Multiple criteria-based efficient schemes for participants selection in mobile crowd sensing

Abstract

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establishes a new and fast-growing sensing paradigm to satisfy this need which is called mobile crowd sensing (MCS). In MCS, the optimality of sensory data quality may not be satisfied due to the existence of inexperienced users and uncoordinated task management. This paper proposes a novel participant selection schemes for enhancing the data quality in MCS. The proposed selection schemes minimise incentive payments by selecting some participants while still satisfying sensory data quality constraint. Multiple criteria factors are used to evaluate the data quality of candidate users for selecting a minimal set of users with the best data quality values by using fuzzy logic controller. These factors include a user experience and a quality of sensory units of a mobile device with each user. The experimental results by using synthetic and real data show that the proposed selection schemes can gather high-quality sensory data with low cost compared to existing schemes.