Sparse mobile crowdsensing: challenges and opportunities

Sensing cost and data quality are two primary concerns in mobile crowd sensing. In this article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which leverages the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated, thus lowering overall sensing cost (e.g., smartphone energy consumption and incentives) while ensuring data quality. Sparse mobile crowdsensing applications intelligently select only a small portion of the target area for sensing while inferring the data of the remaining unsensed area with high accuracy. We discuss the fundamental research challenges in sparse mobile crowdsensing, and design a general framework with potential solutions to the challenges. To verify the effectiveness of the proposed framework, a sparse mobile crowdsensing prototype for temperature and traffic monitoring is implemented and evaluated. With several future research directions identified in sparse mobile crowdsensing, we expect that more research interests will be stimulated in this novel crowdsensing paradigm.

Introduction

With the prevalence of rich-sensor equipped smartphones in recent years, mobile crowdsensing (MCS) has become a promising paradigm to facilitate urban sensing applications, such as environment monitoring, traffic congestion detection, hotspot identification, and public information sharing [1]–[4]. Traditional urban sensing applications rely on the expensive specialized sensing infrastructure (e.g., air quality monitoring stations); however, only utilizing the specialized sensing infrastructure to enable urban sensing applications has some pitfalls such as high deployment/maintenance cost and lack of reusability across multiple applications, which hinder the rapid growth of heterogeneous urban sensing applications to a large scale. Complementary to the traditional sensing paradigm, MCS helps to achieve the urban sensing goal by leveraging the mobility of mobile users, the sensors embedded in mobile phones, and the existing wireless infrastructure to sense and collect environment data, making it possible to inexpensively sense various urban data in regions that are not covered by the specialized sensing infrastructure.