Review Article
Use of Big Data Tools and Industrial Internet of Things:
An Overview
Yingzi Wang ,
1 Muhammad Nazir Jan,2 Sisi Chu,3 and Yue Zhu3
1
College of Intelligence and Computing, TianJin University, TianJin 300300, China
2
Department of Computer Science, University of Swabi, Swabi, Pakistan
3
Automotive Data of China Co.,Ltd., TianJin 300300, China
Correspondence should be addressed to Yingzi Wang; [email protected]
Received 8 September 2020; Revised 26 September 2020; Accepted 3 October 2020; Published 21 October 2020
Academic Editor: Habib Ullah Khan
Copyright © 2020 Yingzi Wang et al. +is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Big data is ever playing an important role in the industry as well as many other organizations. With the passage of time, the volume
of data is increasing. +is increase will create huge bulk of data which needs proper tools and techniques to handle its management
and organization. Different techniques and tools are being used to properly handle the management of data. A detailed report of
these techniques and tools is needed which will help researchers to easily identify a tool for their data and take help to easily
manage the data, organize the data, and extract meaningful information from it. +e proposed study is an endeavour toward
summarizing and identifying the tools and techniques for big data used in Industrial Internet of +ings. +is report will certainly
help researchers and practitioners to easily use the tools and techniques for their need in an effective way.
1. Introduction
With the passage of time, the volume of data is increasing. In
today’s digital world, the information surges with the extensive use of the Internet and global communication systems. +is increase will create huge bulk of data which needs
proper tools and techniques to handle its management and
organization. Big data is ever playing an important role in
the industry as well as many other organizations. Huge bulk
of data is produced from the healthcare information systems,
electronic records, wearables, smart devices, handheld devices, and so on. +e recent increase in medical big data and
the development of computational techniques in the field of
information technology enable researchers and practitioners
to extract and visualize big data in a new spectrum of use.
+e industry is leading toward the spreading out and
developments of IIoT with the incorporation of emerging
technologies and applications of IoT. +e aim of the IIoT is
to achieve high efficiency of operations for management of
industrial assets and to increase the productivity of industries. More attention is given to the applications of IoT with
its integration to industries. +e applications of IoT are
obvious in every field of life from industry to education,
healthcare, and to other places. A number of studies are
available related to the applications, uses, and different
approaches to handle big data [1–8]. Different techniques
and tools are being used for extracting important information from big data. +e data are mostly unstructured
which need proper structure, shape, and management
through which the data can easily be accessed and processed.
+e role of visualization is to capture the important information from the data and to visualize it for the easiness of
practitioners. Some of the programming tools which deal
with big data are Informatica PowerCenter, Apache Hadoop,
and Tableau, which analyze data extremely efficiently and
enable the visualization of meaningful insights extracted
from big data.
To facilitate the management of data for easy access and
to operate, there should be a detailed report on the existing
tools and techniques which can easily access, manage,
operate, and execute useful information from the data for
different purposes. +erefore, to facilitate this process, a
detailed report of the existing literature is presented in this
study. +is detailed report will help researchers and scholars
Hindawi
Scientific Programming
Volume 2020, Article ID 8810634, 10 pages
https://doi.org/10.1155/2020/8810634
to devise new algorithms, techniques, and tools for the
analysis and management of big data.
+e organization of the paper is as follows. Section 2
shows the related work to big data tools and support of the
industry. Section 3 presents the existing approaches to
support big data in IIoT. Section 4 shows the support of IIoT
regarding big data tools and techniques. +e paper is
concluded in Section 5.
2. Big Data Tools and Support of the Industry
With the advancements in Industrial Internet of +ings
(IIoT) sensing, communication, technology characterizations, and high throughput instrumentation, the level of
data generation is expected to grow exponentially [9]. Lin
et al. [10] presented an approach of integrating sensing data
from diverse sources and equipment to apply on IIoT. +e
industrial Micro Control Unit is connected to interface
with actuator, data sources, and equipment. +e experimental results show that IIoT can reduce the problem of
heterogeneous protocol and database manufacturing data
transmission. +is article demonstrates the complexity and
unique nature of multimedia big data (MMBD) computing
for Internet of +ings (IoT) applications as well as builds up
an inclusive taxonomy used for MMBD abstracted into a
new process model reflecting MMBD over IoT. Many research challenges linked with MMBD, for example, quality
of service requirements, heterogeneity, reliability, accessibility, and scalability, are addressed by the process model.
+e process model is discussed through a case study [11]. In
this work, architecture for flood forecasting and monitoring is proposed by means of convergence between HPC
and big data. +is architecture can analyze, store, and
collect big data as well as help in the flood prediction result
generation [12]. Mobile computing services can be used in
IoT by using services of mobile phones, apps, or through
M-Health care system [13]. Alexopoulos et al. [14] presented the IIoT architecture and its development details to
support the industrial product service system life cycle.
In this article, a novel model is developed in the perspective of manufacturing progression that reviews the key
big data analytics (BDA) capabilities. +e findings are
beneficial for the companies in order to understand big
data potential implications as well as their analytics capabilities for their manufacturing processes and efficient
BDA-enabler infrastructure design [15]. Boyes et al. [16]
presented the concept of IIoT and the association to the
ideas such as cyber physical systems and Industry 4.0. IoTrelated taxonomies were analyzed and an analysis framework was developed for IIoT that can be used to list and
characterize the devices of IIoT when analyzing security
vulnerability and threats. For the big data sentiment
analysis (BDSA) and for best or optimal decision selection,
a framework was proposed and also applied as a mathematical algorithm [17]. In this study, for big data and
Cognitive Internet of +ings (CIoT), a new architecture is
proposed. +e planned architecture helps the computing
systems through combining data lake (DL) and warehouse
(DWH), and for the collection of heterogeneous data, a tool
is defined [18]. Urquhart and Mcauley [19] presented an
approach for the risks of IIoT drawn both on the regulatory
and technical perspectives. In this study, functional and
structural properties of cloud manufacturing (CMfg) were
analyzed, and a business intelligence architecture was
proposed that plans to empower distributing pertinent
KPIs identified with intrigued process data, with the helpful
layer of dependability [20].
An overview of big data in smart manufacturing was
directed, and an applied framework was proposed from the
viewpoint of item life cycle. +is framework permits examining key advantages and potential applications, and the
debate of future research directions and current challenges
gives essential insights for the industry and scholarly world
[21]. +is paper examines the current big data analytics
(BDA) technologies, strategies, and algorithms that can
prompt the improvement of insightful Industrial Internet of
+ings (IIoT) frameworks. We devise a scientific classification by characterizing and classifying the literature based
on essential factors (for example, analytics types, industrial
analytics applications, requirements, analytics techniques,
analytics tools, and data sources). +e case studies and
frameworks of different endeavours were presented which
have been profited by BDA [22]. +is paper investigates how
firms can capture an incentive from big data to improve
green commitment by giving an applied model through an
exhaustive and all-encompassing writing that relates big data
sources to the reception of various green systems. +e
principle finding of the examination is that organizations
that need to execute clean innovation strategy frequently
allude to outside accomplice to build up the essential architecture expected to abuse enormous information potentialities [23]. Apart from these approaches, the big data
and IoT have several other applications in diverse issues of
the real world [24–28].
3. Existing Approaches to Support Big
Data in IIoT
Humayun et al. [29] presented a comprehensive report of the
evolution, prevention, and mitigation of ransomware in the
context of IoT. For smart factories, construction path and
reference architecture were proposed by examining IIoT
technology as well as their application in assembling
workshops. Joined with the examination of business as usual
and requirements of the discrete assembling undertaking
workshops, this paper structures the overall theoretical
model architecture of the framework [30]. In this examination, a blockchain-dependent data sharing scheme was
proposed that entirely considers efficiency as well as security
of data sharing. In this plan, a Hyperledger Fabric and
identity authentication-dependent secure data sharing
structure was designed for the data sharing security. Additionally, a network recognition algorithm was proposed to
partition the customers into various data sharing networks
as per the comparability of mark data. +e exploratory
outcomes demonstrate that the proposed colloboration is
successful for efficient and secure data sharing among
various customers [31].
2 Scientific Programming
+is paper discusses about the IoT data management
concepts and current and survey solutions, talks about the
most encouraging solutions, and recognizes important open
exploration issues on the theme giving rules to assist further
contributions [32]. In this article, for a scalable pipeline to
distribute as well as process data as of blend of shop-floor
sources, an architecture was proposed. +e architecture was
implemented in order to explore the feasibility of this
methodology and bring together ad hoc power data and
MTConnect-compliant machine to help analytics applications [33]. +is work presents a procedure data examination
stage which worked around the idea of Industry 4.0. +e
platform uses the big data software tools, ML algorithms,
and state-of-the-art IIoT platforms. +e results indicated
that in situations where process information about the
procedure within reach is restricted, information-driven
delicate sensors are helpful instruments for predictive data
investigation [34]. For industrial data processing, an Industrial Internet of +ings cloud-fog hybrid network
(ITCFN) framework was proposed. +e results have shown
that the proposed framework effectively reduces the processing delay of industrial data [35].
In this study, a systematic strategy was used to review
the weaknesses as well as strengths of open-source
technologies for stream processing and big data to set up
its usage for Industry 4.0 use cases [36]. A framework was
developed for the additive manufacturing enterprises by
combining sustainable smart manufacturing technologies,
additive manufacturing, and big data analytics. +e
proposed framework is beneficial for additive
manufacturing industry leaders to take the right decision
at the beginning stage of the product life cycle [37]. +e
big data characteristic of the testbed was studied by using
an inhouse-developed IoT-enabled manufacturing testbed
[38]. A distributed service-oriented architecture was
provided for the solution of problem of product tracing
[39]. +e distributions of droplet size with high-velocity
airblast atomization were examined [40]. In this article, an
interactive data investigation framework was proposed,
which poses a service-oriented perspective on the smart
factory [23]. +is article investigates the potential of artificial intelligence (AI) as well as machine learning (ML)
to lever big data and Internet of +ings (IoT) in smart
cities in personalised service development. IoT smart city
applications are suggested so as to benefit from this work
[41]. Gierej [42] presented the idea of a business model for
the companies implementing IIoT technologies. +e approach is developed to help traditional companies in the
transition of the digital market.
+e proof procurement challenge is examined. A
contextual investigation of a smart city venture with IoT
administrations gathering big data which are put away in
the cloud processing condition is presented. +e strategies
can be summed up to other big data in the cloud environment [43]. A fault prediction technique dependent on
industrial big data is presented, which legitimately exhumes the connection between the data, for example, the
status as well as sound data, and the equipment faults by
machine learning techniques [44]. Distributed growing
self-organizing map (DGSOM) and a novel distributed selfadaptive neural network algorithm were presented to tackle
unsupervised machine learning need of big data [45].
Younan et al. [46] presented a study with a comprehensive
review of the existing challenges in the literature and
recommended technologies for enabling the analysis of
data and search in the future IoT search engines. Two case
studies are presented to show promising growth on
smartness and intelligence of applications of IoT based on
the integration of information and communication technologies. +e applications of smart phones enable the
patients to know about their diseases after the analysis in
the field of gynaecology and paediatrics [47]. In this article,
an architecture based on Internet of +ings is proposed for
big data that is used for diverse smart cities. +e results
demonstrated that this kind of method has the potential of
the applicability to give beneficial services of smart cities,
for example, detection of travel profiles in smart transport,
comfort in smart buildings, and management of the energy
consumption [48]. Jiang [49] presented an approach which
studies the IoT developments and technologies related to
cloud computing and smart cities and then focussed on the
IoT technologies and cloud computing. Dachyar et al. [50]
conducted an in-depth analysis of the 26420 papers published in the area of IoT. +is article aims to adapt and
detect concept drift dependent on cognitive learning
principles. +e approach executes to detect concept drift,
determines concept drift type as well as in automated time
windows [51]. Table 1 shows the existing approaches,
methods, and tools to support big data.
4. Support of IIoT regarding Big Data Tools
and Techniques
Several studies exist related to the applications of big data
in IIoT. +e study presented an enhanced platform of
industrial big data for the reduction of time and data
storage space of data processing [54]. +e aim of the paper
is to assess the impact of different serialization and compression methods on the platform of big data and then
attempt to select the most suitable method for the platform
of industry. +e aim of the study is to propose a fabric
which is a technique of blockchain-based data transmission
for IIoT [56]. +e approach uses secret sharing mechanism
based on blockchain. +e paper presented an approach of
city geospatial dashboard for the collection, sharing, and
visualization of the data collected from different sources
like satellite data, IoT devices, and other big data [58]. +e
contribution of the paper is to present the concept of
constructing community-based platform of cross IIoT
service through utilizing the existing mobile and fixed
facilities as wireless IoT gateway in a city which facilitates
the easy implementation of IoT gateway at local service for
bringing economical and social values [59]. +e study
focussed on the spatiotemporal modeling to organize the
data in temporal, attributive, and spatial dimensions [60].
To manage the multisource manufacturing data, ontologybased big data integration mechanism is presented. +e
authors proposed an ADTT—advanced distributed tensorScientific Programming 3
train—decomposition approach along with a computational method for the IIoT big data processing [64]. +e
existing literature was searched in order to identify the
associated materials related to the proposed study. For this
purpose, the popular libraries such as ACM, IEEE, ScienceDirect, and Springer were considered to show the
related materials. +e reason behind these libraries was that
these libraries publish quality materials which are peer
reviewed. Figure 1 shows the number of papers published
in the given years in the library of ScienceDirect. +e last
five years were considered as the latest research published
in these recent years.
Figure 2 shows the article type along with the number of
publications in the given library.
Figure 3 shows publication titles and percentage of
publications.
Figure 4 shows the articles types and number of publications in the library IEEE.
Figure 5 shows the publication topics and percentages of
number of publications.
Figure 6 shows the media format and number of publications in the ACM library.
Figure 7 shows the publication types and number of
papers published in the given library.
Figure 8 shows the number of publications in the given
years.
