@Article{jcs.2020.07229,
AUTHOR = {Dingwen Wang, Ming Zhao},
TITLE = {Preserving the Efficiency and Quality of Contributed Data in MCS via User and Task Profiling},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {63--68},
URL = {http://www.techscience.com/JCS/v2n2/39506},
ISSN = {2579-0064},
ABSTRACT = {Mobile crowdsensing is a new paradigm with powerful performance 
for data collection through a large number of smart devices. It is essential to 
obtain high quality data in crowdsensing campaign. Most of the existing specs 
ignore users’ diversity, focus on solving complicated optimization problem, and 
consider devices as instances of intelligent software agents which can make 
reasonable choices on behalf of users. Thus, the efficiency and quality of 
contributed data cannot be preserved simultaneously. In this paper, we propose a 
new scheme for improving the quality of contributed data, which recommends 
tasks to users based on calculated score that jointly take the matching degree and 
task’s rationality into account. We design QIM as Quality Investigation 
Mechanism for profiling tasks’ rationality and matching degree, which draw on 
support vector machine (SVM) to learn it from historical data. Our mechanism is 
validated against the examination in experiment, and the evaluation demonstrates 
that the QIM mechanism achieves a better performance while improving 
efficiency E and quality Q at the same time compared with benchmarks.},
DOI = {10.32604/jcs.2020.07229}
}