Cost-Quality Aware Compressive Mobile Crowdsensing

With ubiquitous mobile devices, mobile crowdsensing (MCS) emerges as a promising paradigm for monitoring the overall status of a large-scale area. However, the MCS applications have yet to be widely adopted in practice because of high sensing and communication costs as well as insufficient participants. To deal with these problems, compressive sensing is introduced into mobile crowdsensing, where it is used to deduce the missing data of unsensed locations by exploiting the inherent correlations of sensory data. This new paradigm is the so-called sparse mobile crowdsensing or compressive crowdsensing (CCS). In this emerging approach, compressive sensing not only can be used after the data are collected, but also before or during the data collection process. Two main questions lying in CCS are: (1) Where to sense? (2) How to recover the unsensed data accurately? Considering the practical factors (e.g., diverse sensing cost and importance disparity of different cells), it would affect the selection strategies design and further determine the recovery accuracy. In this chapter, we will have a close look at recent advances about CCS and provide formulations to solve the above-mentioned problems.