VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

Authors: Angelopoulos, K. et al.

http://eprints.bournemouth.ac.uk/31245/

https://www.sciencedirect.com/science/article/pii/S157411921730648X

Journal: Pervasive and mobile computing

Publisher: Elsevier

ISSN: 1574-1192

DOI: 10.1016/j.pmcj.2018.07.003

This data was imported from Scopus:

Authors: Luceri, L. et al.

http://eprints.bournemouth.ac.uk/31245/

Journal: Pervasive and Mobile Computing

Volume: 49

Pages: 126-138

ISSN: 1574-1192

DOI: 10.1016/j.pmcj.2018.07.003

© 2018 Elsevier B.V. Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowd-sensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO.

This data was imported from Web of Science (Lite):

Authors: Luceri, L. et al.

http://eprints.bournemouth.ac.uk/31245/

Journal: PERVASIVE AND MOBILE COMPUTING

Volume: 49

Pages: 126-138

eISSN: 1873-1589

ISSN: 1574-1192

DOI: 10.1016/j.pmcj.2018.07.003

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