  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
