Open Access
ARTICLE
Preserving the Efficiency and Quality of Contributed Data in MCS via User and Task Profiling
Dingwen Wang, Ming Zhao*
School of Computer Science and Engineering, Central South University, Changsha, 410082, China
* Corresponding Author: Ming Zhao. Email:
Journal of Cyber Security 2020, 2(2), 63-68. https://doi.org/10.32604/jcs.2020.07229
Received 09 May 2019; Accepted 09 June 2020; Issue published 14 July 2020
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.
Keywords
Cite This Article
D. Wang and M. Zhao, "Preserving the efficiency and quality of contributed data in mcs via user and task profiling,"
Journal of Cyber Security, vol. 2, no.2, pp. 63–68, 2020.