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A cyber-physical system based collaborative distributed manufacturing system architecture for intelligent manufacturing

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Abstract

This paper systematically reviews the literature on cyber-physical systems (CPS) based on collaborative distributed manufacturing systems architecture for intelligent manufacturing. To this end, relevant journal articles were analyzed, examining literature about Internet of things, big data, distributed manufacturing systems, collaborative manufacturing, and intelligent manufacturing. Some important issues are identified, as well as gaps in the existing knowledge. Moreover, a CPS architecture in distributed manufacturing is proposed that acts as a platform for the collaborative environment to intelligently performing the tasks. The proposed architecture encapsulates different resources which can act as a guide for building a CPS framework for the equipment interconnection, to the data collection, processing, and the final knowledge procurement and learning. Furthermore, enabling technologies are discussed in the context of distributed manufacturing environments, or successful integration of different technologies to minimize the interoperability.
A cyber-physical system based collaborative distributed manufacturing
system architecture for intelligent manufacturing
Abhishek Thakur1, Nishant Chaudhary1, Piyush Tilokani1, V K Manupati1, Eric Costa2, M. L.
R. Varela, J. Machado
1School of Mechanical Sciences, VIT University, Vellore, Tamil Nadu, INDIA
2INESC TEC - INESC Technology and Science and FEUP - Faculty of Engineering, University of Porto, Porto,
Portugal
cDepartment of Production and Systems, School of Engineering, University of Minho
Abstract
This paper systematically reviews the cyber-physical systems (CPS) based on collaborative
distributed manufacturing systems architecture for intelligent manufacturing. This paper
systematically reviews the literature, examining the role of Internet of things, big data,
distributed manufacturing systems, collaborative manufacturing, and intelligent
manufacturing. To this end, 25 relevant journal articles were analyzed, with the identification
of some important issues, as well as gaps in the existing knowledge. Moreover, a CPS
architecture in distributed manufacturing is proposed that acts as a platform for the
collaborative environment to intelligently performing the tasks. The proposed architecture
encapsulates different resources which can act as a guide to building a CPS framework for the
equipment interconnection, to the data collection, processing and the final knowledge
procurement and learning. Furthermore, enabling technologies are discussed in the context of
distributed manufacturing environment for successful integration of different technologies to
minimize the interoperability.
Keywords: Distributed manufacturing; Cyber-Physical Systems; Internet of Things;
Interoperability.
1. Introduction
Amid the previous decade, the rapid development of Information and Communication
Technologies (ICT) has helped in the utilization of the Cyber-physical Systems (CPS) tools
such as cutting edge sensors, information procurement framework, wireless communication
devices, and appropriated processing arrangements (Monostori L., 2014). Coordinate with
such advancements leads to utilization of effective and efficient resources in the concerned
facility. CPS is an arrangement of teaming up computational elements which are in escalated
association with the encompassing physical world and its on-going procedures, giving and
utilizing, data-accessing and data-processing services available on the internet. CPS has
gotten continually developing considerations of scientists from the scholarly world, industry,
and government as of late. Recent years, a precursor generation of CPS can be found in
different application areas Viz. Aviation, automotive, civil framework, chemical processes,
medicinal services, transportation, and manufacturing (Lee J et al., 2015).
In this research paper, the mentioned CPS in the context of manufacturing scenario
has been taken into consideration for detailing about the processing of complex
manufacturing tasks. In particular, it is much more complex when we need to process the jobs
in a distributed manufacturing environment. The present industry is striving to move closer
towards the fourth generation industrial revolution where manufacturing with the intelligent
and collaborative platform is a must to keep up with the present competition and to produce a
better quality of goods at lower cost and in lesser time (J.C. Chaplin et al., 2015). It is clear
that information technology is known to be useful to reduce the risks in production and also
to forecast the market conditions for a better and optimized production (F. Khodadadiet al.,
2016). On the other hand, collaborative manufacturing, when blended with interconnected
network yields smart production and also associates several industries situated in
geographically distributed locations (D. Tchoffaaet al., 2016).
Despite the fact that, the solid approach of conventional assembling has its particular
leeway, it is not adequate in today's dynamic assembling condition. However, a few issues
have been identified with the traditional manufacturing approach and expressed in (Saygin
and Kilic, 1999). To overcome these issues, a few scientists have understood that there is a
need to integrate both the functionalities to accomplish better execution of the
framework.Also, because of fast advancement of information and communication, product
designers can rapidly circulate their resources at different spots. To accomplish the effective
information and knowledge trade between various offices, there is a requirement for internet
and communication technology through which it can be conceivable to connect every one of
them.
The remaining sections of this paper is organised as follows; In section 2 the literature
review of the identified major research papers is detailed. In section 3 the detail systematic
review methodology has been detailed. Section 4 the architecture with respect to CPS
architecture for intelligent manufacturing is proposed. Conclusions and future research
directions is drawn is section 5.
Literature Review
Lee et al. (2015) proposed 5C architecture where it completely coordinate cyber-physical
systems in the manufacturing industry to improve the existing patterns into an effective
design with the industrial big data analytics and CPS. Chaplin et al. (2015) presented
manufacturing theory and programming design for evolvable assembly systems, given the
standards of decentralization, setting mindfulness and intelligent resources in a data
distribution service. Khodadadi et al. (2016) proposed models, security and protection, and
system correspondence means and conventions. Here, they suggested the subtle elements of
future headings and open difficulties that face with the IoT improvement. Tchoffaa et al.
(2016) presented a method for joining model-based enterprise platform engineering, model-
driven design, and framework building to address the foundation of a sustainable
interoperability inside Distributed Manufacturing Networks. Tranfield et al. (2003) proposed
the degree to which the procedure of efficient audit can be connected to the management field
to deliver a dependable learning stock and improved practice by creating a context-sensitive
research. Colicchia et al. (2012) presented a literature review, examining the procedure of
learning creation, exchange, and improvement from a dynamic viewpoint inside the setting of
the supply chain risk management. Wong et al. (2012) presented systematic literature on
supply chain alignment so as to recognize and develop the alignments in supply chains.
Kamal et al. (2014) suggested concentrating on systematically analyzing and synthesizing
extant research published on supply chain integration (SCI) range and gives the significance
of SCI research zone. Raucha et al. (2015) presented the idea of distributed manufacturing
systems and networks to display a conceivable approach for more sustainable manufacturing
and supply chains. Ding and Jiang (2016) proposed the social sensors and the cyber-physical
system (CPS) nodes that consolidate them into the cyber-physical-social system (CPSS) stage
to encourage the customized creation of social manufacturing environment. Social sensors
manage the social information detecting in social associations among clients, ventures, and
different partners. Liu and Jiang (2016) proposed a design that gives the rule to build a CPS
framework from the equipment interconnection, data acquisition, processing, and
visualization, and the final knowledge acquisition and learning. Moreover, three key
empowering advancements are talked about, i.e., interconnection and interoperability among
various gadgets, industrial big data analysis for production process management and control,
and smart decision making given information procurement and learning approach. Lara and
Grossmann (2016) proposed a Generalized Disjunctive Program (GDP) that characterizes the
ideal 2-dimensional nonstop area and distribution of the potential offices given their most
extreme limit and the given directions of the providers and consumer markets. Gürdüra et al.
(2016) discussed a node-link diagram (NLD) representation system that can be utilized to
picture interoperability in CPS improvement apparatus chains. The design is to help the
apparatus chain engineers to survey the interoperability status that settles on choices on
integration situations accordingly. Tang et al. (2016) proposed a shrewd manufacturing plant
engineering given correspondence and registering layers that embed booking mechanisms
inside a mechanical shop floor. David and Vernadatb (2016) discussed a short history of
Enterprise Information Systems (EISs) and talks about different parts of EISs, including EIS
plan and designing, the effect of enterprise modeling, enterprise architecture, enterprise
integration and interoperability and undertaking organizing on EISs. Seitz and Leibniz (2015)
proposed the upsides of cyber-physical systems in perspective of generation arranging,
controlling and observing. In light of that, utilizing the idea of IFA's Learning Factory, it
depicts how these can be specifically utilized in applying logistic models to improve order
processing. Zhang and Fei (2017) presented the review of IoT-empowered manufacturing
systems (IoT-MS) to give a new paradigm by extending the systems of IoT to manufacturing
field. Furthermore, the general engineering, ongoing data sharing and integration display,
work rationale, and center segments are portrayed in subtle elements. Zach et al.(2016)
suggested a structure distinguish digital vulnerabilities in manufacturing systems frameworks.
