PreprintPDF Available

Transformation to Advanced Mechatronics Systems Within New Industrial Revolution: A Novel Framework in Automation of Everything (AoE)

Authors:
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

The recent advances in cyber-physical domains, cloud, cloudlet and edge platforms along with the evolving Artificial Intelligence (AI) techniques, big data analytics and cutting-edge wireless communication technologies within the Industry 4.0 (4IR) are urging mechatronics designers, practitioners and educators to further review the ways in which mechatronics systems are perceived, designed, manufactured and advanced. Within this scope, we introduce the service-oriented cyber-physical advanced mechatronics systems (AMSs) along with current and future challenges. The objective in AMSs is to create remarkable intelligent autonomous products by 1) forging effective sensing, self-learning, Wisdom as a Service (WaaS), Information as a Service (InaaS), precise decision making and actuation using effective location-independent monitoring, control and management techniques with products, and 2) maintaining a competitive edge through better product performances via immediate and continuous learning, while the products are being used by customers and are being produced in factories within the cycle of Automation of Everything (AoE). With the advanced wireless communication techniques and improved battery technologies, AMSs are capable of getting independent and working with other massive AMSs to construct robust, customisable, energy-efficient, autonomous, intelligent and immersive platforms. In this regard, rather than providing technological details, this paper implements philosophical insights into 1) how mechatronics systems are being transformed into AMSs, 2) how robust AMSs can be developed by both exploiting the wisdom created within cyber-physical smart domains in the edge and cloud platforms, and incorporating all the stakeholders with diverse objectives into all phases of the product life-cycle, and 3) what essential common features AMSs should acquire to increase the efficacy of products and prolong their product life. Against this background, an AMS development framework is proposed in order to contextualize all the necessary phases of AMS development and direct all stakeholders to rivet high quality products and services within AoE.
Content may be subject to copyright.
Received March 2, 2019, accepted March 24, 2019, date of publication March 27, 2019, date of current version April 11, 2019.
Digital Object Identifier 10.1109/ACCESS.2019.2907809
Transformation to Advanced Mechatronics
Systems Within New Industrial Revolution:
A Novel Framework in Automation
of Everything (AoE)
KAYA KURU 1AND HALIL YETGIN 2
1School of Engineering, University of Central Lancashire, Preston PR1 2HE, U.K.
2Department of Electrical and Electronics Engineering, Bitlis Eren University, 13000 Bitlis, Turkey
Corresponding author: Kaya Kuru (kkuru@uclan.ac.uk)
ABSTRACT The recent advances in cyber-physical domains, cloud, cloudlet, and edge platforms along with
the evolving Artificial Intelligence (AI) techniques, big data analytics, and cutting-edge wireless commu-
nication technologies within the Industry 4.0 (4IR) are urging mechatronics designers, practitioners, and
educators to further review the ways in which mechatronics systems are perceived, designed, manufactured,
and advanced. Within this scope, we introduce the service-oriented cyber-physical advanced mechatronics
systems (AMSs) along with current and future challenges. The objective in AMSs is to create remark-
ably intelligent autonomous products by 1) forging effective sensing, self-learning, Wisdom as a Service
(WaaS), Information as a Service (InaaS), precise decision making, and actuation using effective location-
independent monitoring, control and management techniques with products and 2) maintaining a competitive
edge through better product performances via immediate and continuous learning, while the products are
being used by customers and are being produced in factories within the cycle of Automation of Everything
(AoE). With the advanced wireless communication techniques and improved battery technologies, the AMSs
are capable of getting independent and working with other massive AMSs to construct robust, customizable,
energy-efficient, autonomous, intelligent, and immersive platforms. In this regard, rather than providing
technological details, this paper implements philosophical insights into 1) how mechatronics systems are
being transformed into AMSs; 2) how robust AMSs can be developed by both exploiting the wisdom created
within cyber-physical smart domains in the edge and cloud platforms and incorporating all the stakeholders
with diverse objectives into all phases of the product life-cycle; and 3) what essential common features AMSs
should acquire to increase the efficacy of products and prolong their product life. Against this background,
an AMS development framework is proposed in order to contextualize all the necessary phases of AMS
development and direct all stakeholders to rivet high-quality products and services within AoE.
INDEX TERMS Advanced mechatronics systems, Wisdom as a Service (WaaS), Information as a Service
(InaaS), Industry 4.0 (4IR), cyber-physical domains, cloud and edge/fog platforms, Automation of Every-
thing (AoE).
I. INTRODUCTION
Mechatronics can be defined as ‘‘the synergistic integra-
tion of mechanical engineering with electronics and elec-
trical systems using intelligent computer control in the
design and manufacture of industrial products, processes,
The associate editor coordinating the review of this manuscript and
approving it for publication was Tao Wang.
and operations [1]’’ to develop robust, cheaper, more reli-
able, more flexible and customizable products. Mechatronics
systems (MSs) has evolved drastically since its invention
by a Japanese engineer (T. Mori, Yasakawa Electric Co.)
in 1969 to indicate ‘‘mecha’’ from mechanical and ‘‘tron-
ics’’ from electronics, in particular, with the incorporation
of information technology (IT). Machine technology, such as
machine tools, power generators aims at relieving physical
VOLUME 7, 2019
2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
41395
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
stress and strain on human beings, whereas electronics tech-
nology along with IT aims at relieving mental strain of human
beings [2]. Machine technology along with electronics and
IT can substitute for human element in both physical and
mental effort within multidisciplinary engineering. In this
manner, the main driving force in MSs is to integrate tech-
nologies from traditional multi-disciplinary fields in engi-
neering and computer science to develop well-established
autonomous systems in new products. The integration of
mechanical systems and microelectronics opens new possi-
bilities for mechanical design and automatic functions along
with the knowledge-based systems with learning abilities [3].
Increasingly, microcontrollers are embedded in electrome-
chanical devices, creating much more flexibility and control
possibilities in system design [4].
