Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation
AbstractMonitoring 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. View Full-Text
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Kang, X.; Liu, L.; Ma, H. Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation. Sensors 2017, 17, 88.
Kang X, Liu L, Ma H. Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation. Sensors. 2017; 17(1):88.Chicago/Turabian Style
Kang, Xu; Liu, Liang; Ma, Huadong. 2017. "Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation." Sensors 17, no. 1: 88.
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