A Diverse Noise-Resilient DNN Ensemble Model on Edge Devices for Time-Series Data

Many applications such as healthcare and transportation on edge devices will use deep neural network (DNN) prediction based on time-series data collected by the devices. However, the existence of noises in the on-device sensors negatively impacts the sensing output of the DNN models. The state-of-the-art time-series based DNN approaches can deal with Gaussian noise but cannot effectively handle other types of noises in spite of the existence of different types of noises such as shot, burst, transient noises, and their combination. In this paper, we propose an ensemble-based DNN model, namely E–Sense, which consists of different expert models for different noises and shows higher prediction accuracy. Since an edge device may have limited resources to run a large DNN model, we further propose a novel searching-based model compression method called E−Comp that uses knowledge distillation to compress E−Sense to a smaller DNN model while maintaining the accuracy. Our real experiments on live sensor data and trace-driven experiments on three real traces show that E–Sense outperforms other methods in accuracy, and E–Comp reduces 27% inference time without sacrificing accuracy compared with other DNN compression methods. We also distributed our source code.