Affiliation:
1. School of Electronic and Information Engineering, Beijing Jiaotong University, China
2. Research Centre for Intelligent Healthcare, Coventry University, United Kingdom
3. School of Software Engineering, Beijing Jiaotong University, China
Abstract
Data imputation is prevalent in crowdsensing, especially for Internet of Things (IoT) devices. On the one hand, data collected from sensors will inevitably be affected or damaged by unpredictability. On the other hand, extending the active time of sensor networks has urgently aspired environmental monitoring. Using neural networks to design a data imputation algorithm can take advantage of the prior information stored in the models. This paper proposes a preprocessing algorithm to extract a subset for training a neural network on an IoT dataset, including time window determination, sensor aggregation, sensor exclusion and data frame shape selection. Moreover, we propose a data imputation algorithm using deep compressed sensing with generative models. It explores novel representation matrices and can impute data in the case of a high missing ratio situation. Finally, we test our subset extraction algorithm and data imputation algorithm on the EPFL SensorScope dataset, respectively, and they effectively improve the accuracy and robustness even with extreme data loss.
Funder
National Natural Science Foundation of China
Open Research Fund of the State Key Laboratory of Integrated Services Networks
Beijing NSF
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
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