Quality-Aware Sparse Data Collection in MEC-Enhanced Mobile Crowdsensing Systems

Abstract

Mobile crowdsensing (MCS) is a new data collection paradigm profiting from the human-centric cyber social computing. However, due to the humans’ uncontrollable mobility, it raises severe concerns of data redundancy and poor data quality. In this paper, we propose a novel data gathering architecture based on mobile edge computing (MEC), which distributes computing resources [edge nodes (ENs)] in the sensing scenarios close to the mobile users, and thus enables significant improvements to handle users’ frequent location changes and reduce the specified quantity of sensing tasks. Based on the MEC-enhanced architecture, we design a quality-aware sparse data collection (QSDC) algorithm in the MCS systems. In the ENs’ part, QSDC exploits the implicit correlation (IC) among the sensing data to reduce the data redundancy and selects the appropriate users’ group to ensure the spatiotemporal coverage of sensing grids. In the cloud server part, QSDC leverages the compressive sensing to recover the sensing data in the whole sensing area with high data quality. Extensive experiments verify the performance of QSDC based on real data sets under different experiment settings and demonstrate the effectiveness and availability of QSDC.