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Sensors 2017, 17(1), 88; doi:10.3390/s17010088

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
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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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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. 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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