The suggested approach employs the idea of intersection mapping to distinguish the digital
vulnerabilities in manufacturing. Seungmin Rhoa et al. (2016) suggested on innovations and
research patterns for cyber-physical systems advancements and application that provides
contributions while addressing the issues related theoretical and practical aspects of CPS
technologies and their applications. Radu and Remzi (2016) reviewed the present status of
virtualization of the cloud-based administrations, manufacturing and assembling systems for
the utilization of big data analytics for planning and controlling of the operations. Expanding
on already created cloud business solutions, cloud manufacturing is required to offer
enhanced manufacturing and business support. Göran et al. (2016) proposed the idea of
features based assembling for versatile equipment control in distributed and collaborative
CPS manufacturing environments. Also, a feature-based information system supporting the
coordinating of manufacturing assets and tasks with a cyber-physical robot application are
introduced. Hehenberger et al (2016) suggested in their paper to give a review of various
sorts of framework and the associated transition process from mechatronics to CPS and
cloud-based (IoT) systems. The primary drivers for the improvement and advancement of
Cyber-Physical Systems (CPS) are the decrease of advancement expenses and time alongside
the enhancement of the designed products.
3. Research Methodology
The aim of this paper is to make a systematic literature review (SLR) on the role of
information technology, collaborative manufacturing and distributed manufacturing in the
development of SMEs towards industry 4.0(Tranfield, Denver and Smart, 2013). By the
analysis and summarization of the research work done in this field, factors like how the
information technology plays a major role in collaboration and decision support systems are
also reviewed.
Fig.1. Main topics related with distributed manufacturing, intelligent manufacturing, collaborative
manufacturing and IoT.
Thus, the research questions for this SLR are:
RQ1: What is the role of information technology, IoT, collaborative manufacturing
and distributed manufacturing in the development of the SMEs towards Industry 4.0?
RQ2: What topics and issues related to information technology, collaborative
manufacturing, and distributed manufacturing are considered when SMEs apply the
concept of industry 4.0?
This paper will highlight several notions and ideas for further research work in distributed
manufacturing. Also, this will enhance research in computer sciences, artificial intelligence,
and mathematics by (i) making a study for these in the industry 4.0 perspective; (ii) finding
the less developed fields in the present research (iii) Highlighting topics for further study. As
an analysis, a flow chart was prepared, showing the basic framework of the project which is
also useful in the summarisation and further analysis of the research works published so
far[Wong et al.,2012]. A standard five-step methodology has been followed in this research
work which is reflected as follows:
1. Question formulation;
2. Locating studies;
3. Study selection and evaluation;
4. Analysis and synthesis;
5. Reporting and using the results.
In place of the traditional approach, a systematic literature review methodology has been
followed. This is mainly focussed on (i) redefining the bases for the current work; (ii)
integrating the SLR approach into the current body of knowledge; (iii) defining the basics;
(iv) Finding the appropriate research topics. Hence a systematic literature review has been
made on the role of information technology, artificial intelligence, computer sciences,
distributed manufacturing and IoT in the development of the SMEs to revolutionize their
processes to tend to industry 4.0.
The following steps were followed:
Step 1: How information technology, collaborative manufacturing, and distributive
manufacturing play a role in the effectiveness of the SMEs’ approach to industry 4.0? How
interconnectivity manufacturing hubs will help in improving overall productivity?
Step 2: Two bibliographic databases were used (Scopus and Web of Science) for this review
and different combinations of keyword strings were used to obtain a different number of
results. A majority of the published papers and highly relevant peer-reviewed journals in the
field of study were available in the above mentioned bibliographic databases (Kamal and
Iran, 2012).
