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

  • Luca Luceri*
  • , Felipe Cardoso
  • , Michela Papandrea
  • , Silvia Giordano
  • , Julia Buwaya
  • , Stéphane Kundig
  • , Constantinos Marios Angelopoulos
  • , José Rolim
  • , Zhongliang Zhao
  • , Jose Luis Carrera
  • , Torsten Braun
  • , Aristide C.Y. Tossou
  • , Christos Dimitrakakis
  • , Aikaterini Mitrokotsa
  • *Corresponding author for this work
  • University of Applied Sciences and Arts of Southern Switzerland
  • University of Bern
  • University of Geneva
  • Bournemouth University
  • Chalmers University of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)126-138
Number of pages13
JournalPervasive and Mobile Computing
Volume49
DOIs
StatePublished - Sep 2018
Externally publishedYes

Keywords

  • Big data
  • Internet of Things
  • Mobile crowd-sensing

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