Use of Big Data Tools and Industrial Internet of Things:
Yingzi Wang ,
1 Muhammad Nazir Jan,2 Sisi Chu,3 and Yue Zhu3
College of Intelligence and Computing, TianJin University, TianJin 300300, China
Department of Computer Science, University of Swabi, Swabi, Pakistan
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 Â© 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.
With the passage of time, the volume of data is increasing. In
todayâ€™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 [1â€“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
Volume 2020, Article ID 8810634, 10 pages
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 . Lin
et al.  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 . 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 . Mobile computing services can be used in
IoT by using services of mobile phones, apps, or through
M-Health care system . Alexopoulos et al.  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 . Boyes et al. 
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 . 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 . Urquhart and Mcauley  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 .
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
. +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 . +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 . Apart from these approaches, the big data
and IoT have several other applications in diverse issues of
the real world [24â€“28].
3. Existing Approaches to Support Big
Data in IIoT
Humayun et al.  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 . 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 .
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 . 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 . +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 . 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 .
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 . 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 . +e
big data characteristic of the testbed was studied by using
an inhouse-developed IoT-enabled manufacturing testbed
. A distributed service-oriented architecture was
provided for the solution of problem of product tracing
. +e distributions of droplet size with high-velocity
airblast atomization were examined . In this article, an
interactive data investigation framework was proposed,
which poses a service-oriented perspective on the smart
factory . +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
. Gierej  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 . 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 . 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 .
Younan et al.  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 . 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 . Jiang  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. 
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 . Table 1 shows the existing approaches,
methods, and tools to support big data.
4. Support of IIoT regarding Big Data Tools
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 . +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 . +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 . +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 . +e study
focussed on the spatiotemporal modeling to organize the
data in temporal, attributive, and spatial dimensions .
To manage the multisource manufacturing data, ontologybased big data integration mechanism is presented. +e
authors proposed an ADTTâ€”advanced distributed tensorScientific Programming 3
trainâ€”decomposition approach along with a computational method for the IIoT big data processing . +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
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
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  Big data analytics tool based on statistical process monitoring for smart manufacturing
2  Multimedia big data computation and applications of IoT
3  IoT, big data, and HPC-based smart flood management framework
4  Big data analytics for manufacturing processes
5  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  Architecture for Cognitive IoT and big data
7  Challenges and opportunities for publishing IIoT data in manufacturing
8  A comprehensive review of big data analytics throughout product life cycle to support sustainable smart manufacturing
9  Role of big data analytics in IIoT
10  Big data and natural environment
11  Intelligent manufacturing production line data monitoring system for IIoT
12  A secure and efficient data sharing scheme based on blockchain in IIoT
13  Data management techniques for IoT
14  Scalable data pipeline architecture to support the IIoT
15  Industry 4.0-based process data analytics platform
16  Optimization of IIoT data processing latency
17  Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case
18  Framework of big data for sustainable and smart additive manufacturing
19  Feature engineering in big data analytics for IoT-enabled smart manufacturing
20  An architecture for aggregating information from distributed data nodes for IIoT
21  Application of big data analysis technique on high-velocity airblast atomization
22  Interactive data exploration as a service for the smart factory
23  Smart city services using machine learning, IoT, and big data
24  Digital forensics challenges to big data in the cloud
25  On fault prediction based on industrial big data
26  Apache spark-based distributed self-organizing map algorithm for sensor data analysis
27  Techniques of big data to smart city deployments
28  A cognitive data stream mining technique for context-aware IoT systems
29  Implementation of the FSO2
30  An intelligent outlier detection method with one class support tucker machine and genetic algorithm toward big sensor
data in IoT
31  Big data-based improved data acquisition and storage system for designing industrial data platform
32  Cybersecurity in an IIoT environment
33  A secure fabric blockchain-based data transmission technique for IIoT
34  Concept drift detection and adaption in big imbalance IIoT data using an ensemble learning method of offline classifiers
35  City geospatial dashboard
36  A community-based IoT service platform to locally disseminate socially valuable data
37  +e spatiotemporal modeling and integration of manufacturing big data in job shop
38  A big data-enabled consolidated framework for energy efficient software defined data centers in IoT setups
39  A parallel military dog-based algorithm for clustering big data in cognitive IIoT
40  Big data cleaning based on mobile edge computing in industrial sensor cloud
41  A highly efficient distributed tensor-train decomposition method for IIoT big data
42  Big data-driven edge-cloud collaboration architecture for cloud manufacturing
4 Scientific Programming
9% 11% 14%
6% 5% 5% 4%
Future generation computer systems
Journal of network and computer applications
Procedia computer science
Computers in industry
Computers & industrial engineering
Figure 3: Publication titles and number of publications.
No. of papers
Figure 1: Number of papers in the given year for ScienceDirect.
Figure 2: Article type and number of publications.
Scientific Programming 5
Internet of things
7% Learning (artifcial
Security of data
Wireless sensor networks
1% Fault diagnosis
Number of papers
Internet of things Production engineering 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
Control engineering computing
Figure 5: Publication topics and percentage of publications.
Conference Journals Early-access
Number of papers
Figure 4: Articles type and number of publications.
6 Scientific Programming
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.
No data were used to support this study.
Conflicts of Interest
+e authors declare that they have no conflicts of interest
regarding this paper.
+is study was sponsored in part by the Intelligent
Manufacturing Project of Tianjin (20193155).
Number of papers
Reference work entry
Figure 9: Content types and percentage of publications.
Number of papers
Figure 6: Media format and number of publications.
Number of papers
Figure 7: Publication types and number of papers.
Year 2016 2017 2018 2019
Number of papers
Figure 8: Number of papers in the given years.
Scientific Programming 7
 E. Yadegaridehkordi, M. Nilashi, L. Shuib et al., â€œ+e impact
of big data on firm performance in hotel industry,â€ Electronic
Commerce Research and Applications, vol. 40, p. 100921, 2020.
 S. Asadi, R. H. Abdullah, M. Safaei, and S. Nazir, â€œAn integrated sem-neural network approach for predicting determinants of wearable healthcare devices adoption,â€ Mobile
Information Systems, vol. 2019, Article ID 8026042, 9 pages,
 S. Nazir, S. Khan, H. U. Khan et al., â€œA comprehensive analysis
of healthcare big data management, analytics and scientific
programming,â€ IEEE Access, vol. 8, pp. 95714â€“95733, 2020.
 S. Nazir, M. Nawaz, A. Adnan, S. Shahzad, and S. Asadi, â€œBig
data features, applications, and analytics in cardiology-A
systematic literature review,â€ IEEE Access, vol. 7, no. 1,
pp. 143742â€“143771, 2019.
 S. Nazir, M. Nawaz Khan, S. Anwar et al., â€œBig data visualization in cardiology-A systematic review and future directions,â€ IEEE Access, vol. 7, no. 1, pp. 115945â€“115958, 2019.
 A. U. Haq, â€œIntelligent machine learning approach for effective recognition of diabetes in the E-healthcare using
clinical data,â€ Sensors, vol. 20, 2020.
 S. Nazir, S. Ali, M. Yang, and Q. Xu, â€œDeep learning algorithms and multi-criteria decision making used in big data- a
systematic literature review,â€ Security and Communication
Networks, vol. 2020, Article ID 2836064, 19 pages, 2020.
 S. Nazir, A Comprehensive Analysis of Healthcare Big Data
Management, Analytics and Scientific Programming, IEEE
Access, Piscataway, NJ, USA, 2020.
 Q. P. He and J. Wang, â€œStatistical process monitoring as a big
data analytics tool for smart manufacturing,â€ Journal of
Process Control, vol. 67, pp. 35â€“43, 2018.
 Y. J. Lin, C.-B. Lan, and C.-Y. Huang, â€œA realization of cyberphysical manufacturing Control system through industrial
internet of things,â€ Procedia Manufacturing, vol. 39,
pp. 287â€“293, 2019.
 A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. Maasberg, and
K.-K. R. Choo, â€œMultimedia big data computing and internet
of things applications: a taxonomy and process model,â€
Journal of Network and Computer Applications, vol. 124,
pp. 169â€“195, 2018.
 S. K. Sood, R. Sandhu, K. Singla, and V. Chang, â€œIoT, big data
and HPC based smart flood management framework,â€ Sustainable Computing: Informatics and Systems, vol. 20,
pp. 102â€“117, 2018.
 S. H. Almotiri, M. A. Khan, and M. A. Alghamdi, â€œMobile
health (m-health) system in the context of IoT,â€ in Proceedings
of the 2016 IEEE 4th International Conference on Future Internet of 3ings and Cloud Workshops, IEEE, Vienna, Austria,
pp. 39â€“42, August 2016.
 K. Alexopoulos, S. Koukas, N. Boli, and D. Mourtzis, â€œArchitecture and development of an industrial internet of things
framework for realizing services in industrial product service
systems,â€ Procedia CIRP, vol. 72, pp. 880â€“885, 2018.
 A. Belhadi, K. Zkik, A. Cherrafi, S. R. M. Yusof, and S. El
fezazi, â€œUnderstanding big data analytics for manufacturing
processes: insights from literature review and multiple case
studies,â€ Computers & Industrial Engineering, vol. 137, Article
ID 106099, 2019.
 H. Boyes, B. Hallaq, J. Cunningham, and T. Watson, â€œ+e
industrial internet of things (IIoT): an analysis framework,â€
Computers in Industry, vol. 101, pp. 1â€“12, 2018.
 A. Dwivedi and R. P. Pant, â€œAn 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,â€ Journal of King Saud UniversityComputer and Information Sciences, 2019.
 M. S. Hadj Sassi, F. G. Jedidi, and L. C. Fourati, â€œA new
architecture for cognitive internet of things and big data,â€
Procedia Computer Science, vol. 159, pp. 534â€“543, 2019.
 L. Urquhart and D. McAuley, â€œAvoiding the internet of insecure industrial things,â€ Computer Law & Security Review,
vol. 34, no. 3, pp. 450â€“466, 2018.
 J. Ordieres-Mere, J. Villalba-D Â´ Â´Ä±ez, and X. Zheng, â€œChallenges
and opportunities for publishing IIoT data in manufacturing
as a service business,â€ Procedia Manufacturing, vol. 39,
pp. 185â€“193, 2019.
 S. Ren, Y. Zhang, Y. Liu, T. Sakao, D. Huisingh, and
C. M. V. B. Almeida, â€œA comprehensive review of big data
analytics throughout product life cycle to support sustainable
smart manufacturing: a framework, challenges and future
research directions,â€ Journal of Cleaner Production, vol. 210,
pp. 1343â€“1365, 2019.
 M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran,
P. P. Jayaraman, and C. Perera, â€œ+e role of big data analytics
in industrial internet of things,â€ Future Generation Computer
Systems, vol. 99, pp. 247â€“259, 2019.
 F. Calza, A. Parmentola, and I. Tutore, â€œBig data and natural
environment. How does different data support different green
strategies?â€ Sustainable Futures, vol. 2, Article ID 100029,
 V. F. Brock and H. U. Khan, â€œAre enterprises ready for big
data analytics? A survey-based approach,â€ International
Journal of Business Information Systems, vol. 25, no. 2,
pp. 256â€“277, 2017.
 V. Brock and H. U. Khan, â€œBig data analytics: does organizational factor matters impact technology acceptance?â€ vol. 4,
no. 1, p. 21, 2017.
 B. Liao, Y. Ali, S. Nazir, L. He, and H. U. Khan, â€œSecurity
analysis of IoT devices by using mobile computing: a systematic literature review,â€ IEEE Access, vol. 8, pp. 120331â€“
 M. Madhuri, A. Q. Gill, and H. U. Khan, â€œIoT-enabled smart
child safety digital system architecture,â€ in Proceedings of the
2020 IEEE 14th International Conference on Semantic Computing, IEEE, San Diego, CA, USA, pp. 166â€“169, 2020.
 A. Q. Gill, G. Beydoun, M. Niazi, and H. U. Khan,
â€œAdaptive architecture and principles for securing the IoT
systems,â€ in Proceedings of the International Conference on
Innovative Mobile and Internet Services in Ubiquitous
Computing, pp. 173â€“182, Springer, Lodz, Poland, 2020.
 M. Humayun, N. Z. Jhanjhi, A. Alsayat, and V. Ponnusamy,
â€œInternet of things and ransomware: evolution, mitigation
and prevention,â€ Egyptian Informatics Journal, 2020.
 W. Chen, â€œIntelligent manufacturing production line data
monitoring system for industrial internet of things,â€ Computer Communications, vol. 151, pp. 31â€“41, 2020.
 J. Chi, Y. Li, J. Huang et al., â€œA secure and efficient data
sharing scheme based on blockchain in industrial internet of
things,â€ Journal of Network and Computer Applications,
vol. 167, Article ID 102710, 2020.
 B. DiÃƒÂ¨ne, J. J. P. C. Rodrigues, O. Diallo, E. L. H. M. Ndoye,
and V. V. Korotaev, â€œData management techniques for internet of things,â€ Mechanical Systems and Signal Processing,
vol. 138, Article ID 106564, 2020.
8 Scientific Programming
 M. Helu, T. Sprock, D. Hartenstine, R. Venketesh, and
W. Sobel, â€œScalable data pipeline architecture to support the
industrial internet of things,â€ CIRP Annals, vol. 69, no. 1,
pp. 385â€“388, 2020.
 J. C. Kabugo, S.-L. JÂ¨amsÂ¨a-Jounela, R. Schiemann, and
C. Binder, â€œIndustry 4.0 based process data analytics platform:
a waste-to-energy plant case study,â€ International Journal of
Electrical Power & Energy Systems, vol. 115, Article ID 105508,
 W. Liu, G. Huang, A. Zheng, and J. Liu, â€œResearch on the
optimization of IIoT data processing latency,â€ Computer
Communications, vol. 151, pp. 290â€“298, 2020.
 R. Sahal, J. G. Breslin, and M. I. Ali, â€œBig data and stream
processing platforms for Industry 4.0 requirements mapping
for a predictive maintenance use case,â€ Journal of
Manufacturing Systems, vol. 54, pp. 138â€“151, 2020.
 A. Majeed, Y. Zhang, S. Ren et al., â€œA big data-driven
framework for sustainable and smart additive manufacturing,â€ Robotics and Computer-Integrated Manufacturing,
vol. 67, Article ID 102026, 2021.
 D. Shah, J. Wang, and Q. P. He, â€œFeature 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
 T. Zhu, S. Dhelim, Z. Zhou, S. Yang, and H. Ning, â€œAn architecture for aggregating information from distributed data
nodes for industrial internet of things,â€ Computers & Electrical Engineering, vol. 58, pp. 337â€“349, 2017.
 A. UrbÂ´an, A. Groniewsky, M. MalÂ´y, V. JÂ´ozsa, and J. JedelskÂ´y,
â€œApplication of big data analysis technique on high-velocity
airblast atomization: searching for optimum probability
density function,â€ Fuel, vol. 273, Article ID 117792, 2020.
 J. Chin, V. Callaghan, and I. Lam, â€œUnderstanding and
personalising smart city services using machine learning, the
Internet-of-+ings and Big Data,â€ in Proceedings of the 2017
IEEE 26th International Symposium on Industrial Electronics
(ISIE), pp. 2050â€“2055, Edinburgh, Scotland, June 2017.
 S. Gierej, â€œ+e framework of business model in the context of
industrial internet of things,â€ Procedia Engineering, vol. 182,
pp. 206â€“212, 2017.
 X. Feng and Y. Zhao, â€œDigital forensics challenges to big data
in the cloud,â€ 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. 858â€“862, Exeter, UK, June 2017.
 Q. Han, H. Li, W. Dong, Y. Luo, and Y. Xia, â€œOn fault
prediction based on industrial big data,â€ in Proceedings of the
2017 36th Chinese Control Conference (CCC), pp. 10127â€“
10131, Dalian, China, July 2017.
