Cyber-Physical Systems for Cognitive Industrial Internet of Things: Sensory Big Data, Smart Mobile Devices, and Automated Manufacturing Processes.
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)
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.
This paper was supported by Grant GE-1477262 from the Big Data Algorithmic Analytics Laboratory, Memphis, TN.
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.
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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
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.
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|Publication:||Analysis and Metaphysics|
|Date:||Jan 1, 2019|
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