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

机译:IDML:用于WoT中资源受限的智能对象的基于IDentifier的标记语言

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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.
机译:物联网(WoT)中资源受限的智能对象(SO)的数据表示需要兼容性,互操作性,可伸缩性和高效率。但是,在Web平面上进行数据表示的常规方法具有丰富的数据附加信息,并且效率不高。当前关于智能对象的方法并不灵活,它们不能表示复杂的数据结构,例如关系数据和层次结构数据。为了灵活有效地表示资源受限的智能对象中的数据,本文提出了一种基于IDentifier的标记语言(IDML)。当构造IDML的框架时,提出了三个想法,即,键和它们的值之间的关联的结构化,缩短元数据标识符的长度,利用非打印字符来控制数据块的结构。在IDML中,设计了三种数据表示方法,包括顺序数据,关系数据和分层数据。通过比较和计算的评估表明,IDML不仅具有Web平面上通用数据表示语言的可伸缩性和灵活性,而且在智能对象上具有很高的效率。与ANSI10.8.2和JSON相比,IDML可以分别将效率提高37.4%和50.4%。

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