Data Quality Aware Task Allocation Under a Feasible Budget in Mobile Crowdsensing

Abstract

Satisfying spatial-temporal coverage requirement in the interested regions while considering the quality of the sensing data with budget limitation is a major research challenge in mobile crowdsensing. Most existing research in this field focus on the number of sensor readings collected in each covered subarea and do not consider individual differences of participants for contributing to data quality improvement. In this paper, we propose a novel coverage metric, quality coverage, which considers both the spatial coverage and the quality of sensing data and then use task allocation approaches to achieve highly diverse and spatial quality coverage level within a limited budget for different application scenarios.