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Advances in deep neural networks (DNN) greatly bolster real-time detection ofanomalous IoT data. However, IoT devices can barely afford complex DNN modelsdue to limited computational power and energy supply. While one can offloadanomaly detection tasks to the cloud, it incurs long delay and requires largebandwidth when thousands of IoT devices stream data to the cloud concurrently.In this paper, we propose an adaptive anomaly detection approach forhierarchical 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., IoTdevices, edge servers, and cloud. Then, we design an adaptive scheme to selectone of the models based on the contextual information extracted from inputdata, to perform anomaly detection. The selection is formulated as a contextualbandit problem and is characterized by a single-step Markov decision process,with an objective of achieving high detection accuracy and low detection delaysimultaneously. We evaluate our proposed approach using a real IoT dataset, anddemonstrate that it reduces detection delay by 84% while maintaining almost thesame accuracy as compared to offloading detection tasks to the cloud. Inaddition, our evaluation also shows that it outperforms other baseline schemes.

Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing