CCS-TA: Quality-guaranteed online task allocation in compressive crowdsensing

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing applications. In this paper, we leverage the spatial and temporal correlation among the data sensed in different sub-Areas to significantly reduce the re-quired number of sensing tasks allocated (corresponding to budget), yet ensuring the data quality. Specifically, we propose a novel framework called CCS-TA, combining the state-of-The-Art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a mini-mum number of sub-Areas for sensing task allocation in each sensing cycle, while deducing the missing data of un-allocated sub-Areas under a probabilistic data accuracy guar-antee. Evaluations on real-life temperature and air qual-ity monitoring datasets show the effectiveness of CCS-TA. In the case of temperature monitoring, CCS-TA allocates 18.0-26.5% fewer tasks than baseline approaches, allocating tasks to only 15.5% of the sub-Areas on average while keeping overall sensing error below 0.25°C in 95% of the cycles.