Printer Friendly

Cyber-Physical Systems for Cognitive Industrial Internet of Things: Sensory Big Data, Smart Mobile Devices, and Automated Manufacturing Processes.

1. Introduction

Cognitive Industrial Internet of Things represents the harnessing of cognitive computing technologies developed on cognitive science and artificial intelligence. (Hu et al., 2018) The broad advancement of connected sensors, cloud systems, big data analytics, and omnipresent sensing technologies has enabled Cognitive Internet of Things and its applications in an early stage. (Gad et al., 2018) With the explosion of data from smart devices, it is a difficult task to enhance performance and attain intelligence (Bobakova, 2017; Kmecova, 2018; Meila, 2018; Peters, 2017; Popescu et al., 2017; Sion, 2018; Stroe, 2018) employing the present Internet of Things assimilated in cognitive and cooperative processes. (Wu et al., 2019)

2. Conceptual Framework and Literature Review

Internet of Things usually encompasses lossy and insufficiently powered networks of interconnected sensors (Gheisari and Tahavori, 2019): its setting is full of sensors, computational components, radio-frequency identification, and display devices that are linked ceaselessly. (Verma and Sood, 2018) The Internet of Things and artificial intelligence represent causal agents in driving the technological breakthrough of smart manufacturing (Jouet, 2018; Lazaroiu et al., 2017; Nica, 2018; Popescu Ljungholm, 2018; Popescu et al., 2019; Sorells, 2018), furthering economic growth. (Hu et al., 2019) Cognitive Internet of Things represents the present Internet of Things assimilated in cognitive and cooperative processes to advance performance and attain intelligence. (Liu et al., 2019) Industry 4.0 will significantly connect the cutting-edge development of data technology with groundbreaking manufacturing sector and production servicing operations to boost transformation and enhancement of intelligent equipment. (Wan et al., 2018)

3. Methodology and Empirical Analysis

Building my argument by drawing on data collected from BCG, Business Insider, Deloitte, LMTPE-RWTH Aachen University, and PwC, I performed analyses and made estimates regarding the main areas of interest for manufacturers within Industry 4.0 (%) and the effects of Industry 4.0 on the workforce (%). Data collected from 4,200 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

The Internet of Things constitutes a relevant driving force in the smart manufacturing environment. (Durao et al., 2018) The standard Internet of Things, which is developed on established static designs, is deficient in intelligence and cannot conform to the intensifying application performance stipulations. (Gheisari and Tahavori, 2019) With the worldwide fashionableness of Internet of Things technology, vast amounts of digital mobile devices have been advanced. (Li et al., 2019) In Internet of Things, a range of objects carry out the tasks of sensing, interaction, and computation for supplying perpetual services to the consumers. (Makkar and Kumar, 2019) Social networks in the sphere of Cognitive Internet of Things enable the progressive identification of first-rate services and important data. (Zhou et al., 2018) In Cognitive Internet of Things, truth discovery is instrumental in determining trustworthy values from wide-ranging information to assist Cognitive Internet of Things in supplying thoughtful observations and usefulness from gathered data. (Zhang et al., 2019) (Tables 1-7)

5. Conclusions and Implications

Internet of Things is a wide-ranging network that furthers big data analytics. (Jia and Guo, 2019) With the advancement and taking on of Internet of Things, factories show a tendency to being more harmonized and interconnected. (Hu et al., 2018) The broad harnessing of wireless sensor networks, cloud technologies, industrial robots, embedded computing, and affordable sensors (Sponte (Pictalu), 2018) has enabled Industrial Internet of Things. (Zhang et al., 2018) Integrating cognition in Internet of Things bolsters it with mental ability and cutting-edge intelligence. (Gheisari and Tahavori, 2019) As manufacturing systems depart from automated models to groundbreaking configurations (e.g. smart factories in Industry 4.0), streamlined wireless networks are developing into high-potential communication systems operational in the manufacturing sphere. (Li and Wan, 2018)

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-1477262 from the Big Data Algorithmic Analytics Laboratory, Memphis, TN.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

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

REFERENCES

Bobakova, V. (2017). "The Formation of Regional Self-Government in the Slovak Republic and its Sources of Funding," Administratie si Management Public 28: 97-115.

Durao, L. F. C. S., M. M. Carvalho, S. Takey, P. A. Cauchick-Miguel, and E. Zancul (2018). "Internet of Things Process Selection: AHP Selection Method," The International Journal of Advanced Manufacturing Technology 99: 2623-2634.

Gad, R., M. Talha, A. A. Abd El-Latif, M. Zorkany, A. El-Sayed, N. El-Fishawy, et al. (2018). "Iris Recognition Using Multi-Algorithmic Approaches for Cognitive Internet of Things (CIoT) Framework," Future Generation Computer Systems 89: 178-191.

Gheisari, S., and E. Tahavori (2019). "CCCLA: A Cognitive Approach for Congestion Control in Internet of Things Using a Game of Learning Automata," Computer Communications 147: 40-49.

Hu, L., D. Tian, and K. Lin (2018). "Cognitive Industrial Internet of Things," Mobile Networks and Applications 23: 1607-1609.

Hu, L., Y. Miao, G. Wu, M. M. Hassan, and I. Humar (2019). "iRobot-Factory: An Intelligent Robot Factory Based on Cognitive Manufacturing and Edge Computing," Future Generation Computer Systems 90: 569-577.

Jia, M., and Q. Guo (2019). "Intelligent Cognitive Internet of Integrated Space and Terrestrial Things," Mobile Networks and Applications 24: 1924-1925.

Jouet, J. (2018). "Digital Feminism: Questioning the Renewal of Activism," Journal of Research in Gender Studies 8(1): 133-157.

Kmecova, I. (2018). "The Processes of Managing Human Resources and Using Management Methods and Techniques in Management Practice," Ekonomicko-manazerske spektrum 12(1): 44-54.

Lazaroiu, G., A. Pera, R. O. Stefanescu-Mihaila, N. Mircica, and O. Negurita (2017). "Can Neuroscience Assist Us in Constructing Better Patterns of Economic Decision-Making?," Frontiers in Behavioral Neuroscience 11: 188.

Li, X., and J. Wan (2018). "Proactive Caching for Edge Computing-enabled Industrial Mobile Wireless Networks," Future Generation Computer Systems 89: 89-97.

Li, Y., H. Lu, H. Kim, and S. Serikawa (2019). "Touch Switch Sensor for Cognitive Body Sensor Networks," Computer Communications 146: 32-38.

Liu, Y., A. Liu, T. Wang, X. Liu, and N. N. Xiong (2019). "An Intelligent Incentive Mechanism for Coverage of Data Collection in Cognitive Internet of Things," Future Generation Computer Systems 100: 701-714.

Makkar, A., and N. Kumar (2019). "Cognitive Spammer: A Framework for PageRank Analysis with Split by Over-sampling and Train by Under-Fitting," Future Generation Computer Systems 90: 381-404.

Meila, A. D. (2018). "Regulating the Sharing Economy at the Local Level: How the Technology of Online Labor Platforms Can Shape the Dynamics of Urban Environments," Geopolitics, History, and International Relations 10(1): 181-187.

Nica, E. (2018). "The Social Concretisation of Educational Postmodernism," Educational Philosophy and Theory 50(14): 1659-1660.

Peters, M. A. (2017). "Disciplinary Technologies and the School in the Epoch of Digital Reason: Revisiting Discipline and Punish after 40 Years," Contemporary Readings in Law and Social Justice 9(1): 28-46.

Popescu Ljungholm, D. (2018). "Reality-Construction Processes in Information Societies: Algorithmic Regulation, Automated Decision-Making, and Networked Governance," Review of Contemporary Philosophy 17: 107-113.

Popescu, G. H., E. Nica, F. C. Ciurlau, M. Comanescu, and T. Bitoiu (2017). "Stabilizing Valences of an Optimum Monetary Zone in a Resilient Economy--Approaches and Limitations," Sustainability 9(6): 1051.

Popescu, G. H., J. V. Andrei, E. Nica, M. Mieila, and M. Panait (2019). "Analysis on the Impact of Investments, Energy Use and Domestic Material Consumption in Changing the Romanian Economic Paradigm," Technological and Economic Development of Economy 25(1): 59-81.

Sion, G. (2018). "Smart Educational Ecosystems: Cognitive Engagement and Machine Intelligence Algorithms in Technology-Supported Learning Environments," Analysis and Metaphysics 17: 140-145.

Sorells, B. (2018). "Will Robotization Really Cause Technological Unemployment? The Rate and Extent of Potential Job Displacement Caused by Workplace Automation," Psychosociological Issues in Human Resource Management 6(2): 68-73.

Sponte (Pistalu), M. (2018). "Cognitive Performance and Labor Market Outcomes: Evidence from the U.S.," Economics, Management, and Financial Markets 13(2): 70-75.

Stroe, M. A. (2018). "Harold Bloom and the Brain-Wave Theory of Creativity," Creativity 1(2): 3-111.

Verma, P., and S. K. Sood (2018). "Internet of Things-based Student Performance Evaluation Framework," Behaviour & Information Technology 37(2): 102-119.

Wan, J., J. Li, Q. Hua, A. Celesti, and Z. Wang (2018). "Intelligent Equipment Design Assisted by Cognitive Internet of Things and Industrial Big Data," Neural Computing and Applications. doi: 10.1007/s00521-018-3725-5

Wu, P., Z. Lu, Q. Zhou, Z. Lei, X. Li, M. Qiu, et al. (2019). "Big Data Logs Analysis Based on seq2seq Networks for Cognitive Internet of Things," Future Generation Computer Systems 90: 477-488.

Zhang, Y., L. Peng, Y. Sun, and H. Lu (2018). "Intelligent Industrial IoT Integration with Cognitive Computing," Mobile Networks and Applications 23: 185-187.

Zhang, C., L. Zhu, C. Xu, K. Sharif, X. Du, and M. Guizani (2019). "LPTD: Achieving Lightweight and Privacy-Preserving Truth Discovery in CIoT," Future Generation Computer Systems 90: 175-184.

Zhou, K., J. Zeng, Y. Liu, and F. Zou (2018). "Deep Sentiment Hashing for Text Retrieval in Social CIoT," Future Generation Computer Systems 86: 362-371.

Nela Mircica

nelamircica@yahoo.com

Spiru Haret University, Bucharest, Romania

Received 28 April 2019 * Received in revised form 24 August 2019

Accepted 27 August 2019 * Available online 5 December 2019

doi:10.22381/AM1820195
Table 1 Industry 4.0 framework and contributing digital
technologies (%)

Mobile devices                                          14
IoT platforms                                           11
Location detection technologies                          6
Advanced human-machine interfaces                        6
Authentication and fraud detection                       5
3D printing                                              9
Smart sensors                                           12
Big data analytics and advanced algorithms              13
Multilevel customer interaction and customer profiling  13
Augmented reality/Wearables                              5
Cloud computing                                          6

Sources: PwC; my survey among 4,200 individuals conducted March 2019.

Table 2 The main areas of interest for manufacturers within
Industry 4.0 (%)

Internet of Things platform and connectivity  14
Operations optimization                       11
Cyber security                                13
Sensing and imaging                           11
Additive manufacturing                         7
Supply chain                                  12
Robotics                                      10
Safety                                         9
Predictive maintenance                         7
Inspection and testing                         6

Sources: Deloitte; my survey among 4,200 individuals conducted
March 2019.

Table 3 About how much does your company plan to invest in the next
five years for Internet of Things solutions? (%)

<$100,000                 43
$100,000-$1 million       27
$1 million-$10 million    16
$10 million-$100 million  11
$100 million or more       3

Sources: Business Insider; my survey among 4,200 individuals
conducted March 2019.

Table 4 In which areas will your company use data analytics in five
years? Improving customer relationship and customer intelligence
along the product life cycle (%)

Basing product/service development on customer specifications  18
Innovation in customer service                                 17
Using data analytics to meet customer requirements and         19
improve operational performance
Customizing products all the way down to a lot size of 1       13
Building a customer-focused supply chain                       18
Driving customer-centric marketing and channel access          15

Sources: PwC; my survey among 4,200 individuals conducted March 2019.