Bradley et al. [5] analyzed the relationship between the
Internet of Things (IoT) and mechatronics, and concluded
that ‘‘the future, though challenging, is still seen as requir-
ing mechatronics thinking. We entirely agree with this
conclusion, yet with a different way of thinking. Mecha-
tronics thinking has led to cyber-physical systems (CPS)
and consequently smart domains which has changed our life
significantly in the cloud platform. In this study, we are
exploring Advanced Mechatronics Systems (AMSs) with
respect to interconnected CPS where AMSs are taking their
places in the market replacing the stand-alone MSs by
involving a wider scope of stakeholders. The aim in AMSs
development is to create high-quality intelligent autonomous
products and maintain a competitive edge through better
product performance by forging effective sensing, self learn-
ing, self-optimization, self-configuration, self-diagnosis and
precise autonomous decision making and actuation with no or
less human intervention using effective location-independent
monitoring, control and management applications with prod-
ucts. AMSs are exploiting the wisdom created by CPS in the
edge platform and primarily in the cloud platform along with
the abilities of these platforms. With the advanced wireless
communication techniques and improved battery technolo-
gies, AMSs are capable of getting independent and working
with other massive AMSs to construct robust, customizable,
energy-efficient, autonomous, intelligent and immersive plat-
forms. In this context, we focus on AMSs that integrate
various components to achieve more synergistic products
in the light of advanced electronics, computers, intelligent
software techniques, Industry 4.0 (4IR), cyber-physical smart
domains, cloud and edge computing. More explicitly, this
paper provides philosophical insights into AMSs regarding
their present, past and future aspects.
The way of doing business has drastically changed with
the smart applications/devices, where one is communicat-
ing to another within smart domains using the smart cloud
and edge platforms. Smart platforms provide Machine-to-
Machine (M2M) communication between smart objects with
distributed intelligence and decision making capacity through
integration of several technologies, such as sensors, actua-
tors, identification, tracking, and enhanced communication
protocols [6]. Millions of sensors and devices are
continuously producing data and exchanging important mes-
sages via complex networks supporting M2M communi-
cations and monitoring and controlling critical smartworld
infrastructures [7]. By 2022, Taylor [8] foresees that M2M
traffic flows are expected to constitute up to 45% of the whole
Internet traffic. We envision that smart domains and platforms
will be the fundamental building blocks for the creation
of AMSs and this M2M traffic rate will rise over 60% by
2025 with the new cutting edge communication technologies,
such as 5G. Ultra-low latency is identified as one of the major
requirements of 5G [9]. The importance of this research is
derived from the fact that we studied the ways through which
the cloud, fog/edge and AMSs concepts can be used in the
context of smarter life within the Automation of Everything
(AoE). Additionally, the life-time optimization, expressly,
continuous improvement of currently used products and the
products meeting the market is disclosed in this study, along
with location-independent control and monitoring abilities
in AoE using the wisdom created in both cloud and edge
platforms and the abilities of these platforms in order to
compose better products. To clarify the novelty of this paper,
the contributions are outlined as follows.
1) To the extent of our knowledge, this is the first
attempt that explicitly studies AMSs by forging the
features of the smart domains, platforms, communica-
tion technologies, 4IR and mechatronics in a new con-
cept - Automation of Everything (AoE), to enable the
implementation of next-generation automation systems
through exponentially revolutionizing industry.
2) For the first time, the essential features of an AMS
along with an explicit definition are revealed within a
framework proposed.
3) The ways of producing cheaper and more effective
AMSs with no or less number of sensors are proposed
using Wisdom as a Service (WaaS) and Information as
a Service (InaaS).
4) The ways of life-time optimization of products,
evolving products and technology generations within
reduced design and run-time changes, in particular,
in order to increase the customer satisfaction, are pro-
posed.
5) The ideal framework of present and future techno-
logical products is presented with respect to AMSs
considering the concept of the crucial features and
architectures of AMSs within AoE.
6) New insights and design guidelines are provided for the
companies that either use or produce IT and would like
to lead the market in the near future.
7) Future of AMSs are analyzed with respect to the emerg-
ing communication technologies, particularly promis-
ing wireless communication technologies.
The remainder of this paper is organized as follows.
Section Iprovides a comprehensive state-of-the-art literature
on the evolution of MSs. 4IR and its main components are
presented in Section II. The CPS and smart domains, cloud
41396 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 1. The structure overview of this paper.
and edge platforms are analyzed in Section III. Section IV
introduces the transformation to MSs whereas the AMSs con-
cept and main components along with transformation phases
are explored in Section V. The design and modelling of AMSs
is addressed in Section VI along with a proposed architectural
design (i.e., AoE) to shed light on how to develop AMSs in
the context of the main components explained throughout the
paper. The new cutting-edge communication technologies are
disclosed in Section VII regarding their likely propulsion to
the revolution of industry and AMSs. The essential features
of AMSs within a proposed framework and challenges are
revealed in Sections VIII and IX, respectively. Discussions
are provided in Section X. Finally, Section XI draws conclu-
sions and provides directions for potential future ideas. Read-
ers are referred to Fig. 1for an explicit structure overview of
the paper.
II. INDUSTRY 4.0 (4IR)
Automation in industrial plants accelerated after the inven-
tion of PLC (i.e., programmable logic controllers) by Gen-
eral Motors in 1968 [10] along with the other controllers,
such as PID (proportional-integral-derivative), SCADA
(supervisory control and data acquisition) and DCSs (i.e.,
distributed control systems) by supporting enterprise resource
planning (ERP) systems [11]. The industrial revolution after
4IR (i.e., the fourth industrial revolution) declared in Ger-
many within a government high tech-project in 2011 has
drastically changed the entire production cycle [12]. Com-
puters and automation has come together in an entirely new
way throughout smart automation, self-optimization, self-
configuration, self-diagnosis/prognosis with robotics con-
nected remotely to computer systems equipped with machine
learning (ML) and artificial intelligence (AI) algorithms that
can learn and control the robotics with very little input from
human operators [13].