Table 1. Search strings and number of results
search strings search field date of
search
number of
results
Web of Science
(“distributed manufacturing”
alternatively, “collaborative
manufacturing”
alternatively, “intelligent manufacturing”)
moreover, (“industry 4.0”)
Topic 28.02.16 4
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing”)
moreover, (“decision support system”)
Topic 28.02.16 7
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing”)
moreover, (“small and medium
sized enterprises”)
Topic 28.02.16 1
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing” )
moreover, (“IOT or “internet of things”)
Topic 28.02.16 8
Scopus
(“distributed manufacturing”
alternatively, “collaborative
manufacturing”
alternatively, “intelligent manufacturing”)
moreover, (“industry 4.0”)
article title, abstract,
keywords
28.02.16 25
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing”)
moreover, (“decision support system”)
article title, abstract,
keywords 28.02.16 76
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing”)
moreover, (“small and medium
sized enterprises”)
article title, abstract,
keywords
28.02.16 10
(“distributed manufacturing”
alternatively, “collaborative manufacturing.”
alternatively, “intelligent manufacturing” )
moreover, (“IOT” or “internet of things”)
article title, abstract,
keywords
28.02.16 47
Table 1 presents the keyword strings applied and the number of results obtained.
The initial combinations of keywords yielded 178 articles, i.e. 20 for Web of Science and 158
for Scopus. Mendeley software was used to remove duplicate papers on the relevant similar
topics as obtained on the two databases.
Step 3: In this, some filtration criteria were defined to get the most relevant studies to be
included in the work. Only time horizon of ten years was selected (2007-2017) because
industry 4.0 is the new concept that came into existence during this period (Colicchia and
Strozzi, 2012). Following are the most relevant subject areas that are considered while
selecting the peer-reviewed journals.
Web of Science:
a) Engineering Manufacturing;
b) Computer Science Interdisciplinary Applications;
c) Engineering multidisciplinary;
d) Computer Science artificial intelligence.
Scopus:
a) Engineering;
b) Computer Science;
c) Mathematics;
d) Material Science.
This filtration reduced the number of articles to be reviewed to 147 in Scopus and 14 in Web
of Science. Mendeley was used to remove duplicate papers, and remaining papers were for
our reference and analyses. This process was performed by reviewers to check for
concordance and agreement. Following is the criteria for eligibility of papers : (i) relevance
to industry 4.0 (ii) focus on the area of manufacturing studies (iii) qualitative and
quantitative nature of papers (iv) focus on the keywords: information technology, distributive
and collaborative manufacturing in the context of industry 4.0 with importance on decision-
support system and IOT(internet of things). The number of papers hence is reduced to 77.
Finally, a detailed analysis of the 77 articles was made, performing a full-text review. Articles
from 1995 to 2004 were first excluded since they were the only representative of a very small
percentage of the sample, and also to perform a more focused review and analysis, giving
priority to more recent studies and consequently reducing the time horizon from 20 to 10
years (2005–2014). Through the full-text review, some other articles were excluded as they
were not by the specific research focus of this study, this allows reducing the number of final
articles for analysis and synthesis to 38, as listed in Table 2.
Table 2. Summary of the systematic review articles selection and evaluation.
Database Search 1 Search 2 Search 3 Search 4 Search 5
Web Of Science 4 7 1 8 20
INCLUSION/EXCLUSION
CRITERIA
data range
(2007-2017)
4 6 1 8 19
document
type
(article)
4 6 1 8 19
subject area
(engineering manufacturing,
computer science
interdisciplinary applications,
engineering
multidisciplinary,
computer science
artificial intelligence)
2 6 1 5 14
language (English)
4 7 1 6 20
SCOPUS 25 76 10 47 158
INCLUSION/E
XCLUSION
CRITERIA
data range
(2007-2017)
25 76 7 47 155
document type ( article,
review, conference papers)
22 59 8 48 131
subject area (engineering,
computer science,
mathematics,
material science)
25 72 9 40 147
Language
23 75 9 40 147
The content was analyzed and summarized to a chronological development of the study fields
and the key issues and the possible topics for further research. The common ideas and the
possibility of errors in the overall papers were found to obtain a better quality data. The
content of 38 articles was analysed concerning given keywords. The data was hence
systematized and tabulated. This paper illustrates the formal presentation of the results to the
academic community (Step 5). The remaining content of the paper reports the findings of the
present study in a thematic way.
2. Cyber-physical system based distributed manufacturing environment
Distributed manufacturing is a type of decentralized manufacturing honed by ventures
utilizing a system of geologically scattered assembling offices that are coordinated using
information technology. Distributed Manufacturing System, is characterized as an
arrangement of self-governing specialists, which are commonly reliant on each other. Now a
days, the extent of DMS has evolved to enterprises who purposely supplement each other on
innovation and work force (Erwin Rauch et al., 2015).
