A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities

Abstract

Sparse mobile crowdsensing (SMCS) has emerged as a promising sensing paradigm for urban sensing, leveraging the spatial and temporal correlation among data sensed in distinct sub-areas to cut sensing expenses dramatically. It intelligently selects only a tiny portion of the target regions for sensing and accurately infers the data for the remaining unsensed areas. SMCS confronts numerous challenges, such as sensing cell selection and missing data inference, when compared to mobile crowdsensing. Researchers in recent years have proposed plenty of strategies to solve these challenges. From the perspective of comparing MCS, we aim to provide a comprehensive literature review of recent advances in SMCS in this paper. We begin by going over the preliminary of SMCS and MCS, including their evolution, characteristics, and life-cycle stages. We then go through their common key techniques and recent developments. Furthermore, we give a review of the unique key techniques as well as the most recent advancements. We finally identify existing applications and highlight potential research opportunities for SMCS. Our objective is to provide researchers with a comprehensive understanding of SMCS.

Keywords

• Sparse mobile crowdsensing
• participant recruitment
• privacy protection
• incentive mechanism