DECO: False Data Detection and Correction Framework for Participatory Sensing
Abstract
Participatory sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of participatory sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth the important issues of false data detection and correction in participatory sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose DECO, a general framework to detect false values for participatory sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, DECO is able to accurately detect the false data and estimate both false and missing values for data correction. We validate our design through an experimental case study.
Recommended Citation
L. Cheng et al., "DECO: False Data Detection and Correction Framework for Participatory Sensing," Proceedings of the IEEE 23rd International Symposium on Quality of Service (2015, Portland, OR), pp. 213 - 218, Institute of Electrical and Electronics Engineers (IEEE), Jun 2015.
The definitive version is available at https://doi.org/10.1109/IWQoS.2015.7404736
Meeting Name
IEEE 23rd International Symposium on Quality of Service, IWQoS 2015 (2015: Jun. 15-16, Portland, OR)
Department(s)
Computer Science
Keywords and Phrases
Compressed sensing; Quality of service; Data corrections; Data quality; False data; Missing data; Missing values; Participatory Sensing; Spatio temporal; Channel estimation
International Standard Book Number (ISBN)
978-1-4673-7113-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 2015
Comments
This work was supported in part by the National Natural Science Foundation of China (61170296, 61190125, 61300174, 61303202), 973 Program (2013CB035503), China Postdoctoral Science Foundation (2013M530511, 2014T70026, 2014M560334), and Open Foundation of State Key Lab of Networking & Switching Tech. (Beijing Univ. of Posts & Telecomm., SKLNST-2013-1-02).