We have shown the architecture for distributed manufacturing environment in Fig.2. We have
displayed the detailed description of each layer of architecture in Fig.3, Fig.4. and Fig.5.
Fig. 2. CPS architecture for intelligent manufacturing.
3.1 Physical connection layer
Sensors are the basic devices to measure physical environment in the vicinity. Appropriate
sensors can be used to detect temperature, vibration, pressure, mass flow rate, etc. [Banica
and Florinel-Gabriel, 2012]. So the foremost step of CPS implementation in distributed
manufacturing environment is to embed components like sensors, radio-frequency
identification devices (RFID) and measurement devices on the manufacturing resources and
to distribute them in the production environment (Kai Ding and Pingyu Jiang, 2016). All the
machines need to be connected to a local network such as local area networks (LANs) and
metropolitan area networks (MANs). Convention, processing, area, separation, and capacity
should be considered when the installed segment is picked. For instance, the uniform and
vigorous associations between heterogeneous physical elements (e.g., fabricating assets,
sensors, actuators, and estimation gadgets) ought to be characterized; appropriate sensors
(sort and particular) ought to be chosen and sent to legitimate areas with minimal effort and
high productivity on the premise of authentic machining assignments. To execute the
information learning framework given in Fig.3, numerous critical thinking systems in
manmade brainpower, for example, Case-based Reasoning (CBR) and Machine Learning
(ML) can be utilized. Taking care of an issue by CBR includes acquiring an issue depiction,
recovering comparative cases by contrasting estimation or post-handled information with
cases for the situation base, reusing the data in the recovered cases, overhauling the proposed
arrangement as indicated by particular conditions in target space, and holding another
experience to the case base (De Mantaras RL et al, 2005).
Fig. 3. A framework for knowledge acquisition and learning.
3.2 Middleware layer
In this research, a general middleware for implementing the CPS is developed as shown in
Fig. 4. Middleware frequently empowers interoperability between applications that keep
running on various working frameworks, by providing administrations so the application can
trade information in gauges based manner. Middleware is like the canter layer of a three-level
single framework engineering, except that it is extended over different frameworks or
applications (Chao Liu and Pingyu Jiang, 2016). Cases incorporate EAI programming,
telecommunications software, transaction monitors, and informing and queuing
programming. Subsequently, CPS middleware goes about as a bond among physical
connection layer, computation layer, and external applications. As per the above depictions,
the middleware must bolster the accompanying capacities: Device administration. Diverse
outside applications are probably going to utilize distinctive sensors/RFID gadgets/estimation
gadgets which have distinctive brands and sorts (Didem Gürdür et al., 2016). Besides, these
gadgets have their correspondence, protocols, and benchmarks. Accordingly, an open gadget
administration module is expected to drive these various gadgets to cooperate, and eventually
accomplish the objective of attachment and play. Interface definition. The information
interface gave a channel to CPS hub correspondences and required information/data to the
calculation layer and outer applications, concealing every one of the points of interest of
differing qualities, Information administration. The information gathered from sensors/RFID
gadgets/estimation gadgets can be creation state (e.g., temperature, humidity, and clamour),
machine working condition (e.g., power, speed, and vibration), work piece state and quality
information (e.g., area, size, harshness, and resilience), and so forth. The possibly vast
assortment of information sorts and arrangements requires a uniform information
configuration and information trade standard to oversee information in setting with process-
related data in the shop floor (Dunbing Tang et al., 2016).
Fig.4. Development of a
general middleware.
3.3 Computation layer
Much information,
continuous on the web or
historical offline, is assembled by different sensors/RFID gadgets/estimation gadgets, or
acquired from Enterprise Information Systems (EIS) for example, ERP, MES, and SCM as
shown in Fig.5. Particular models, calculations, and apparatuses must be utilized to separate
basic examples that give better understanding over working machine conditions, work piece
quality, producing forms, and so on (David Romero and François Vernadat, 2016). Consider
job shop planning, for instance; the dispatching principles are fused with the information
received from online estimation, information handling, or information combination, which
bodes well particularly when machines work in a mind-boggling generation condition and
experience an alternate crumbling rate(Kai-Frederic Seitza and Peter Nyhuisa, 2016). In this
layer, two types of enormous information registering that should be tended to be batch
computing and stream computing. Batch computing is utilized to process expansive volumes
of recorded information, and stream figuring is utilized to handle the information stream got
from sensors. After group registering or stream figuring, the outcomes are transmitted back to
the machine site for operation/prepare control and upkeep. So this layer goes about as
supervisory control to make machines or to assemble process self-versatile and self-ware
(Yingfeng Zhang and Fei Tao, 2017). Then again, generous learning about machine operation
conduct and generation prepare has been removed by information mining while executing
CPS in the shop floor. This layer assumes liability for coordinating the produced learning
with people's understanding, consequently making a brought together perspective of
information, data and learning to strengthen the basic leadership.