With the rapid development of electric and electronic
technology, information technology and advanced manufac-
turing technology, the production mode of manufacturing
enterprises is being transferred from digital to intelligent
systems [14]. Within this industrial revolution, rather than
applying a rigid mass production system, production automa-
tion is relying more and more of customization of prod-
ucts with highly flexible (mass-) production which utilizes
relatively intelligent machines, that can adapt much more
rapidly to product changes and unforeseen process events
by maintaining a highly efficient production cycle [12]. 4IR
aims to dramatically enhance the productivity of manufactur-
ing technologies through the collection and analysis of real-
time data [15]. In this context, MSs and IoT devices have
evolved drastically in parallel with 4IR. The production of
components needed for any system can be rapidly developed,
which leads to efficient development of advanced products
in the market with the help of the Industrial IoT (IIoT) [16].
In this manner, the immense growth of IoT devices comes
right after this industrial revolution in 2011.
Innovative ideas have been concretized with the help of
rapid production of the desired customizable products within
this industrial revolution. Gordon Moore, co-founder of Intel,
states that the number of transistors per square inch on a
chip doubles every 18 months while the price is halved [17].
Consequently, the technological replacement period has been
considerably reduced owing to those similar improvements.
One improvement has led to another and vice-versa. IoT,
particularly IIoT has enabled 4IR as a new manufacturing
paradigm [18]. More than 3 million industrial robots will
be serving in factories across the world by 2020 [19]. 4IR
and similar initiatives are embracing the concept of ‘‘edge
in the factory’’ where the decision-making process moves
from cloud to edge; analytical algorithms can help realize
such concepts, since cloud based solutions are not optimal for
meeting the low latency demands of manufacturing processes
and devices, mainly because of the large volume of data pro-
duced at a rapid rate [20]. We envision that the wisdom-driven
manufacturing by exploiting the big data using advanced
AI analytics will lead to perpetual and exponential revolu-
tion of the industry, and consequently products and services
by incorporating contemporary communication technologies,
e.g. 5G and beyond, into the production will enable powerful
VOLUME 7, 2019 41397
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
interactions with the cloud platform. Having described the
range of new technologies that are fusing physical, digital
and biological worlds, 4IR revolution is about to alter the way
people live, work, and relate to one another [21].
III. CLOUD, CLOUDLET, EDGE/FOG, CPS AND WISDOM
A. CLOUD, CLOUDLET, EDGE/FOG AND CPS
The cloud platform has the advantages for massive storage,
heavy duty computation, global coordination and wide-area
connectivity, while the edge will be useful for real time
processing, rapid innovation, user-centric service and edge
resource pooling [29]. The cloud is approaching to the edge
as the massive network in a wider infrastructure along with
the deployment of multiple virtual machines (VMs) in numer-
ous cloudlets by leading providers [30], which facilitates the
reduced latency requirement of AMSs. Experimental results
demonstrate that cloudlets can decrease the response time
by 51% and diminish energy dissipation by up to 42% in
a mobile device compared to cloud offload [31]. The main
cloud platform providers, including IBM, Amazon EC2,
Alibaba, Microsoft Azure, Fiware and Google are serving an
open public network connecting businesses and individuals
all around the world under an umbrella with the Infrastructure
as a Service (IaaS), the Platform as a Service (PaaS) and the
Software as a Service (SaaS). However, the emerging IoT
introduces new challenges, such as stringent latency, capacity
constraints, resource-constrained devices, uninterrupted ser-
vices with intermittent connectivity and enhanced security,
which cannot be adequately addressed by the centralized
cloud computing architecture [29]. Mobile devices that are
connected to distant centralized cloud servers attempting to
acquire sophisticated applications impose additional load on
both Radio Access Networks (RANs) and backhaul networks,
which results in high latency [32]. The edge (or fog) com-
puting is an emergent architecture for computing, storage,
control, and networking that distributes these services closer
to end users [29] to enable a more independent processing
and organization. Both edge and fog computing attempts
to achieve the same goal, but through different approaches:
the fog computing moves intelligence to the IoT gateways
or to the local area network, whereas the edge computing
pushes intelligence to the edge devices - at the bottom of the
architecture hierarchy [33]. The segmentation of what tasks
go to the fog/edge and what tasks go to the back-end cloud
is application specific and can change dynamically based
upon the state of the network, including processor loads,
link bandwidths, storage capacities, faulty events, security
threats [34] and per user requirements.
The term mobile-edge computing (MEC) is also often
used to indicate the edge platform that is an instance of
edge computing where the objective is to provide cloud-
computing capabilities at the edge of the cellular network
using the radio-access network along with other wireless
technologies [35], which integrates the MEC servers and
cloudlet infrastructures [36]. There are several platforms
provided by the leading companies to be employed as an
immediate edge computing platform and gateway (GW) for
specific types of smart domains, such as Apple Homekit,
CISCO Fog Computing IOx, Google NEST, IBM Node-RED
Bluemix, Intel IoT platform, Microsoft nitrogen.io. A laptop,
a smartphone or a single board computing device (SBCD),
such as Raspberry PI can serve as an edge platform. Noting
that today several studies have been focusing on making
these edge platforms more intelligent and stronger, as dis-
cussed in [20], [31] and [37] on which we will be focusing
in this paper, while conceptualizing the guidelines in AMSs
development. The devices used in these smart cyber-physical
platforms and domains are termed as IoT, which is firstly
proposed by MIT (Massachusetts Institute of Technology)
in 1999 [38]. International Telecommunication Union (ITU)
suggested the term ‘‘Internet of Things’’ for official and com-
mon use among researchers, industries and end-users. IoT
is internet-oriented (middleware), things oriented (sensors,
actuators) and semantic-oriented (knowledge). Noting that
‘‘Internet of Everything’’ (IoE) and ‘‘Internet of Anything’
(IoA) are also being used instead of IoT.1
One of the first instantiations of a ‘‘thing’’ connected to the
Internet was a modified soda machine at the Carnegie Mellon
University in 1982, which could report its beverage inven-
tory as well as the temperature of the beverages stored [17].
