@inproceedings{f566fe4bb7344099999c0195f558d375,
title = "QualityDeepSense: Quality-aware deep learning framework for internet of things applications with sensor-temporal attention",
abstract = "Deep neural networks are becoming increasingly popular in mobile sensing and computing applications. Their capability of fusing multiple sensor inputs and extracting temporal relationships can enhance intelligence in a wide range of applications. One key problem however is the noisy on-device sensors, whose characters are heterogeneous and varying over time. The existing mobile deep learning frameworks usually treat every sensor input equally over time, lacking the ability of identifying and exploiting the heterogeneity of sensor noise. In this work, we propose QualityDeepSense, a deep learning framework that can automatically balance the contribution of sensor inputs over time by their sensing qualities. We propose a sensor-temporal attention mechanism to learn the dependencies among sensor inputs over time. These correlations are used to infer the qualities and reassign the contribution of sensor inputs. QualityDeepSense can thus focus on more informative sensor inputs for prediction. We demonstrate the effectiveness of QualityDeepSense using the noise-augmented heterogeneous human activity recognition task. QualityDeepSense outperforms the state-of-the-art methods by a clear margin. In addition, we show QualityDeepSense only impose limited resource-consumption burden on embedded devices.",
keywords = "Deep Learning, Internet of Things, Mobile Computing, Sensing Quality",
author = "Shuochao Yao and Shaohan Hu and Yiran Zhao and Tarek Abdelzaher",
note = "Funding Information: Research reported in this paper was sponsored in part by NSF under grants CNS 16-18627 and CNS 13-20209 and in part by the Army Research Laboratory under Cooperative Agreements W911NF-09-2-0053 and W911NF-17-2-0196. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, NSF, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2nd International Workshop on Embedded and Mobile Deep Learning, EMDL 2018 ; Conference date: 15-06-2018",
year = "2018",
month = jun,
day = "15",
doi = "10.1145/3212725.3212729",
language = "English (US)",
series = "EMDL 2018 - Proceedings of the 2018 International Workshop on Embedded and Mobile Deep Learning",
publisher = "Association for Computing Machinery, Inc",
pages = "42--47",
booktitle = "EMDL 2018 - Proceedings of the 2018 International Workshop on Embedded and Mobile Deep Learning",
}