By applying an information securing and learning procedure to creation handle
administration, CPS will develop much clever as all the more assembling undertakings are
executed.
Fig 5.Services and instruments required to
implement CPS to shop floor.
5. Conclusion and future research direction.
In this paper, based on the findings from the
previous studies we analyze, synthesize and generate
a comprehensive systematic literature review (SLR)
on the role of cyber-physical system based
collaborative manufacturing with distributed manufacturing environment. The SLR
methodology turned out to be a valuable tool for detailed analysis of the literature with the
improvement including the synthesis of main findings of the literature, and to establish a
foundation for future research. Moreover, cyber-physical system based collaborative
manufacturing architecture for intelligent manufacturing is proposed. The architecture
consists of three key layers such as computational layer, application service layer and
optimization layer for the setup and operation of the CPS. In the computational layer,
interconnection and interoperability among different devices like sensors, radio-frequency
identification devices (RFID) and measurement devices are established. Whereas in the
application service layer the relationship among physical connection layer, computation layer,
and their external applications were established. In optimization layer, the supervisory control
to make machines or to assemble process, self-versatile and self-aware can be performed.
In the future work, one can use the proposed architecture to reduce their
interoperability issues in the real time environment and will be helpful for future
digitalization.
Acknowledgement
This project is funded by Department of Science and Technology (DST- SERB), India under
the grant Earlier career research.
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... Cyber-Physical Systems (SPSs) refer to physical systems with computational power, which are connected to web and may access specific services on the web (Thakur, Chaudhary, Tilokani, & Machado, 2017). CPSs are essential in the smart grid, especially the smart meters which manage the measurement of the energy consumption, transmit diagnosis to the service provider and receive signals from it (Zipperer et al., 1743). ...
Chapter
Energy demand has increased significantly in the recent years due to the emerging of new technologies and industries, in particular in the developing countries. This increase requires much more developed power grid system than the existing traditional ones. Smart grid (SG) offers a potential solution to this problem. Being one of the most needed and complex cyber-physical systems (CPS), SG has been addressed exhaustively by researchers, from different views and aspects. However, energy optimization yet needs much more studying and examination. Therefore, this chapter presents a comprehensive investigation and analysis of the state-of-the-art developments in SG as a CPS with emphasis on energy optimization techniques and challenges. It also surveys the main challenges facing the SG considering CPS factors and the remarkable accomplishments and techniques in addressing these challenges. In addition, the document contrasts between different techniques according to their efficiency, usage, and feasibility. Moreover, this work explores the most effective applications of the SG as a CPS.
... Cyber-Physical Systems (SPSs) refer to physical systems with computational power, which are connected to web and may access specific services on the web (Thakur, Chaudhary, Tilokani, & Machado, 2017). CPSs are essential in the smart grid, especially the smart meters which manage the measurement of the energy consumption, transmit diagnosis to the service provider and receive signals from it (Zipperer et al., 1743). ...
Chapter
Energy demand has increased significantly in the recent years due to the emerging of new technologies and industries, in particular in the developing countries. This increase requires much more developed power grid system than the existing traditional ones. Smart grid (SG) offers a potential solution to this problem. Being one of the most needed and complex cyber-physical systems (CPS), SG has been addressed exhaustively by researchers, from different views and aspects. However, energy optimization yet needs much more studying and examination. Therefore, this chapter presents a comprehensive investigation and analysis of the state-of-the-art developments in SG as a CPS with emphasis on energy optimization techniques and challenges. It also surveys the main challenges facing the SG considering CPS factors and the remarkable accomplishments and techniques in addressing these challenges. In addition, the document contrasts between different techniques according to their efficiency, usage, and feasibility. Moreover, this work explores the most effective applications of the SG as a CPS.
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