It’s predicted that by the year 2020 more than 50 billion
devices would be connected around the world [40], [41].
The number of connected things will exceed 7 trillion by
2025 which makes 1000 devices per person and an estimated
value of 36 trillion of dollars [24]. We can safely conclude
that the way of doing business regarding CPS stimulates a
new industrial revolution despite many challenges, where the
most critical ones are discussed in Section IX.
Amongst all 4IR technologies, IoT is set to have the
most impact on industries in the future, as most devices
will soon be able to collect or process data [21]. IoT with
resource constraint characteristics is composed of physi-
cal objects embedded with electronics, software, and sen-
sors, which allows objects to be sensed and controlled
remotely across the existing network infrastructure, facilitates
direct integration between the physical world and computer
communication networks, and significantly contributes to
enhanced efficiency, accuracy, and economic benefits [42].
These objects use new idiosyncratic protocols, such as the
message queuing telemetry transport (MQTT) to overcome
the compatibility issues of high latency networks with limited
bandwidth often used for M2M communications [17] and
other resource constraint issues. The IoT term is used as
an umbrella keyword for covering various aspects related to
the extension of the Internet and the Web into the physical
realm, by means of the widespread deployment of spatially
distributed devices with embedded identification, sensing
and/or actuation capabilities [43]. IoT envisions a future in
which digital and physical entities can be linked, by means
of appropriate information and communication technologies
1Readers can find the evolution of the IoT technologies in [39] and [27].
41398 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
such as Radio Frequency Identifier (RFID), Near Field
Communication (NFC), Wifi or Bluetooth [17], to enable
a whole new class of applications and services [43]. IoT
devices create a voluminous amount of data, continuously,
but can cope with only a limited number of computational
queries [44].2
The idea of smart objects are the building blocks of IoT
incorporating intelligence into everyday objects by which
everyday objects turn into smart objects [24] with embedded
electronics using ubiquitous services [46], [27], CPS [47]
and Wireless Sensor Networks (WSN) [48], [39], [22], M2M
communications [49] and cloud and edge platforms to
1) sense and collect information from the environment,
2) communicate, interact and control the physical world
to be interconnected to each other to exchange data and
information [24], and 3) finally automate and facilitate our
daily-life using multi-functional hybrid products.
The GW architecture supports edge computing and GWs
are mainly used to manage the traffic between the edge and
the cloud platforms, as desired. The GW structure aims to
process data at the edge computing platform instead of doing
everything in the cloud platform [50]. In addition, the role of
the GW is to allow Internet access for end devices that are not
able to implement the full Internet protocol stack [50]. More-
over, virtualization feature of GW assists in making software
updates, handling functionalities and managing features, for
instance, supporting a new protocol on the fly [50].
B. BIG DATA AND WISDOM
Recent advances in cyber-physical domains and platforms,
big data and data analytics have enabled the development of
an open architecture with intelligent sharing and services [51]
along with the Web of Things (WoTs) that facilitates ser-
vices offered at Open Systems Interconnection model (OSI)’s
application layer [52]. One of the recent prominent trends
is to integrate all smart domains in a combined architecture
of the cloud platform [6] to create bigger synergies even
though it involves many challenges, some of which are dis-
cussed in Section IX. With an increased digital consumption
the world is creating massive amounts of data on a daily
basis. IDC (a technology research firm) estimates that data
have been constantly growing twice as much for every other
year [51]. According to Domo’s Data Never Sleeps 6.0 report,
by 2020 there are 2.5 quintillion bytes (1 million terabytes)
of data created each day and it is estimated that 1.7MB of
data will be created each second by every single person on
earth [53].
Cloud platforms accommodate a large volume of big data
in high dimensions that is generated exponentially in dif-
ferent formats and from different sources and likely input
for all other smart systems as a wisdom using the intelli-
gent big data analytics. A revolutionary networking model
called Information Centric Networking (ICN) has recently
2Readers can find more information about the interplay between the IoT,
cloud and edge/fog in [29], [45]
FIGURE 2. Interaction of smart domains and formation of WaaS and
InaaS.
attracted the attention of the research community working on
data dissemination, mainly wisdom sharing among various
smart domains [54]. Some of the main smart domains are
smart home, smart building, smart city, smart transportation,
smart health, smart shopping, smart industry, smart factory
and manufacturing, smart logistics and retail, smart agricul-
ture, smart energy and smart grid, as recorded in Table 1
along with their applications. The connected devices in these
smart domains not only talk to each other within their
domains, but also they interact with various other devices
from diverse smart domains. For example, security, fire or
gas alarm using intelligent sensors in smart home domain
may trigger an action for the police or fire department in
smart city domain. More explicitly, there is no strict bound-
aries between these smart domains; an output of the smart
devices may become the input for other smart devices within
their domains and for other domains, as illustrated in Fig 2,
in which the smart city is located in the center for signifying
people-focused cyber-physical insights, since more than 60%
of the population will be living in urban environment by
2030 [25]. Cities with heavy populations escalate burden on
energy, water, buildings, public places, transportation and
many other things [25]. The proliferation of devices with
communicating-actuating capabilities is bringing closer the
vision of an IoT, where the sensing and actuation functions
seamlessly blend into these devices. More functional and
novel devices are made possible through the access of rich
new information sources [27]. The privacy and protection
of individuals are vitally important due to huge volume of
data recorded from the actions/activities of these individuals.
Zhang et al. demonstrated that 20% of the big image data was
found sensitive and maintained in the edge platform whereas
80% of was found insensitive and encrypted, then subsampled
VOLUME 7, 2019 41399
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
TABLE 1. Smart domains and their applications.
41400 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
and stored in the cloud platform [55]. Research estimates
that 90% of the data generated by the endpoints will be in
stored and processed locally rather than processing in the
cloud [56]. The sanitization process of the big data should be
performed meticulously before sharing in order to preserve
the security and privacy of individuals. More and more sen-
sors and devices are being interconnected via IoT techniques,
and these devices generate massive amount of data, which
demand further processing and provide intelligence to both
service providers and users [7]. Therefore, explosive growth
of data from terabytes to petabytes has triggered interest in
a new interdisciplinary form of scientific inquiry referred
to as ‘‘data science’’ and ‘‘data analytics’’ [17]. With the
help of the competent and scalable data analytics, the busi-
ness processes can be accelerated and the overall decision
making process may be substantially improved. The busi-
ness intelligence landscape is currently dominated by the big
data and all the business models are rapidly adopting the
big data analytics solutions, and consequently, exponentially
increasing the big data created in the cloud and edge plat-
forms as a Data-as-a-Service (DaaS) can be transformed into
Information-as-a-Service (InaaS); InaaS can be transformed
into Knowledge-as-a-Service (KaaS) using Big Data analyt-
ics, and finally KaaS can be transformed into Wisdom-as-a-
Service (WaaS), where Wisdom is an insight to know what’s
true or right for making correct judgements and decisions,
and taking actions, accordingly [51].3These transformations
can be readily observed from Fig. 8. The WaaS standards and
service platforms are expected to be fine-tuned continuously
as a core infrastructure for intelligence industry and smart
city to support the development of various intelligent IT
applications and it is anticipated that this will bring a huge
economic value for intelligence IT industry by realizing the
pay-as-you-go manner [57].
IV. TRANSFORMATION TO MECHATRONICS SYSTEMS
FROM MECHANICAL SYSTEMS
Mechatronics does not indicate any new revolutionary
technology [4], [58]. It is a philosophical approach in which
mechanics, electronics and IT are forged in a synergistic
way to produce enhanced products and systems. This process
was eased using semiconductors. Semiconductor technology
within conductivity modification using modified doped sili-
con with electrically neutral n-type silicon and p-type silicon
has changed the understanding of MSs drastically regarding
digital electronics, digital memory, amplification and switch-
ing. A semiconductor is able to transfer the current to trigger
desired activation easily as a transistor in order to improve the
conductivity performance by simply applying small positive
voltage to the gate that is insulated from the semiconductor
by an oxide layer and connects two n-types modified silicon
through a p-type modified silicon (i.e., NPN) as forming a
conducting channel rather than using a moving mechanical
3Readers can find monitoring and control, the big data and business
analytics as well as information sharing and collaboration issues regarding
cloud environment in [39].
switch as shown in Fig 5, which does not only makes the
response time quicker, but also makes the development of
mechanisms simpler in a way of transforming mechanic func-
tions into electronics and software. Furthermore, semicon-
ductor technology made it easier to embed billions of micro-
transistors or electronic mechanisms into a processor. In this
sense, transistor has truly revolutionized human existence by
impacting practically everything in our everyday lives [4].
Intel’s Stratix 10 contains over 30 billion transistors.4A typ-
ical car has more than 100 embedded processors for engine
control, emissions control, sound system etc.
More elements of MSs, such as microprocessors and
microcomputers with faster speeds, larger memories, and
more functionalities are being released by the help of the
advanced transistors. Integrated sensors and microcomputers
with less cost and space, higher precision, and fewer distur-
bances through digital transmission lead to smart sensors [3].
Integrated actuators and microcomputers develop to smart
actuators to carry out the desired outputs, such as position,
angle, force or torque [3]. Semiconductor sensors and actu-
ators called microelectromechanical (MEM) devices have
been changing the mechatronics systems drastically in 21st
century by merging silicon based microelectronics with
micromachining technology [4]. Miniaturization of compo-
nents and consequently devices using MEM technology is
imperative for ergonomic and functional use. This technology
is being effectively used in almost every field such as auto-
motive, electronics, medicine, communications and defense,
(e.g., airbag, intelligent tyres, disk drive heads).
Analysis of the characteristics and behavioral patterns of
biological systems has been an active study discipline since
the creation of human kind in order to find solutions for com-
plex problems that cannot be readily explained. This analysis
using analogies has largely been applied to real world prob-
lems. More explicitly, biological systems have evolved to find
just-good enough solutions to survive in complex, dynam-
ically changing, and uncertain environments [59]. Under-
standing and adapting the underlying principles of these solu-
tions to engineering systems have the promise of enabling
many new MSs that can operate in unstructured and uncertain
environments robustly and efficiently. Nowadays, human-
learning-inspired systems are able to learn and function suc-
cessfully by exploring, observing, watching and exploiting
with reinforcement learning without prior knowledge, in par-
ticular in the hybrid combination of reinforcement learning
and deep-learning with limited prior knowledge using effec-
tive behavioral mappings, such as Markov Decision Pro-
cess (MDP) by maximizing the reward. Biologically inspired
(bio-inspired) design is not about blindly copying biological
systems, but more on understanding the physical principles
of their operation and adapting such principles to engineering
systems with the available synthetic materials, manufacturing
methods, computation, and power sources [59].
4https://www.intel.co.uk/content/www/uk/en/products/programmable/
fpga/stratix-10.html
VOLUME 7, 2019 41401
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 3. The basic elements of mechatronics systems [58]. The
functions of mechatronics systems are composed of sensing, processing
and actuating.
FIGURE 4. The intricate components of mechatronics systems [4].
Bolton et al. [58] outlined the basic components of a MS as
illustrated in Fig. 3. These components are detailed by Alci-
atore and Histand [4] as described in Fig. 4. Engineering of
MSs and products with advanced control systems is currently
available in a substantial number of industrial smart products
in many domains. All of these powerful and compelling sys-
tems are controlled by microcontrollers, on which intelligent
software running along with external devices, such as sensors
and actuators that might interface to the microcontrollers.5
The philosophical insights of mechatronics have paved the
way to the development of IoT devices.
V. TRANSFORMATION TO ADVANCED MECHATRONICS
SYSTEMS FROM MECHATRONICS SYSTEMS
Current trends are placing new requirements on the
technology used to support current product and service
production [11]. The underlying precepts of the transfer of
functionality from the mechanical domain to the electron-
ics and information domains have been maintained with
the mechatronics understanding [5], which made significant
changes to the way of thinking in mechatronics. The sole
understanding of MSs is not sufficient to address the rapid
changing dynamics in the industry and the market. With
the further development of technology and in response to
increased customer diversity, and service and product qual-
ity requirements, the meaning of mechatronics has been
5Readers can access further information about the main components of
MSs in [4] and [58].
