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  • 标题:Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach
  • 本地全文:下载
  • 作者:Xiaoqun Liao ; Mohammad Faisal ; Qing QingChang
  • 期刊名称:Scientific Programming
  • 印刷版ISSN:1058-9244
  • 出版年度:2020
  • 卷号:2020
  • 页码:1-7
  • DOI:10.1155/2020/8859454
  • 出版社:Hindawi Publishing Corporation
  • 摘要:

    Due to the enhancements of Internet of Things (IoT) and sensors deployments, the production of big data in Industrial Internet of Things (IIoT) is increased. The accessing and processing of big data become a challenging issue due to the limited storage space, computational time, networking, and IoT devices end. IoT and big data are well thought-out to be the key concepts when describing new information architecture projects. The techniques, tools, and methods that help to provide better solutions for IoT and big data can have an important role to play in the architecture of business. Different approaches are being practiced in the literature for evaluating the role of big data in IIoT. These techniques are not handling the situations when complexity of dependency arises among parameters of the alternatives. The proposed research uses the approach of Analytic Network Process (ANP) for evaluating the role of big data in IIoT. The results show that the proposed research works well for evaluating the role of big data in IIoT.

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