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.

提出了一种基于层级边缘计算系统的自适应异常检测方法，使用三个不同复杂度的 DNN 模型分别对物联网设备、边缘服务器和云进行检测，并设计了一种基于上下文信息提取的自适应方案，以同时实现高检测准确性和低检测延迟。实验结果表明与数据离线到云端处理相比，能将检测延迟降低 84%，同时保持准确性。