skip to main content
10.1145/3167132.3173379acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Smart IoT monitoring framework based on oneM2M for fog computing

Published:09 April 2018Publication History

ABSTRACT

As the Internet of Things (IoT) technology evolves, it is no longer appropriate to process real-time, large-volume data generated by numerous IoT devices in a cloud computing environment. To solve this problem, fog computing has been proposed which minimizes response time and makes real - time processing suitable. However, there is still a lack of research on techniques for efficiently managing various services and monitoring IoT devices in real time. Therefore, this paper proposes an efficient framework to monitor IoT devices in fog computing environment. The proposed monitoring framework is based on the oneM2M standard and consists of three detailed frameworks: device manager, monitoring manager, and data manager.

References

  1. {n. d.}. ONEM2M TECHNICAL SPECIFICATION. https://ko.wikipedia.org/wiki/OneM2M.({n. d.}). Accessed: 2017-09-20.Google ScholarGoogle Scholar
  2. {n. d.}. oneM2M Wikipedia. https://ko.wikipedia.org/wiki/OneM2M. ({n. d.}). Accessed: 2017-09-20.Google ScholarGoogle Scholar
  3. Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 13--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Amir Vahid Dastjerdi, Harshit Gupta, Rodrigo N Calheiros, Soumya K Ghosh, and Rajkumar Buyya. 2016. Fog computing: Principles, architectures, and applications. arXiv preprint arXiv:1601.02752 (2016).Google ScholarGoogle Scholar
  5. Gartner. 2017. Gartner Report. (2017). http://www.gartner.com/newsroom/id/3598917Google ScholarGoogle Scholar
  6. Doug Handler Joseph Bradley, Joel Barbier. 2013. Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion. (2013).Google ScholarGoogle Scholar
  7. Tom H Luan, Longxiang Gao, Zhi Li, Yang Xiang, Guiyi Wei, and Limin Sun. 2015. Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815 (2015).Google ScholarGoogle Scholar
  8. Ivan Stojmenovic. 2014. Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian. IEEE, 117--122.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Smart IoT monitoring framework based on oneM2M for fog computing

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing
          April 2018
          2327 pages
          ISBN:9781450351911
          DOI:10.1145/3167132

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 April 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate1,650of6,669submissions,25%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader