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Industrial Big Data Analytics for Cognitive Internet of Things: Wireless Sensor Networks, Smart Computing Algorithms, and Machine Learning Techniques.

1. Introduction

Cognitive Industrial Internet of Things supplies first-rate performance of interacting, computing, supervising, and cutting-edge machine intelligence (Gutu, 2018; Klierova and Kutik, 2017; Meila, 2018; Nica et al., 2018; Popescu Ljungholm, 2018) for advancing smart Industrial Internet of Things applications, e.g. Industry 4.0 and cognitive manufacturing. (Hu et al., 2018) Connected Cognitive Internet of Things devices and sensors generate large volumes of information. Cognitive systems collect data from their encounters and thus enhance their operation when carrying out recurrent assignments. (Gad et al., 2018)

2. Conceptual Framework and Literature Review

The advancement of coherent space and terrestrial interaction and networking technologies offers significant technical backing for the promotion of the Internet of Things. (Jia and Guo, 2019) Industrial Internet of Things constitutes a direct catalyst for manufacturing upgrading. (Zhang et al., 2018) Network devices in the Internet of Things are typically equipped with incongruous sensors and actuators making up lossy and low-powered infrastructure, and through access networks with multifarious technologies (Bratu, 2018; Hoffman and Friedman, 2018; Krizanova et al., 2019; Nelson, 2018; Nica, 2018; Popescu et al., 2017) being connected to the Internet for the purpose of transferring data and sharing resources. (Gheisari and Tahavori, 2019) Big data technology can supply swift computing and vast stowage via parallel computing and distributed storage systems. (Wu et al., 2019) Cognitive Internet of Things improves the effectiveness of intelligence in smart objects that can take autonomous decisions in any setting. (Makkar and Kumar, 2019) For Cognitive Internet of Things, inadequacies in sensing data coverage lead to fatality and social turmoil. (Liu et al., 2019)

3. Methodology and Empirical Analysis

We inspected, used, and replicated survey data from BCG, CIO, EY, Gartner, PwC, and Statista, performing analyses and making estimates regarding how Industry 4.0 is delivering revenue, cost and efficiency gains (%) and top technologies being considered in-line with organizations' strategic plan (%). Structural equation modeling was used to analyze the data and test the proposed conceptual model.

4. Results and Discussion

In Internet of Things, intelligent decisions are taken in conformity with the data extracted from unprocessed information through smart devices. (Zafar et al., 2019) Most devices in the Internet of Things have insufficient processing power, determinate storage capacity and energy limitations. (Gheisari and Tahavori, 2019) Internet of Things carries out the service-oriented design that facilitates the interaction among smart objects. (Makkar and Kumar, 2019) Relying on cognitive computing in putting into operation smart manufacturing, Industrial Internet of Things is considerably becoming more efficient. (Zhang et al., 2018) Cloud-assisted Cognitive Internet of Things encompasses relevant data analytics potentiality derived from the computing and information storage performance of cloud virtual machines. (Zhan et al., 2018) (Tables 1-4)

5. Conclusions and Implications

The standard Internet of Things cannot adequately conform to the growing application performance guidelines (Devine, 2017; Kanovska, 2018; Lazaroiu, 2018; Nica et al., 2014; Pilkington, 2018; Popescu et al., 2018) by being chiefly concerned with how to enable and connect objects to sense the physical realm and subsequently to distribute the gathered information online. (Gheisari and Tahavori, 2019) The services of an incisive Industrial Internet of Things assimilation with cognitive computing can be suggestive, prescriptive, or instructive, being more inspiring and robust by design options to make a new category of issues computable. (Zhang et al., 2018) Cognitive Internet of Things can obtain important data from a range of Internet of Things devices. By carrying out truth identification, factual sensory information can be collected, significantly advancing the validness of Cognitive Internet of Things applications. (Zhang et al., 2019)

Note

The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. The precision of the online polls was measured using a Bayesian credibility interval.