 M. Jayaratne, D. Alahakoon, D. D. Silva, and X. Yu, â€œApache
spark based distributed self-organizing map algorithm for
sensor data analysis,â€ in Proceedings of the IECON 2017-43rd
Annual Conference of the IEEE Industrial Electronics Society,
pp. 8343â€“8349, Beijing, China, November 2017.
 M. Younan, E. H. Houssein, M. Elhoseny, and A. A. Ali,
â€œChallenges and recommended technologies for the industrial
internet of things: a comprehensive review,â€ Measurement,
vol. 151, p. 107198, 2020.
 Y. Karaca, M. Moonis, Y.-D. Zhang, and C. Gezgez, â€œMobile
cloud computing based stroke healthcare system,â€ International Journal of Information Management, vol. 45, pp. 250â€“
 M. V. Moreno, F. Terroso-Saenz, A. Gonzalez-Vidal et al.,
â€œApplicability of big data techniques to smart cities deployments,â€ IEEE Transactions on Industrial Informatics, vol. 13,
no. 2, pp. 800â€“809, 2017.
 D. Jiang, â€œ+e construction of smart city information system
based on the Internet of +ings and cloud computing,â€
Computer Communications, vol. 150, pp. 158â€“166, 2020.
 M. Dachyar, T. Y. M. Zagloel, and L. R. Saragih, â€œKnowledge
growth and development: internet of things (IoT) research,
2006Ë†aâ‚¬â€œ2018,â€ Heliyon, vol. 5, no. 8, Article ID e02264, 2019.
 D. Nallaperuma, D. D. Silva, D. Alahakoon, and X. Yu, â€œA
cognitive data stream mining technique for context-aware IoT
systems,â€ in Proceedings of the IECON 2017-43rd Annual
Conference of the IEEE Industrial Electronics Society,
pp. 4777â€“4782, Beijing, China, November 2017.
 A. Bamrungwong, â€œImplementation of the FSO2 life extension program by using big data and IIoT,â€ in Proceedings of
the 2019 Petroleum and Chemical Industry Conference Europe
(PCIC EUROPE), pp. 1â€“8, Paris, France, May 2019.
 X. Deng, P. Jiang, X. Peng, and C. Mi, â€œAn intelligent outlier
detection method with one class support tucker machine and
genetic algorithm toward big sensor data in internet of
things,â€ IEEE Transactions on Industrial Electronics, vol. 66,
no. 6, pp. 4672â€“4683, 2019.
 D. Geng, C. Zhang, C. Xia, X. Xia, Q. Liu, and X. Fu, â€œBig databased improved data acquisition and storage system for designing industrial data platform,â€ IEEE Access, vol. 7,
pp. 44574â€“44582, 2019.
 F. A. B. JuÃƒÂ¡rez, â€œCybersecurity in an industrial internet of
things environment (IIoT) challenges for standards systems
and evaluation models,â€ in Proceedings of the 2019 8th International Conference On Software Process Improvement,
vol. 23, pp. 1â€“6, Leon, Mexico, October 2019.
 W. Liang, M. Tang, J. Long, X. Peng, J. Xu, and K. C. Li, â€œA
secure FaBric blockchain-based data transmission technique
for industrial internet-of-things,â€ IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3582â€“3592, 2019.
 C.-C. Lin, D.-J. Deng, C.-H. Kuo, and L. Chen, â€œConcept drift
detection and adaption in big imbalance industrial IoT data
using an ensemble learning method of offline classifiers,â€ IEEE
Access, vol. 7, pp. 56198â€“56207, 2019.
 K. K. Lwin, Y. Sekimoto, W. Takeuchi, and K. Zettsu, â€œCity
geospatial dashboard: IoT and big data analytics for geospatial
solutions provider in disaster management,â€ in Proceedings of
the 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM),
pp. 1â€“4, December 2019.
 Y. Shoji, K. Nakauchi, W. Liu, Y. Watanabe, K. Maruyama,
and K. Okamoto, â€œA community-based IoT service platform
to locally disseminate socially-valuable data :best effort local
data sharing network with no conscious effort?â€ in Proceedings of the 2019 IEEE 5th World Forum on Internet of
3ings (WF-IoT), pp. 724â€“728, April 2019.
