Preserving the Efficiency and Quality of Contributed Data in MCS via User and Task Profiling

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.