几乎相同的准确性。此外，我们的评估还表明它优于其他基线方案。并证明与将检测任务卸载到云相比，它减少了 84% 的检测延迟，同时保持几乎相同的准确度。此外，我们的评估还表明它优于其他基线方案。并证明与将检测任务卸载到云相比，它减少了 84% 的检测延迟，同时保持几乎相同的准确度。此外，我们的评估还表明它优于其他基线方案。