 W. Fang, Y. Guo, W. Liao, S. Huang, C. Yang, and K. Cui,
â€œ+e spatio-temporal modeling and integration of
manufacturing big data in job shop: an ontology-based approach,â€ in IEEE 7th International Conference Oon Industrial
Engineering Aand Applications (ICIEA), 16-21 April 2020
2020, pp. 394â€“398, 2020.
 K. Kaur, S. Garg, G. Kaddoum, E. Bou-Harb, and
K.-K. R. Choo, â€œA bBig dData-eEnabled cConsolidated
fFramework for eEnergy eEfficient sSoftware dDefined dData
cCenters in IoT sSetups,â€ IEEE Transactions on Industrial
Informatics, vol. 16, no. 4, pp. 2687â€“2697, 2020.
Scientific Programming 9
 A. K. Tripathi, K. Sharma, M. Bala, A. Kumar, V. G. Menon,
and A. K. Bashir, â€œA parallel military dog based algorithm for
clustering big data in cognitive industrial internet of things,â€
IEEE Transactions on Industrial Informatics, p. 1, 2020.
 T. Wang, H. Ke, X. Zheng, K. Wang, A. K. Sangaiah, and
A. Liu, â€œBig data cleaning based on mobile edge computing in
industrial sensor-cloud,â€ IEEE Transactions on Industrial
Informatics, vol. 16, no. 2, pp. 1321â€“1329, 2020.
 X. Wang, L. T. Yang, L. Song, H. Wang, L. Ren, and J. Deen,
â€œA tensor-based multi-attributes visual feature recognition
method for industrial intelligence,â€ IEEE Transactions on
Industrial Informatics, p. 1, 2020.
 C. Yang, S. Lan, L. Wang, W. Shen, and G. G. Q. Huang, â€œBig
data driven edge-cloud collaboration architecture for cloud
manufacturing: a software defined perspective,â€ IEEE Access
vol. 8, pp. 45938â€“45950, 2020.
10 Scientific Programming
Copyright of Scientific Programming is the property of Hindawi Limited and its content may
not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
express written permission. However, users may print, download, or email articles for
Get Professional Assignment Help Cheaply
Are you busy and do not have time to handle your assignment? Are you scared that your paper will not make the grade? Do you have responsibilities that may hinder you from turning in your assignment on time? Are you tired and can barely handle your assignment? Are your grades inconsistent?
Whichever your reason is, it is valid! You can get professional academic help from our service at affordable rates. We have a team of professional academic writers who can handle all your assignments.
Why Choose Our Academic Writing Service?
- Plagiarism free papers
- Timely delivery
- Any deadline
- Skilled, Experienced Native English Writers
- Subject-relevant academic writer
- Adherence to paper instructions
- Ability to tackle bulk assignments
- Reasonable prices
- 24/7 Customer Support
- Get superb grades consistently
Online Academic Help With Different Subjects
Students barely have time to read. We got you! Have your literature essay or book review written without having the hassle of reading the book. You can get your literature paper custom-written for you by our literature specialists.
Do you struggle with finance? No need to torture yourself if finance is not your cup of tea. You can order your finance paper from our academic writing service and get 100% original work from competent finance experts.
While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.
Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.
In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.
Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.
We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!
We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.
Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.
What discipline/subjects do you deal in?
We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.
Are your writers competent enough to handle my paper?
Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.
What if I don’t like the paper?
There is a very low likelihood that you won’t like the paper.
- When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
- We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.
In the event that you don’t like your paper:
- The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
- We will have a different writer write the paper from scratch.
- Last resort, if the above does not work, we will refund your money.
Will the professor find out I didn’t write the paper myself?
Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.
What if the paper is plagiarized?
We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.
When will I get my paper?
You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.
Will anyone find out that I used your services?
We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.
How our Assignment Help Service Works
1. Place an order
You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.
2. Pay for the order
Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.
3. Track the progress
You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.
4. Download the paper
The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.
PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET A PERFECT SCORE!!!