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Article
Immersing citizens and things into smart cities: a social machine-based and data artifact-driven approach
Computing
  • Emir Ugljanin, State University of Novi Pazar
  • Ejub Kajan, State University of Novi Pazar
  • Zakaria Maamar, Zayed University
  • Muhammad Asim, National University of Computer and Emerging Sciences Islamabad
  • Vanilson Burégio, Universidade Federal Rural de Pernambuco
ORCID Identifiers

0000-0002-3753-3535

Document Type
Article
Publication Date
7-1-2020
Abstract

© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. This paper presents an approach for allowing the transparent co-existence of citizens and IoT-compliant things in smart cities. Considering the particularities of each, the approach embraces two concepts known as social machines and data artifacts. On the one hand, social machines act as wrappers over applications (e.g., social media) that allow citizens and things to have an active role in their cities by reporting events of common interest to the population, for example. On the other hand, data artifacts abstract citizens’ and things’ contributions in terms of who has done what, when, where, and why. For successful smart cities, the approach relies on the willingness and engagement of both citizens and things. Smart cities’ initiatives are embraced and not imposed. A case study along with a testbed that uses a real dataset about car-traffic accident in a state in Brazil demonstrate the technical doability and scalability of the approach. The evaluation consists of assessing the time to drill into the different generated data artifacts prior to generating useful details for decision makers.

Publisher
Springer
Keywords
  • Data artifact,
  • Internet-of-Things,
  • Smart city,
  • Social machine
Scopus ID
85077567957
Indexed in Scopus
Yes
Open Access
No
https://doi.org/10.1007/s00607-019-00774-9
Citation Information
Emir Ugljanin, Ejub Kajan, Zakaria Maamar, Muhammad Asim, et al.. "Immersing citizens and things into smart cities: a social machine-based and data artifact-driven approach" Computing Vol. 102 Iss. 7 (2020) p. 1567 - 1586 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/0010-485X" target="_blank">0010-485X</a>
Available at: http://works.bepress.com/zakaria-maamar/393/