DECO: False Data Detection and Correction Framework for Participatory Sensing


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.

Meeting Name

IEEE 23rd International Symposium on Quality of Service, IWQoS 2015 (2015: Jun. 15-16, Portland, OR)


Computer Science


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).

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)


Document Type

Article - Conference proceedings

Document Version


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© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2015