Funding

This paper was supported by Grant GE-1473862 from the Center for Big Data-driven Algorithmic Decision-Making, Portland, OR.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Scott Graessley

scott.graessley@aa-er.org

The Cognitive Automation Research Unit at CLI, New Haven, CT, USA

Petr Suler

petr.suler@cez.cz

The School of Expertness and Valuation, The Institute of Technology and Business in Ceske Budejovice, Czech Republic

Tomas Kliestik

tomas.kliestik@fpedas.uniza.sk

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Eva Kicova

eva.kicova@fpedas.uniza.sk

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Received 29 March 2019 * Received in revised form 26 August 2019

Accepted 28 August 2019 * Available online 15 December 2019

doi:10.22381/AM1820193
Table 1 How Industry 4.0 is delivering revenue, cost and
efficiency gains (%)

Additional revenue from:
Digitizing products and services within the existing portfolio       21
New digital products, services and solutions                         20
Offering big data and analytics as a service                         19
Personalized products and mass customization                         14
Capturing high-margin business through improved                      14
customer insight from data analytics
Increasing market share of core products                             12

Lower cost and greater efficiency from:
Real-time inline quality control based on big data analytics         19
Modular, flexible and customer-tailored production concepts          13
Real-time visibility into process and product variance,              15
augmented reality and optimization by data analytics
Predictive maintenance on key assets using predictive algorithms to  13
optimize repair and maintenance schedules and improve asset uptime
Vertical integration from sensors through MES to
real-time production                                                 10
planning for better machine utilization and faster
throughput times
Horizontal integration, as well as track-and-trace of products        9
for better inventory performance and reduced logistics
Digitization and automation of processes for a smarter use            8
of human resources and higher operations speed
System based, real-time end-to-end planning and
horizontal collaboration                                              7
using cloud based planning platforms for execution optimization
Increased scale from increased market share of core products          6

Sources: PwC; our survey among 4,700 individuals conducted
February 2019.

Table 2 Technology developments that enable Service 4.0 (%)

Big data and analytics  develop deeper insight into customer        18
                        behavior, preferences, and pathways.
Cloud computing         manages huge data volume in open            13
                        systems and provide services on demand.
Robotic process         replaces humans in work processes that      14
automation              are entirely rule based.
Bionic computing        interacts naturally with virtual agents,     8
                        digital devices, and services.
Cognitive computing     simulates human thought processes and       11
                        provide intelligent, virtual assistance.
Virtualization          free services from reliance on specific      8
                        software and hardware and ensure
                        flexibility, adaptability, and robustness.
Ubiquitous              create an ongoing connection in areas as    12
connectivity and        varied as on-the-spot service provision
the Internet            and remote monitoring.
of Things
Smart devices           develop an ecosystem of apps and cloud      11
                        services that utilize high-performance
                        devices.
Augmented reality       provide the necessary information when       5
                        needed in areas as varied as manuals,
                        pricing, and alerts.

Sources: BCG; our survey among 4,700 individuals conducted
February 2019.

Table 3 To what extent have you implemented, piloted, or planned
to implement the following technologies within your company? (%)

                                     Digital  Digital
                                     novice   follower

Predictive maintenance of assets     36       82
and products
Manufacturing execution systems      26       75
Integrated end-to-end                31       74
supply chain planning
Connectivity/                        27       71
Industrial Internet of Things
Digital twin of products             21       56
and manufacturing line
Collaborative robots, smart robots,  21       39
Robotic Process Automation
Artificial intelligence               9       16
Virtual reality/Augmented reality     7       19
solutions

                                     Digital    Digital
                                     innovator  champion

Predictive maintenance of assets     97          98
and products
Manufacturing execution systems      94          96
Integrated end-to-end                95         100
supply chain planning
Connectivity/                        95          99
Industrial Internet of Things
Digital twin of products             86          98
and manufacturing line
Collaborative robots, smart robots,  78          95
Robotic Process Automation
Artificial intelligence              52          77
Virtual reality/Augmented reality    48          82
solutions

Sources: PwC; our survey among 4,700 individuals conducted
February 2019.

Table 4 Top technologies being considered in-line with
organizations' strategic plan (%)

Virtual/Augmented/Mixed reality             35
Cognitive analytics and machine learning    68
Robotic process automation                  57
Blockchain                                  31
Digital/Crypto-currencies                    6
Conversational systems                      37
Analytics on non-conventional data sources  38
3D printing                                 19

Sources: CIO; EY; our survey among 4,700 individuals conducted
February 2019.
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Author:Graessley, Scott; Suler, Petr; Kliestik, Tomas; Kicova, Eva
Publication:Analysis and Metaphysics
Date:Jan 1, 2019
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