Figure 9 shows the article types and percentages of
publication in the Springer library.
Table 1: Existing approaches, methods, and tools to support big data.
S.No Reference Title
1 [9] Big data analytics tool based on statistical process monitoring for smart manufacturing
2 [11] Multimedia big data computation and applications of IoT
3 [12] IoT, big data, and HPC-based smart flood management framework
4 [15] Big data analytics for manufacturing processes
5 [17] An algorithmic implementation of entropic ternary reduct soft sentiment set using soft computing technique on big data
sentiment analysis for optimal selection of a decision based on real-time update in online reviews
6 [18] Architecture for Cognitive IoT and big data
7 [20] Challenges and opportunities for publishing IIoT data in manufacturing
8 [21] A comprehensive review of big data analytics throughout product life cycle to support sustainable smart manufacturing
9 [22] Role of big data analytics in IIoT
10 [23] Big data and natural environment
11 [30] Intelligent manufacturing production line data monitoring system for IIoT
12 [31] A secure and efficient data sharing scheme based on blockchain in IIoT
13 [32] Data management techniques for IoT
14 [33] Scalable data pipeline architecture to support the IIoT
15 [34] Industry 4.0-based process data analytics platform
16 [35] Optimization of IIoT data processing latency
17 [36] Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case
18 [37] Framework of big data for sustainable and smart additive manufacturing
19 [38] Feature engineering in big data analytics for IoT-enabled smart manufacturing
20 [39] An architecture for aggregating information from distributed data nodes for IIoT
21 [40] Application of big data analysis technique on high-velocity airblast atomization
22 [23] Interactive data exploration as a service for the smart factory
23 [41] Smart city services using machine learning, IoT, and big data
24 [43] Digital forensics challenges to big data in the cloud
25 [44] On fault prediction based on industrial big data
26 [45] Apache spark-based distributed self-organizing map algorithm for sensor data analysis
27 [48] Techniques of big data to smart city deployments
28 [51] A cognitive data stream mining technique for context-aware IoT systems
29 [52] Implementation of the FSO2
30 [53] An intelligent outlier detection method with one class support tucker machine and genetic algorithm toward big sensor
data in IoT
31 [54] Big data-based improved data acquisition and storage system for designing industrial data platform
32 [55] Cybersecurity in an IIoT environment
33 [56] A secure fabric blockchain-based data transmission technique for IIoT
34 [57] Concept drift detection and adaption in big imbalance IIoT data using an ensemble learning method of offline classifiers
35 [58] City geospatial dashboard
36 [59] A community-based IoT service platform to locally disseminate socially valuable data
37 [60] +e spatiotemporal modeling and integration of manufacturing big data in job shop
38 [61] A big data-enabled consolidated framework for energy efficient software defined data centers in IoT setups
39 [62] A parallel military dog-based algorithm for clustering big data in cognitive IIoT
40 [63] Big data cleaning based on mobile edge computing in industrial sensor cloud
41 [64] A highly efficient distributed tensor-train decomposition method for IIoT big data
42 [65] Big data-driven edge-cloud collaboration architecture for cloud manufacturing
4 Scientific Programming
24%
15%
9% 11% 14%
7%
6% 5% 5% 4%
Procedia manufacturing
IFAC-papersonline
Future generation computer systems
Journal of network and computer applications
Computer networks
Procedia CIRP
Procedia computer science
Computer communications
Computers in industry
Computers & industrial engineering
Figure 3: Publication titles and number of publications.
2016
11% 2017
14%
2018
19%
2019
26%
2020
30%
No. of papers
Figure 1: Number of papers in the given year for ScienceDirect.
Review articles
Research articles
Encyclopedia
Book chapters
Editorials
News
Short communications
Others
800
700
600
500
400
300
200
100
0
Figure 2: Article type and number of publications.
Scientific Programming 5
Internet of things
29%
Production engineering
computing
10%
Cloud computing
7% Learning (artifcial
intelligence)
6%
Security of data
4%
Big data
4%
Wireless sensor networks
4%
Data analysis
4%
Internet
3%t
Computer network
security
3%
Data privacy
2%
Protocols
2%
Cyberphysical
systems
2%
Optimisation
2%
Condition monitoring
2%
Cryptography
2%
Industrial control
2%
Mobile computing
2% Maintenance
engineering
2%
Factory automation
1%
Manufacturing systems
1% Data
mining
1% Fault diagnosis
1%
Control
engineering
computing
1%
Neural nets
1%
Number of papers
Internet of things Production engineering computing
Cloud computing
Learning (artifcial intelligence) Security of data
Wireless sensor networks Data analysis
Computer network security Data privacy
Cyber-physical systems Optimization
Cryptography Industrial control
Maintenance engineering Factory automation
Data mining Fault diagnosis
Big data
Internet
Protocols
Condition monitoring
Mobile computing
Manufacturing systems
Control engineering computing
Neural nets
Figure 5: Publication topics and percentage of publications.
Conference Journals Early-access
articles
Magazines Books
Number of papers
0
100
200
300
400
500
600
700
Figure 4: Articles type and number of publications.
6 Scientific Programming
5. Conclusion
With the passage of time, the volume of data is increasing. +is
increase will create huge bulk of data which needs proper tools
and techniques to handle its management and organization.
Big data is ever playing an important role in the industry as well
as many other organizations. Huge bulk of data is produced
from the healthcare information systems, electronic records,
wearables, smart devices, handheld devices, and so on. +e
recent increase in medical big data and the development of
computational techniques in the field of information technology enable researchers and practitioners to extract and
visualize big data in a new spectrum of use. Different techniques and tools are being used to properly handle the
management of data. A detailed report of these techniques and
tools is needed which will help researchers to easily identify a
tool for their data and take help to easily manage the data,
organize the data, and extract meaningful information from it.
+e proposed study is an endeavour toward summarizing and
identifing the tools and techniques for big data used in IIoT.
+is report will help researchers and practitioners to easily use
the tools and techniques for their need in an effective way and
will devise new solutions for the industry of big data.
Data Availability
No data were used to support this study.
Conflicts of Interest
+e authors declare that they have no conflicts of interest
regarding this paper.
Acknowledgments
+is study was sponsored in part by the Intelligent
Manufacturing Project of Tianjin (20193155).
63%
18%
17%
2%
Number of papers
Chapter
Article
Conference paper
Reference work entry
Figure 9: Content types and percentage of publications.
76%
12%
11% 0%
1%
Number of papers
PDF
Images
HTML
Archive/Zip
Epub
Figure 6: Media format and number of publications.
Proceedings, 120
Journals, 22
Newsletters, 4
Magazines, 2
0
20
40
60
80
100
120
Proceedings
Journals
Newsletters
Magazines
Number of papers
Figure 7: Publication types and number of papers.
Year 2016 2017 2018 2019
Years
0
50
100
150
200
250
300
350
Number of papers
Figure 8: Number of papers in the given years.
Scientific Programming 7
References
[1] E. Yadegaridehkordi, M. Nilashi, L. Shuib et al., “+e impact
of big data on firm performance in hotel industry,†Electronic
Commerce Research and Applications, vol. 40, p. 100921, 2020.
[2] S. Asadi, R. H. Abdullah, M. Safaei, and S. Nazir, “An integrated sem-neural network approach for predicting determinants of wearable healthcare devices adoption,†Mobile
Information Systems, vol. 2019, Article ID 8026042, 9 pages,
2019.
[3] S. Nazir, S. Khan, H. U. Khan et al., “A comprehensive analysis
of healthcare big data management, analytics and scientific
programming,†IEEE Access, vol. 8, pp. 95714–95733, 2020.
[4] S. Nazir, M. Nawaz, A. Adnan, S. Shahzad, and S. Asadi, “Big
data features, applications, and analytics in cardiology-A
systematic literature review,†IEEE Access, vol. 7, no. 1,
pp. 143742–143771, 2019.
[5] S. Nazir, M. Nawaz Khan, S. Anwar et al., “Big data visualization in cardiology-A systematic review and future directions,†IEEE Access, vol. 7, no. 1, pp. 115945–115958, 2019.
[6] A. U. Haq, “Intelligent machine learning approach for effective recognition of diabetes in the E-healthcare using
clinical data,†Sensors, vol. 20, 2020.
[7] S. Nazir, S. Ali, M. Yang, and Q. Xu, “Deep learning algorithms and multi-criteria decision making used in big data- a
systematic literature review,†Security and Communication
Networks, vol. 2020, Article ID 2836064, 19 pages, 2020.
[8] S. Nazir, A Comprehensive Analysis of Healthcare Big Data
Management, Analytics and Scientific Programming, IEEE
Access, Piscataway, NJ, USA, 2020.
[9] Q. P. He and J. Wang, “Statistical process monitoring as a big
data analytics tool for smart manufacturing,†Journal of
Process Control, vol. 67, pp. 35–43, 2018.
[10] Y. J. Lin, C.-B. Lan, and C.-Y. Huang, “A realization of cyberphysical manufacturing Control system through industrial
internet of things,†Procedia Manufacturing, vol. 39,
pp. 287–293, 2019.
[11] A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. Maasberg, and
K.-K. R. Choo, “Multimedia big data computing and internet
of things applications: a taxonomy and process model,â€
Journal of Network and Computer Applications, vol. 124,
pp. 169–195, 2018.
[12] S. K. Sood, R. Sandhu, K. Singla, and V. Chang, “IoT, big data
and HPC based smart flood management framework,†Sustainable Computing: Informatics and Systems, vol. 20,
pp. 102–117, 2018.
[13] S. H. Almotiri, M. A. Khan, and M. A. Alghamdi, “Mobile
health (m-health) system in the context of IoT,†in Proceedings
of the 2016 IEEE 4th International Conference on Future Internet of 3ings and Cloud Workshops, IEEE, Vienna, Austria,
pp. 39–42, August 2016.
[14] K. Alexopoulos, S. Koukas, N. Boli, and D. Mourtzis, “Architecture and development of an industrial internet of things
framework for realizing services in industrial product service
systems,†Procedia CIRP, vol. 72, pp. 880–885, 2018.
[15] A. Belhadi, K. Zkik, A. Cherrafi, S. R. M. Yusof, and S. El
fezazi, “Understanding big data analytics for manufacturing
processes: insights from literature review and multiple case
studies,†Computers & Industrial Engineering, vol. 137, Article
ID 106099, 2019.
[16] H. Boyes, B. Hallaq, J. Cunningham, and T. Watson, “+e
industrial internet of things (IIoT): an analysis framework,â€
Computers in Industry, vol. 101, pp. 1–12, 2018.
[17] A. Dwivedi and R. P. Pant, “An algorithmic implementation
of entropic ternary reduct soft sentiment set (ETRSSS) using
soft computing technique on big data sentiment analysis
(BDSA) for optimal selection of a decision based on real-time
update in online reviews,†Journal of King Saud UniversityComputer and Information Sciences, 2019.
[18] M. S. Hadj Sassi, F. G. Jedidi, and L. C. Fourati, “A new
architecture for cognitive internet of things and big data,â€
Procedia Computer Science, vol. 159, pp. 534–543, 2019.
[19] L. Urquhart and D. McAuley, “Avoiding the internet of insecure industrial things,†Computer Law & Security Review,
vol. 34, no. 3, pp. 450–466, 2018.
[20] J. Ordieres-Mere, J. Villalba-D ´ ´ıez, and X. Zheng, “Challenges
and opportunities for publishing IIoT data in manufacturing
as a service business,†Procedia Manufacturing, vol. 39,
pp. 185–193, 2019.
[21] S. Ren, Y. Zhang, Y. Liu, T. Sakao, D. Huisingh, and
C. M. V. B. Almeida, “A comprehensive review of big data
analytics throughout product life cycle to support sustainable
smart manufacturing: a framework, challenges and future
research directions,†Journal of Cleaner Production, vol. 210,
pp. 1343–1365, 2019.
[22] M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran,
P. P. Jayaraman, and C. Perera, “+e role of big data analytics
in industrial internet of things,†Future Generation Computer
Systems, vol. 99, pp. 247–259, 2019.
[23] F. Calza, A. Parmentola, and I. Tutore, “Big data and natural
environment. How does different data support different green
strategies?†Sustainable Futures, vol. 2, Article ID 100029,
2020.
[24] V. F. Brock and H. U. Khan, “Are enterprises ready for big
data analytics? A survey-based approach,†International
Journal of Business Information Systems, vol. 25, no. 2,
pp. 256–277, 2017.
[25] V. Brock and H. U. Khan, “Big data analytics: does organizational factor matters impact technology acceptance?†vol. 4,
no. 1, p. 21, 2017.
[26] B. Liao, Y. Ali, S. Nazir, L. He, and H. U. Khan, “Security
analysis of IoT devices by using mobile computing: a systematic literature review,†IEEE Access, vol. 8, pp. 120331–
120350, 2020.
[27] M. Madhuri, A. Q. Gill, and H. U. Khan, “IoT-enabled smart
child safety digital system architecture,†in Proceedings of the
2020 IEEE 14th International Conference on Semantic Computing, IEEE, San Diego, CA, USA, pp. 166–169, 2020.
[28] A. Q. Gill, G. Beydoun, M. Niazi, and H. U. Khan,
“Adaptive architecture and principles for securing the IoT
systems,†in Proceedings of the International Conference on
Innovative Mobile and Internet Services in Ubiquitous
Computing, pp. 173–182, Springer, Lodz, Poland, 2020.
[29] M. Humayun, N. Z. Jhanjhi, A. Alsayat, and V. Ponnusamy,
“Internet of things and ransomware: evolution, mitigation
and prevention,†Egyptian Informatics Journal, 2020.
[30] W. Chen, “Intelligent manufacturing production line data
monitoring system for industrial internet of things,†Computer Communications, vol. 151, pp. 31–41, 2020.
[31] J. Chi, Y. Li, J. Huang et al., “A secure and efficient data
sharing scheme based on blockchain in industrial internet of
things,†Journal of Network and Computer Applications,
vol. 167, Article ID 102710, 2020.
[32] B. Diène, J. J. P. C. Rodrigues, O. Diallo, E. L. H. M. Ndoye,
and V. V. Korotaev, “Data management techniques for internet of things,†Mechanical Systems and Signal Processing,
vol. 138, Article ID 106564, 2020.
8 Scientific Programming
[33] M. Helu, T. Sprock, D. Hartenstine, R. Venketesh, and
W. Sobel, “Scalable data pipeline architecture to support the
industrial internet of things,†CIRP Annals, vol. 69, no. 1,
pp. 385–388, 2020.
[34] J. C. Kabugo, S.-L. J¨ams¨a-Jounela, R. Schiemann, and
C. Binder, “Industry 4.0 based process data analytics platform:
a waste-to-energy plant case study,†International Journal of
Electrical Power & Energy Systems, vol. 115, Article ID 105508,
2020.
[35] W. Liu, G. Huang, A. Zheng, and J. Liu, “Research on the
optimization of IIoT data processing latency,†Computer
Communications, vol. 151, pp. 290–298, 2020.
[36] R. Sahal, J. G. Breslin, and M. I. Ali, “Big data and stream
processing platforms for Industry 4.0 requirements mapping
for a predictive maintenance use case,†Journal of
Manufacturing Systems, vol. 54, pp. 138–151, 2020.
[37] A. Majeed, Y. Zhang, S. Ren et al., “A big data-driven
framework for sustainable and smart additive manufacturing,†Robotics and Computer-Integrated Manufacturing,
vol. 67, Article ID 102026, 2021.
[38] D. Shah, J. Wang, and Q. P. He, “Feature engineering in big
data analytics for IoT-enabled smart manufacturing – comparison between deep learning and statistical learning,â€
Computers & Chemical Engineering, vol. 141, Article ID
106970, 2020.
[39] T. Zhu, S. Dhelim, Z. Zhou, S. Yang, and H. Ning, “An architecture for aggregating information from distributed data
nodes for industrial internet of things,†Computers & Electrical Engineering, vol. 58, pp. 337–349, 2017.
[40] A. Urb´an, A. Groniewsky, M. Mal´y, V. J´ozsa, and J. Jedelsk´y,
“Application of big data analysis technique on high-velocity
airblast atomization: searching for optimum probability
density function,†Fuel, vol. 273, Article ID 117792, 2020.
[41] J. Chin, V. Callaghan, and I. Lam, “Understanding and
personalising smart city services using machine learning, the
Internet-of-+ings and Big Data,†in Proceedings of the 2017
IEEE 26th International Symposium on Industrial Electronics
(ISIE), pp. 2050–2055, Edinburgh, Scotland, June 2017.
[42] S. Gierej, “+e framework of business model in the context of
industrial internet of things,†Procedia Engineering, vol. 182,
pp. 206–212, 2017.
[43] X. Feng and Y. Zhao, “Digital forensics challenges to big data
in the cloud,†in Proceedings of the 2017 IEEE International
Conference on Internet of 3ings (i3ings) and IEEE Green
Computing and Communications (GreenCom) and IEEE
Cyber, Physical and Social Computing (CPSCom) and IEEE
Smart Data (SmartData), pp. 858–862, Exeter, UK, June 2017.
[44] Q. Han, H. Li, W. Dong, Y. Luo, and Y. Xia, “On fault
prediction based on industrial big data,†in Proceedings of the
2017 36th Chinese Control Conference (CCC), pp. 10127–
10131, Dalian, China, July 2017.
[45] M. Jayaratne, D. Alahakoon, D. D. Silva, and X. Yu, “Apache
spark based distributed self-organizing map algorithm for
sensor data analysis,†in Proceedings of the IECON 2017-43rd
Annual Conference of the IEEE Industrial Electronics Society,
pp. 8343–8349, Beijing, China, November 2017.
[46] M. Younan, E. H. Houssein, M. Elhoseny, and A. A. Ali,
“Challenges and recommended technologies for the industrial
internet of things: a comprehensive review,†Measurement,
vol. 151, p. 107198, 2020.
[47] Y. Karaca, M. Moonis, Y.-D. Zhang, and C. Gezgez, “Mobile
cloud computing based stroke healthcare system,†International Journal of Information Management, vol. 45, pp. 250–
261, 2019.
[48] M. V. Moreno, F. Terroso-Saenz, A. Gonzalez-Vidal et al.,
“Applicability of big data techniques to smart cities deployments,†IEEE Transactions on Industrial Informatics, vol. 13,
no. 2, pp. 800–809, 2017.
[49] D. Jiang, “+e construction of smart city information system
based on the Internet of +ings and cloud computing,â€
Computer Communications, vol. 150, pp. 158–166, 2020.
