.png){kind=icon}
.png){kind=icon}
Return
Compressive sensing based data quality improvement for crowd-sensing applications
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jnca.2016.10.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
False data detection and correction
Crowd-sensing
Spatio-temporal compressive sensing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
3.6K
Citations:
1.1W
Organization
View more organizations