Compressive Sensing based Data Quality Improvement for Crowd-Sensing Applications
Crowd-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 crowd-sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth an important issue of false data detection and correction in crowd-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 crowd-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. Through comprehensive performance evaluations, we demonstrate the efficacy of DECO in achieving false data detection and correction for crowd-sensing applications with incomplete sensory data.
L. Cheng et al., "Compressive Sensing based Data Quality Improvement for Crowd-Sensing Applications," Journal of Network and Computer Applications, vol. 77, pp. 123-134, Academic Press, Jan 2017.
The definitive version is available at https://doi.org/10.1016/j.jnca.2016.10.004
Center for High Performance Computing Research
Keywords and Phrases
Compressed sensing; Signal reconstruction; Comprehensive performance evaluation; Compressive sensing; Crowd-sensing; Data corrections; False data; Missing values; Sensing applications; Spatio temporal; Channel estimation; False data detection and correction; Spatio-temporal compressive sensing
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Academic Press, All rights reserved.
01 Jan 2017