[50] M. Dachyar, T. Y. M. Zagloel, and L. R. Saragih, “Knowledge
growth and development: internet of things (IoT) research,
2006ˆa€“2018,†Heliyon, vol. 5, no. 8, Article ID e02264, 2019.
[51] D. Nallaperuma, D. D. Silva, D. Alahakoon, and X. Yu, “A
cognitive data stream mining technique for context-aware IoT
systems,†in Proceedings of the IECON 2017-43rd Annual
Conference of the IEEE Industrial Electronics Society,
pp. 4777–4782, Beijing, China, November 2017.
[52] A. Bamrungwong, “Implementation of the FSO2 life extension program by using big data and IIoT,†in Proceedings of
the 2019 Petroleum and Chemical Industry Conference Europe
(PCIC EUROPE), pp. 1–8, Paris, France, May 2019.
[53] X. Deng, P. Jiang, X. Peng, and C. Mi, “An intelligent outlier
detection method with one class support tucker machine and
genetic algorithm toward big sensor data in internet of
things,†IEEE Transactions on Industrial Electronics, vol. 66,
no. 6, pp. 4672–4683, 2019.
[54] D. Geng, C. Zhang, C. Xia, X. Xia, Q. Liu, and X. Fu, “Big databased improved data acquisition and storage system for designing industrial data platform,†IEEE Access, vol. 7,
pp. 44574–44582, 2019.
[55] F. A. B. Juárez, “Cybersecurity in an industrial internet of
things environment (IIoT) challenges for standards systems
and evaluation models,†in Proceedings of the 2019 8th International Conference On Software Process Improvement,
vol. 23, pp. 1–6, Leon, Mexico, October 2019.
[56] W. Liang, M. Tang, J. Long, X. Peng, J. Xu, and K. C. Li, “A
secure FaBric blockchain-based data transmission technique
for industrial internet-of-things,†IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3582–3592, 2019.
[57] C.-C. Lin, D.-J. Deng, C.-H. Kuo, and L. Chen, “Concept drift
detection and adaption in big imbalance industrial IoT data
using an ensemble learning method of offline classifiers,†IEEE
Access, vol. 7, pp. 56198–56207, 2019.
[58] K. K. Lwin, Y. Sekimoto, W. Takeuchi, and K. Zettsu, “City
geospatial dashboard: IoT and big data analytics for geospatial
solutions provider in disaster management,†in Proceedings of
the 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM),
pp. 1–4, December 2019.
[59] Y. Shoji, K. Nakauchi, W. Liu, Y. Watanabe, K. Maruyama,
and K. Okamoto, “A community-based IoT service platform
to locally disseminate socially-valuable data :best effort local
data sharing network with no conscious effort?†in Proceedings of the 2019 IEEE 5th World Forum on Internet of
3ings (WF-IoT), pp. 724–728, April 2019.
[60] W. Fang, Y. Guo, W. Liao, S. Huang, C. Yang, and K. Cui,
“+e spatio-temporal modeling and integration of
manufacturing big data in job shop: an ontology-based approach,†in IEEE 7th International Conference Oon Industrial
Engineering Aand Applications (ICIEA), 16-21 April 2020
2020, pp. 394–398, 2020.
[61] K. Kaur, S. Garg, G. Kaddoum, E. Bou-Harb, and
K.-K. R. Choo, “A bBig dData-eEnabled cConsolidated
fFramework for eEnergy eEfficient sSoftware dDefined dData
cCenters in IoT sSetups,†IEEE Transactions on Industrial
Informatics, vol. 16, no. 4, pp. 2687–2697, 2020.
Scientific Programming 9
[62] A. K. Tripathi, K. Sharma, M. Bala, A. Kumar, V. G. Menon,
and A. K. Bashir, “A parallel military dog based algorithm for
clustering big data in cognitive industrial internet of things,â€
IEEE Transactions on Industrial Informatics, p. 1, 2020.
[63] T. Wang, H. Ke, X. Zheng, K. Wang, A. K. Sangaiah, and
A. Liu, “Big data cleaning based on mobile edge computing in
industrial sensor-cloud,†IEEE Transactions on Industrial
Informatics, vol. 16, no. 2, pp. 1321–1329, 2020.
[64] X. Wang, L. T. Yang, L. Song, H. Wang, L. Ren, and J. Deen,
“A tensor-based multi-attributes visual feature recognition
method for industrial intelligence,†IEEE Transactions on
Industrial Informatics, p. 1, 2020.
[65] C. Yang, S. Lan, L. Wang, W. Shen, and G. G. Q. Huang, “Big
data driven edge-cloud collaboration architecture for cloud
manufacturing: a software defined perspective,†IEEE Access
,
vol. 8, pp. 45938–45950, 2020.
10 Scientific Programming
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