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Sensors 2017, 17(1), 88;

Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation

Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
Author to whom correspondence should be addressed.
Academic Editors: Mianxiong Dong, Zhi Liu, Anfeng Liu and Didier El Baz
Received: 1 November 2016 / Revised: 19 December 2016 / Accepted: 20 December 2016 / Published: 4 January 2017
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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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. View Full-Text
Keywords: crowdsensing; urban sensing; environment monitoring crowdsensing; urban sensing; environment monitoring

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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.

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