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Immersing citizens and things into smart cities: a social machine-based and data artifact-driven approach

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Abstract

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.

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Notes

  1. www.gartner.com/newsroom/id/3165317.

  2. www.asianscientist.com/2017/04/tech/crowdsourcing-traffic-violation-road-safety.

  3. https://data.oecd.org/popregion/urban-population-by-city-size.htm.

  4. www.prf.gov.br/portal/dados-abertos/acidentes.

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Authors and Affiliations

  1. State University of Novi Pazar, Novi Pazar, Serbia

    Emir Ugljanin & Ejub Kajan

  2. Zayed University, Dubai, UAE

    Zakaria Maamar

  3. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Muhammad Asim

  4. Federal Rural University of Pernambuco, Recife, Brazil

    Vanilson Burégio

Authors
  1. Emir Ugljanin
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  2. Ejub Kajan
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  3. Zakaria Maamar
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  4. Muhammad Asim
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  5. Vanilson Burégio
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Correspondence to Ejub Kajan.

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Ugljanin, E., Kajan, E., Maamar, Z. et al. Immersing citizens and things into smart cities: a social machine-based and data artifact-driven approach. Computing 102, 1567–1586 (2020). https://doi.org/10.1007/s00607-019-00774-9

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