Table 5 Elements that are expected to be highly relevant along the
automotive value chain in 2030 (%)

Value         Component       Press shop         Body shop
chain         manufacturing

Plant         Modular line    Energy             Modular line
structure     setup (95)      efficiency         setup (90)
                              (88)               (89)
Plant                         Flexible
structure                     equipment
                              (65)
Plant         Smart robots    Predictive         Smart robots
digitization  (91)            maintenance        (89)
                              (95)
Plant         Decentralized   Smart              Production
digitization  production      presses (89)       simulations
              steering (83)                      (86)
Plant         Digital plant   Production         Decentralized
digitization  logistics (78)  simulations        production
                              (79)               steering (80)
Plant         Additive        Digital plant      Big data and
digitization  manufacturing   logistics          analytics (79)
              (78)            (80)
Plant         Lean            New technologies
processes     Management      will enhance lean
              (94)            management (66)
                              New technologies
                              will make lean
                              management
                              obsolete (4)

Value         Paint shop      Final assembly
chain

Plant         Energy          Modular line
structure     efficiency      setup (87)

Plant                         Multidirectional
structure                     layout (80)

Plant         Decentralized   Smart robots
digitization  production      (90)
              steering (77)
Plant         Big data and    Decentralized
digitization  analytics (78)  production
                              steering (83)
Plant         Advanced        Digital plant
digitization  painting (69)   logistics (79)

Plant         Automated       Augmented
digitization  quality         reality (71)
              control (74)
Plant
processes

Sources: BCG; LMTPE-RWTH Aachen University; my survey among 4,200
individuals conducted March 2019.

Table 6 The effects of Industry 4.0 on the workforce (%)

Big data-driven           Algorithms based on historical data identify
quality control           quality issues and reduce product failures.
Robot-assisted            Flexible, humanoid robots perform
production                other operations.
Self-driving              Fully automated transportation systems
logistics vehicles        navigate intelligently within the factory.
Production line           Novel software enables assembly line
simulation                simulation and optimization.
Smart supply network      Monitoring of an entire supply network allows
                          for better supply decisions.
Predictive maintenance    Remote monitoring of equipment permits
                          repair prior to breakdown.
Machines as a service     Manufacturers sell a service, including
                          maintenance, rather than a machine.
Self-organizing           Automatically coordinated machines optimize
production                their utilization and output.
Additive manufacturing    3D printers create complex parts in one step,
of complex parts          making assembly redundant.
Augmented work,           Fourth dimension facilitates operating
maintenance, and service  guidance, remote assistance, and
                          documentation.

Big data-driven           95
quality control
Robot-assisted            92
production
Self-driving              87
logistics vehicles
Production line           85
simulation
Smart supply network      86

Predictive maintenance    84

Machines as a service     80

Self-organizing           79
production
Additive manufacturing    75
of complex parts
Augmented work,           76
maintenance, and service

Sources: BCG; my survey among 4,200 individuals conducted March 2019.

Table 7 Which statement best describes the level of automation and
connectivity at your manufacturing operations? (%)

                                    Digital  Digital
                                    novice   follower

Only selectively automated          16       48
and connected
Largely at the single plant level   18       23
Seamlessly across multiple plants    4       10
and beyond manufacturing
Fully across internal and external  19        6
plants, exchanging and acting on
information in real-time

                                    Digital    Digital
                                    innovator  champion

Only selectively automated          25         11
and connected
Largely at the single plant level   38         21
Seamlessly across multiple plants   38         48
and beyond manufacturing
Fully across internal and external  37         38
plants, exchanging and acting on
information in real-time

Sources: PwC; my survey among 4,200 individuals conducted March 2019.
COPYRIGHT 2019 Addleton Academic Publishers
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Mircica, Nela
Publication:Analysis and Metaphysics
Date:Jan 1, 2019
Words:2316
Previous Article:Semantically Enriched Internet of Things Sensor Data in Smart Networked Environments.
Next Article:Big Data, Blockchain, and Artificial Intelligence in Cloud-based Accounting Information Systems.
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters