Data-Quality-Aware Participant Selection Mechanism for Mobile Crowdsensing

Data quality assurance is one of the most critical challenges in the context of Mobile CrowdSensing (MCS). How to effectively select appropriate participants from large-scale candidates to perform sensing tasks while satisfying certain constraint is a problem to be solved. Motivated by this, this paper studies the problem of data-quality-aware participant selection for MCS. Firstly, we propose a quality-aware participant reputation model by introducing active factor to lay a theoretical foundation. Secondly, we present a Multi-Stage Decision solution based on Greedy strategy (MSD-G) to optimize the pending problem while satisfying certain data quality constraint. Extensive simulations over a real dataset verify that our proposed MSD-G can effectively realize participant selection with ideal recruitment cost and sensing data quality.