FIGURE 5. NPN and PNP types of bipolar junction transistors.
broadened further to include intelligent information and
communication technologies, such as knowledge-base, big
data analytics, Data Mining (DM), AI, ML, Deep Learn-
ing (DL), WSN, cloud and edge platforms, cyber-physical
domains, more importantly synergistic interconnection of
various MSs/IoTs. The leading companies, such as Google
($3.9 billion), Amazon ($871 million), Apple ($786 million),
Intel ($776 million) and Microsoft ($690 million) have been
immensely investing on AI technologies since 2006 [60].
This indicates that we are moving into a new phase in which
the way of doing business is significantly changing with
mainly advancing AI, communication technologies and inter-
twined smart domains, and the components given in Figs. 3
and 4, which also discuss that MSs are not sufficient to estab-
lish desired AMSs in order to meet the needs of the stakehold-
ers, in particular customers and companies. In this context,
other new components are required to be incorporated into
these elements to develop AMSs by which the new future will
be shaped, enabling better ways of interacting, working and
living. The main components of AMSs and their interrelation
are illustrated in Fig. 6, in which the components incorpo-
rated are highlighted. AMSs, coping with rapidly changing
dynamics, are replacing MSs with a new form with the aid of
the cloud and edge platforms, CPS, IoE, 4IR, cutting-edge AI
and communication technologies.
In the coming years, IoT is expected to bridge diverse
technologies to enable new applications by connecting
physical objects together in support of intelligent decision
making [45]. Furthermore, the evolution of the industrial
IoT and 4IR creates the possibility of connecting computer
automated control systems for remote monitoring and rapid
response to events requiring real-time handling [61]. M2M or
Device to Device (D2D) along with Peer to Machine (P2M)
and Peer to Peer (P2P), talking mechatronics devices within
sharing knowledge, more importantly using service-oriented
WaaS through the cloud platform within smart domains are
replacing stand-alone mechatronics devices rapidly in order
to enable enhanced responses to time-critical actuation and
context-dependent data needed for AMSs to function in a
more desired way. WaaS can make correct judgements, deci-
sions, and actions to provide the right service for the right
AMSs at the right time with the right content.
Bradley et al. [5] explores the relationship between IoT
and mechatronics: IoT is forcing disciplines to further review
the ways in which mechatronics systems and components
are perceived, designed and manufactured. In this regard,
41402 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 6. AMS framework and the main components of an AMS and their interaction with the environment and other AMSs.
for the future of mechatronics, recent years have seen a
shift from systems based on the interconnection of physical
components, in which transmitted data have been used to
facilitate control of systems. Information is at the heart of
these systems and serviced by smart objects [5] in a broader
aspect covering several advanced systems at a time in a
more complex synergistic understanding using the wisdom -
i.e., WaaS, and InaaS created in the cloud and edge platforms
in a pay-as-you-go model.
On one hand, the vastly and rapidly growing number of
connected things is creating data at an exponential rate [56].
Contrarily, many resource-constrained devices will not be
able to rely solely on their own limited resources to ful-
fil all their computing needs. The majority of AMSs may
not have sufficient resources to process the big data with
respect to advanced processing and monitoring tools that are
already in the cloud platform, such as Amazon Web Ser-
vices (AWS) IoT, Bluemix, and Microsoft Azure IoT Suite.
The data collected by the sensors are in raw form and in
large volume; these data need to be stored, processed, and
analyzed to extract interpretable information from it, which
is accomplished by storage and data analytical tools provided
by the cloud platform [62], where near to real-time operations
can be performed. However, these are usually delay-tolerant
applications. The outcome derived from raw data collected by
the sensors can be used by any application [62] as a service,
such as InaaS and WaaS. The cloud platform derives manifold
advantages to store and process enormous data generated
by heterogeneous devices, in particular, these devices have
limited computation and storage capacity [6].
On the other hand, the cloud platform may not be suit-
able for scenarios involving real-time operations, low latency
requirements, and high quality of service (QoS). Therefore,
extending cloud computing and services to the edge platform
where proximity to consumers supports for quick processing
at the edge fulfilling low latency requirements is imperative,
particularly for customer-centric applications, such as medi-
cal devices, connected cars, industrial robotics and vehicles in
aviation. Against this background, a mobile smart phone with
improved computing abilities may serve as an edge platform
to provide local control and analytics applications to AMSs.
The direct interaction of these devices with the cloud will
be unrealistic and cost prohibitive, since such interactions
often require resource-intensive processing and sophisticated
protocols [29]. Sending all the data to the cloud will require
prohibitively high network bandwidth, which is mostly infea-
sible with respect to network bandwidth constraints [29].
For instance, an autonomous vehicle can generate big data,
which are estimated to be about one gigabyte per second [63].
Aircraft engines, such as in Boeing 787 generates about 1 Ter-
abyte of sensor data every 24 hours and Industrial machines
and their parts, such as gear system generates huge amount
VOLUME 7, 2019 41403
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
of vibration data every second [64]. Thus, sending all the
data to the cloud will require prohibitively high network
bandwidth [29]. Some functions are naturally more advan-
tageous to be carried out in the edge while others in the
cloud. Determining which functions should be carried out
in the edge platform and how the edge should interact with
the cloud will be the key aspects of the edge research and
development [29]. More explicitly, the data required to be
sent to the cloud platform, the data maintained in the edge
platform and the data deleted to spare enough space for
further processes should be determined during the develop-
ment phase of AMSs based on the constraints, abilities and
requirements using data-intensive applications and big data
analytics for data processing under the supervision of data
scientists.
An important feature of an intelligent system is the auto-
matic supervision and fault diagnosis of its components [3]
or one mechatronics system may be monitoring the health
of other mechatronics systems. Advanced intelligent diag-
nosis/prognosis applications without human intervention are
required in order not to face any malfunction, such as software
failure in AMSs. Prognosis of pending faults are supposed
to be carried out to be proactive using the circumstantial
evidences and symptoms, such as noise, vibration, smell etc.
Systematic error (i.e., inaccuracy), random error (i.e., impre-
cision) and blunders (engineering errors) should be mini-
mized through effective self calibration abilities and care-
ful design, and furthermore, approaching failures and sys-
tem inconsistencies should be foresighted through effective
and autonomous intelligent prognosis techniques to establish
precise and consistent-robust AMS. Required maintenance
processes should be triggered autonomously via the edge
or cloud platform where the edge platform is not close to
the people who may be concerned using remote abilities of
the smart cloud platform based on the non-stop 24/7 service
targeted for AMSs.
More sensors mean more information for better decision
making but introduce more complexity and expense to the
systems. Within the cloud, it is possible for AMSs to function
with no or less number of sensors resulting in less com-
plex AMSs. Information or wisdom already created in the
cloud can be incorporated into AMSs as InaaS or WaaS.
For instance, weather conditions, such as temperature, wind
speed and humidity are already in the smart city domain
and one can readily incorporate these parameters into AMSs
rather than integrating additional sensors into the existing
AMSs to measure these parameters separately. Similarly,
smart vehicle systems should be using the information created
by the sensors in smart transportation domain.
WSNs are required as AMSs get independent to function
efficiently anywhere, anytime. In this manner, establishment
of energy efficient AMS is an urgent need. Wireless sen-
sor devices- i.e., wide range of intelligent and tiny wireless
sensing devices, are resource constrained and operate on bat-
teries, the communication overhead and power consumption
are therefore important issues for WSNs design [22]. It is
essential to design reliable and efficient communication pro-
tocols to remotely manage sensor devices without consuming
significant resources [22] and they are opt to switch to sleep
mode during their nonactivity period and wake up when
required [62]. Furthermore, by transferring computation and
communication overhead from nodes with limited battery
supply to nodes with significant power resources, the system
can extend the lifetime of the individual nodes [7].6The sleep
interval of sensors can be predicted and controlled depend-
ing upon their prior activities, data acquired and remaining
battery level in which the sensors undergo automatic con-
figuration, optimization, and healing mechanisms to save
energy [62]. The modelling of AMSs for the sake of the
functioning independently anywhere anytime is explored in
Section VI along with an explicit architectural framework and
various examples.
The low-cost SBCDs, such as Raspberry Pi, Arduino Uno,
Intel Galileo, Intel Edison, Beagleboard, Libelium Waspmote
in a light-weight, easy to use, customizable and highly-
interoperable way are paving the way towards the devel-
opment of AMSs. Their success lies in their small design
and built-in general purpose input output (GPIO) pins, wire-
less components, such as built-in Wi-fi, Bluetooth and many
other promising abilities. Moreover, many other devices, such
as sensors, actuators, extension units compatible with those
SBCDs, such as Google’s Rainbow HAT are progressively
attaining their places in the market, which contributes to the
development of AMSs. We envision that those SBCDs with
promising abilities will create an immensely growing market
for building AMSs.
VI. MOLDING OF ADVANCED MECHATRONICS SYSTEMS
WITHIN AUTOMATION OF EVERYTHING (AOE)
An explicit architectural scheme of AMSs that aims to
establish a heterogeneous execution environment using a
homogeneous computing model is presented in Fig. 7. Main
components of this design are the platform of automated
AMSs (poaAMSs) along with the intelligent edge platform,
customers, vendors, the cloud platform and production plant,
which support QoS in many aspects. AMSs can be mobile
or location-dependent systems in this architecture which is a
promising enabler of many real-life applications and systems.
AMSs in a dedicated location or on mobile are automated
to work harmoniously forming the basis of an optimal envi-
ronment that reacts to our needs and moods. They are able
to communicate with each other directly to use any resource
and/or information in virtual manner or to trigger any desired
actuation in other AMSs. WaaSs and InaaSs along with the
capabilities of the cloud and edge platforms open up possi-
bilities for dynamic learning through autonomous feedback,
feedforward, and cross-linking between all stakeholders dur-
ing the product life-cycle. AMSs are designed to utilize
available WaaSs and InaaSs in the first instance rather than
embracing more sensors. The main reason for connecting to
6Readers can find the detailed analysis of the efficient use of WSN in [65].
41404 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 7. Framework of AoE: Service-oriented architecture of poaAMSs with the internal and external components and main
functions, which we name it as Automation of Everything (AoE).
VOLUME 7, 2019 41405
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 8. Framework of big data transformation between the main
poaAMSs and other poaAMSs, and the cloud platform.
the edge platform from the cloud platform is to benefit from
these services while maintaining the location-independent
monitoring and controlling abilities. The required WaaS or
InaaS may either be in the cloud or in the edge platform.
More explicitly, WaaSs and InaaSs in the edge platform may
be created by poaAMSs as depicted in Figure 8or directly
obtained from the cloud platform. The vendors have to buy
these services in a pay-as-you-go model without on-demand
access for minimizing latency if they are in the cloud platform
and these services are opt to update the edge platform in inter-
mittent manner for the most recent information or wisdom
via GWs using resource scheduling functions. These services
are ready-to-use if they are in the edge platform as illustrated
in Figure 8. Strictly speaking, AMSs acquire these most
recent updated services in the edge platform without demand-
ing from the cloud, which enables real-time deadline require-
ments. A WaaS may be sufficient for an AMS to determine
how to actuate, or it can be fused with other WaaSs, InaaSs
and information gathering from local sensors on the AMS for
real-time data collection and control purposes. Additionally,
the vendors can create multi-purpose WaaSs unique to their
products in the cloud platform, if there is no available WaaS.
