QITA: Quality inference based task assignment in mobile crowdsensing

Abstract

With the rapid proliferation of mobile devices, Mobile Crowdsensing (MCS) has become an efficient way to ubiquitously sense and share environment data. Due to the openness of MCS, sensors and workers are of different qualities. Low quality sensors and workers may yield low sensing quality. Thus it is important to infer workers’ qualities and seek a valid task assignment with enough total qualities for MCS. To solve the quality inference problem, we adopt truth inference methods to iteratively infer workers’ qualities. This paper also proposes an task assignment problem called quality-bounded task assignment with redundancy constraint (QTAR) based on truth inference. We prove that QTAR is NP-complete and propose a (2+∈) - approximation algorithm QTA for QTAR. Finally, experiments conducted on real dataset prove the efficiency and effectiveness of the algorithms.