Printer Friendly

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)


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-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.


Bratu, S. (2018). "The Ethics of Algorithmic Sociality, Big Data Analytics, and Data-Driven Research Patterns," Review of Contemporary Philosophy 17: 100-106.

Devine, N. (2017). "Aims of Education in a Post-Neoliberal Context," Knowledge Cultures 5(6): 96-107.

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.

Gutu, G. (2018). "Interferential Creativity: The Case of Paul Celan," Creativity 1(2): 113-135.

Hoffman, S. F., and H. H. Friedman (2018). "Machine Learning and Meaningful Careers: Increasing the Number of Women in STEM," Journal of Research in Gender Studies 8(2): 11-27.

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

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

anovska, L. (2018). "Smart Services and Their Benefits for Manufacturers from a Global Perspective," Ekonomicko-manazerske spektrum 12(2): 46-56.

Klierova, M., and J. Kutik (2017). "One Stop Government--Strategy of Public Services for Citizens and Businesses in Slovakia," Administratie si Management Public 28: 66-80.

Krizanova, A., G. Lazaroiu, L. Gajanova, J. Kliestikova, M. Nadanyiova, and D. Moravcikova (2019). "The Effectiveness of Marketing Communication and Importance of Its Evaluation in an Online Environment," Sustainability 11: 7016.

Lazaroiu, G. (2018). "Participation Environments, Collective Identities, and Online Political Behavior: The Role of Media Technologies for Social Protest Campaigns," Geopolitics, History, and International Relations 10(2): 58-63.

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). "Sustainable Urban Mobility in the Sharing Economy: Digital Platforms, Collaborative Governance, and Innovative Transportation," Contemporary Readings in Law and Social Justice 10(1): 130-136.

Nelson, C. (2018). "Beyond Prometheus: Creativity, Discourse, Ideology, and the Anthropocene," Knowledge Cultures 6(2): 111-131.

Nica, E., G. H. Popescu, E. Nicolaescu, and V. D. Constantin (2014). "The Effectiveness of Social Media Implementation at Local Government Levels," Transylvanian Review of Administrative Sciences 10(SI): 152-166.

Nica, E., C. Manole, and C. I. Stan (2018). "A Laborless Society? How Highly Automated Environments and Breakthroughs in Artificial Intelligence Bring About Innovative Kinds of Skills and Employment Disruptions, Altering the Nature of Business Process and Affecting the Path of Economic Growth," Journal of Self-Governance and Management Economics 6(4): 25-30.

Nica, E. (2018). "Will Robots Take the Jobs of Human Workers? Disruptive Technologies that May Bring About Jobless Growth and Enduring Mass Unemployment," Psychosociological Issues in Human Resource Management 6(2): 56-61.

Pilkington, O. A. (2018). "Presented Discourse in Popular Science Narratives of Discovery: Communicative Side of Thought Presentation," Linguistic and Philosophical Investigations 17: 7-28.

Popescu Ljungholm, D. (2018). "Regulation of Automated Individual Decision-Making and Artificially Intelligent Algorithmic Systems: Is the GDPR a Powerful Enough Mechanism to Protect Data Subjects?," Analysis and Metaphysics 17: 116-121.

Popescu, G. H., V. Sima, E. Nica, and I. G. Gheorghe (2017). "Measuring Sustainable Competitiveness in Contemporary Economies--Insights from European Economy," Sustainability 9(7): 1230.

Popescu, G. H., I. E. Petrescu, and O. M. Sabie (2018). "Algorithmic Labor in the Platform Economy: Digital Infrastructures, Job Quality, and Workplace Surveillance," Economics, Management, and Financial Markets 13(3): 74-79.

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.

Zafar, S., R. Hussain, F. Hussain, and S. Jangsher (2019). "Interplay between Big Spectrum Data and Mobile Internet of Things: Current Solutions and Future Challenges," Computer Networks 163: 106879.

Zhan, D., L. Ye, H. Zhang, B. Fang, H. Li, Y. Liu, et al. (2018). "A High-Performance Virtual Machine Filesystem Monitor in Cloud-assisted Cognitive IoT," Future Generation Computer Systems 88: 209-219.

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.

Scott Graessley

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

Petr Suler

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

Tomas Kliestik

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

Eva Kicova

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

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
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

                                     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

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.
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:Graessley, Scott; Suler, Petr; Kliestik, Tomas; Kicova, Eva
Publication:Analysis and Metaphysics
Date:Jan 1, 2019
Previous Article:Regulating Government and Private Use of Unmanned Aerial Vehicles: Drone Policymaking, Law Enforcement Deployment, and Privacy Concerns.
Next Article:Semantically Enriched Internet of Things Sensor Data in Smart Networked Environments.

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