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Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Xiangmao Chang1, 2, *, Yuan Qiu1, Shangting Su1, Deliang Yang3
1 Nanjing University of Aeronautics and Astronautics, Nanjing, China.
2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China.
3 Michigan State University, Lansing, USA.
* Corresponding Author: Xiangmao Chang. Email: .

Computers, Materials & Continua 2020, 63(2), 691-703. https://doi.org/10.32604/cmc.2020.07923

Received 11 July 2019; Accepted 31 July 2019; Issue published 01 May 2020

Abstract

Wireless sensor networks are increasingly used in sensitive event monitoring. However, various abnormal data generated by sensors greatly decrease the accuracy of the event detection. Although many methods have been proposed to deal with the abnormal data, they generally detect and/or repair all abnormal data without further differentiate. Actually, besides the abnormal data caused by events, it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources. Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events. In this paper, we propose a data cleaning approach based on Stacked Denoising Autoencoders (SDAE) and multisensor collaborations. We detect all abnormal data by SDAE, then differentiate the abnormal data by multi-sensor collaborations. The abnormal data caused by events are unchanged, while the abnormal data caused by other factors are repaired. Real data based simulations show the efficiency of the proposed approach.

Keywords

Data cleaning, wireless sensor networks, stacked denoising autoencoders, multi-sensor collaborations.

Cite This Article

X. Chang, Y. Qiu, S. Su and D. Yang, "Data cleaning based on stacked denoising autoencoders and multi-sensor collaborations," Computers, Materials & Continua, vol. 63, no.2, pp. 691–703, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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