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IDML: IDentifier-Based Markup Language for Resource-Constrained Smart Objects in WoT

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

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Abstract

Data representation for resource-constrained Smart Objects (SOs) in Web of Things (WoT) requires compatibility, interoperability, scalability and high efficiency. However, general methods of data representation on web plane have rich extra information for data and they are not efficient; and current methods on Smart Objects are not flexible and they cannot represent complex data structures, such as relational data and hierarchical data. To represent data in resource-constrained Smart Objects flexibly and efficiently, this paper presents IDentifier-based Markup Language (IDML). When constructing the framework for IDML, three ideas are proposed, i.e., structuralization of associations between keys and their values, shortening the length of metadata identifiers, utilization nonprinting characters to control the structure of data block. In IDML, three kinds of data representation methods are designed, including sequential data, relational data, and hierarchical data. Evaluation by comparison and calculation shows that, IDML not only has the scalability and flexibility of general data representation languages on web plane, but also has high efficiency on Smart Objects. Compared with ANSI10.8.2 and JSON in a case scenario, IDML can improve efficiency up to 37.4% and 50.4% respectively.

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Author information

Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100086, China

    Wuming Luo

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  1. Wuming Luo
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Correspondence to Wuming Luo .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Luo, W. (2019). IDML: IDentifier-Based Markup Language for Resource-Constrained Smart Objects in WoT. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_68

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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