Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

Urban pollution is usually monitored via fixed stations that provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Currently, the spread of...

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Bibliographic Details
Main Authors: Marco Zappatore, Corrado Loglisci, Antonella Longo, Mario A. Bochicchio, Lucia Vaira, Donato Malerba
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Classification
data quality
data trustworthiness level prediction
machine learning
mobile crowd sensing
transductive learning algorithm
Online Access:https://ieeexplore.ieee.org/document/8878118/

Internet

https://ieeexplore.ieee.org/document/8878118/

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