title: Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation.
abstract: Monitoring the status of urban environments, which provides fundamental information for a city, yields crucial insights into various fields of urban research. Recently, with the popularity of smartphones and vehicles equipped with onboard sensors, a people-centric scheme, namely "crowdsensing", for city-scale environment monitoring is emerging. This paper proposes a data correlation based crowdsensing approach for fine-grained urban environment monitoring. To demonstrate urban status, we generate sensing images via crowdsensing network, and then enhance the quality of sensing images via data correlation. Specifically, to achieve a higher quality of sensing images, we not only utilize temporal correlation of mobile sensing nodes but also fuse the sensory data with correlated environment data by introducing a collective tensor decomposition approach. Finally, we conduct a series of numerical simulations and a real dataset based case study. The results validate that our approach outperforms the traditional spatial interpolation-based method.
authors.affiliation: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China. kangxu88@bupt.edu.cn.
authors.forename: Xu
authors.initials: X
authors.lastname: Kang
authors.affiliation: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China. liangliu@bupt.edu.cn.
authors.forename: Liang
authors.initials: L
authors.lastname: Liu
authors.affiliation: Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China. mhd@bupt.edu.cn.
authors.forename: Huadong
authors.initials: H
authors.lastname: Ma
keywords: crowdsensing; environment monitoring; urban sensing