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Detecting Situations from Heterogeneous Internet of Things Data in Smart City Context

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Intelligent Computing (SAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 858))

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Abstract

Internet of Things (IoT) offers a lot of benefits for building smart city today and tomorrow. In a smart city, large number of heterogeneous IoT devices are likely to be embedded, which will generate huge volume of data with different formats. Therefore, it is a challenge to address such IoT data heterogeneity and process them together to support decision makers with information of interest. This paper provides a framework to achieve this goal. Our experiment shows that the information obtained from raw IoT data provides situational awareness for the decision makers of smart city in a seamless fashion.

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Notes

  1. 1.

    Smartsantander, www.smartsantander.eu.

  2. 2.

    OpenStack, http://www.openstack.org.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

    SK Alamgir Hossain & Md. Anisur Rahman

  2. Department of Software Engineering CCIS, King Saud University, Riyadh, Kingdom of Saudi Arabia

    M. Anwar Hossain

Authors
  1. SK Alamgir Hossain
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  2. Md. Anisur Rahman
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  3. M. Anwar Hossain
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Corresponding author

Correspondence to SK Alamgir Hossain .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Alamgir Hossain, S., Rahman, M.A., Hossain, M.A. (2019). Detecting Situations from Heterogeneous Internet of Things Data in Smart City Context. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_85

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