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      Establishment of Trust in Internet of Things by Integrating Trusted Platform Module: To Counter Cybersecurity Challenges

      1 , 2 , 3 , 3 , 3
      Complexity
      Hindawi Limited

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          Abstract

          With the increasing day-to-day acceptance of IOT computing, the issues related to it are also getting more attention. The current IOT computing infrastructure brings some security challenges concerned with the users/customers and CSP. The users can store their confidential data at IOT storage and can access them anytime when they need. Lack of trust exists among IOT users and between IOT users and CSP. The prevention of this risk is a big research issue and it needs to be solved. There is a need for trusted IOT computing in recent times to provide trusted services. Here, we propose the integration of TPM in IOT computing to performs cryptographic operations and provide hardware-based security. In this domain, different schemes and methods have been proposed to build trust in IOT computing, but the suitable solution has not been presented by these schemes because these schemes lack in terms of some security services. A comparative study based on trusted computing schemes has also been presented in this paper along with different implementations of critical analysis. Our study is based on an overview of the main issues and summarizing the literature along with their strengths and limitations. In the end, we integrated the trusted platform module in the IOT architecture to establish the trust in IOT computing and to enhance the cybersecurity challenges and evaluated it with the help of mathematical/algorithms/graph theory/matrices and logical diagrams.

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          A method for obtaining digital signatures and public-key cryptosystems

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            Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach

            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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              Big Data, Scientific Programming, and Its Role in Internet of Industrial Things: A Decision Support System

              Big data is a challenging issue as its volume, shape, and size need to be modified in order to extract important information for a specific purpose. The amount of data is rising with the passage of time. This increase in volume can be a challenging issue to analyze the data for smooth industry and the Internet of things. Several tools, techniques, and mechanisms are available to support the handling and management process of such data. Decision support systems can be one of the important techniques which can support big data in order to make decisions on time. The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things. The study has used the tool of SuperDecisions to plot the hierarchy of situations of big data and scientific programming and to select the best alternative among the available.
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                Author and article information

                Contributors
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                Journal
                Complexity
                Complexity
                Hindawi Limited
                1099-0526
                1076-2787
                December 21 2020
                December 21 2020
                : 2020
                : 1-9
                Affiliations
                [1 ]Department of CS & IT, University of Malakand, KPK 18800, Pakistan
                [2 ]SZABIST, Islamabad, Pakistan
                [3 ]Department of Electronic Engineering, Korea Polytechnic University, Siheung, Republic of Korea
                Article
                10.1155/2020/6612919
                3708d175-c1b6-4bf1-b5dc-1d82a1c5e546
                © 2020

                https://creativecommons.org/licenses/by/4.0/

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