One way of doing this is to generate WaaSs by poaAMSs
that are designed to use all sensors for their distinguishing
purposes. These services can also be integrated into other
AMSs that are designed to leverage these particular WaaSs in
the cloud platform rather than to directly use sensors. Noting
that using WaaSs is crucial, particularly for application sce-
narios requiring mass amount of information detection from
numerous sources, which can be highly complex, costly and
impractical.
Communication of users and vendors with poaAMSs in
the cloud platform is carried out using different VMs to
effectively manage data traffic, authentication, privacy and
security. VM interaction is provided to the cloud customers
where the cloud is closest to the users, which is commonly
called as cloudlet [66] in order to provide the best possible
latency requirements. Several poaAMSs can be clustered to
form an integrated ecosystem of poaAMSs if they are geo-
graphically available to be merged without the need for cloud
network resources, as illustrated in Fig. 9. Moreover, a cluster
of poaAMSs can also be established using various cloudlets
and their virtualization and data replication abilities without
generating additional data traffic for the main cloud center
in order to build bigger automated platforms with respect to
the abilities of the cloud platform. For instance poaAMSs-
4 in Fig. 9can jointly work with poaAMSs-1, poaAMSs-
2 and poaAMSs-3 using two cloudlets. It has been discovered
that service providers could analyze sensitive information
such as user activities, even with encrypted devices [67].
Private information of users must never be sent to the VMs,
which are assigned for other particular tasks such as the
VMs reserved for vendors to remotely monitor and to access
real-time health information of AMSs. Sensitive data should
only be stored on authorized VMs to ensure anticipated
security and privacy. Furthermore, sensitive data that are
forwarded to the cloud should be encrypted to meet security
and privacy requirements. This would prevent third party to
potentially sniff on the network for the packets of crucial
data, which might compromise the security and privacy. The
customer uses VM1whereas the vendor uses VM2in our
design in Fig. 7. A VM instance is created for each AMS or
each edge platform, and vendors can use different VMs while
several vendors can contribute to poaAMSs. The GW is the
interface between poaAMSs and cloud platforms to manage
the necessary authentication, and to protect poaAMSs from
malicious eaves-droppers and network attacks. Additionally,
in a binary decision between edge and cloud with respect to
the proximity to the poaAMSs, the customer can establish a
communication with the resources directly where s/he is in
the dedicated location of poaAMSs.
Users are authorized to perform specific operations
whereas vendors are authorized for other specific opera-
tions. The main authorized jobs unique to the customer and
vendors are depicted in Fig. 7. Automatic notifications and
other required information for customers are sent to VM1,
where the costumer can monitor and control the services
of poaAMSs via this particular VM. One can accomplish
these tasks while being away from poaAMSs, since the
cloud allows efficient real-time data collection and analysis
by offering a comprehensive selection of resources, remote
data management, easy access, and economic benefits [68].
However, attaining low latency is particularly difficult when
41406 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 9. Communication between the cloud platform and the poaAMSs.
cloud services are involved, as the cloud could be far away
from devices [20], [30] even though the cloud platform is
closer to users with the extension of the cloud center using
cloudlets and virtualization of resources.
AMSs are connected to each other in a distributed com-
puting environment, such as M2M to perform their tasks in
smarter ways mainly using the edge platform, particularly for
satisfying very low latency requirements, where a specific
output of one AMS can be relayed as an input to other
AMSs in order to commence a desired actuation. Addition-
ally, AMSs autonomously learn about the individual behav-
ior, and adjust their responses accordingly using intelligent
techniques. For instance, if an AMS-based clock is adjusted
to 6am from its usual 8am, the AMS-based coffee machine
learns this behavior and prepares the coffee according to this
time adjustment. Similarly, all other waking up sequence of
events (e.g., turning on tv) created by other AMSs are auto-
matically adjusted based on this time alteration. The more
intelligence in the edge as explored in [20], particularly for
the poaAMSs, the better performance can be attained from
our proposed architecture, which is illustrated in Fig. 7.
A large volume of data is created in poaAMSs and pro-
cessed near the data source to enable knowledge generation
to occur at the data source using data analytics, real-time data
processing, data caching, and computation offloading in the
edge platform. Most of this data is deleted in an intermittent
manner by intelligent data reduction tools after establishing
WaaSs and InaaSs using advanced self learning and decision
making techniques to guarantee that available memory is
not exceeded. Each update for WaaSs and InaaSs may make
the previous data obsolete. Useful data are transformed into
abstract forms, WaaSs or InaaSs to reduce the data traffic load
either for the notification of the customer or for vendors to
be able to evaluate how their systems are functioning. The
useful data in abstract form or as WaaSs or InaaSs are stored
in the cloud platform by transmitting packages from the edge
platform in aggregated and compressed forms to diminish
bandwidth requirements, transmission delay and packet loss,
which enables efficient streaming data collection, storing,
processing and transmitting. The cloud servers in the far end
environment can provide more computing power and more
long-term data storage, such as massive parallel data pro-
cessing, big data mining, big data management, ML [7], [69].
This, in turn, has led to an aggregation of data that is
now being employed to optimize all steps of the processes
to ensure higher quality goods are manufactured at lower
cost [5]. These abstract data forms are particularly analyzed
by the advanced big data tools to monitor the health of the
AMSs. Detected problems are fixed and lifetime optimization
of AMSs are carried out based on these results for both the
products that are being used by the customers, and the prod-
ucts that are being produced in the factory. Most importantly,
new products meeting the market are progressively improved
in 4IR in order not to cause any similar faults by learning from
the experiences of the products currently used by the existing
customers.
VOLUME 7, 2019 41407
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
Assume that a customer is using an intelligent autonomous
blind system working based on the changing daylight and
weather conditions along with user preferences. It needs
various sensors to be able to detect the current weather and
daylight conditions to work as desired. If it is working stand-
alone with all these sensors, we can call this system as a MS.
More explicitly, assuming that there is an ongoing issue with
the system and the customer is ringing the vendor to fix this
problem, where this system can be referred to as a MS. If the
customer is just able to control this system from home, not
from his way to the house or workplace, this can also be
determined as a MS. So, what makes a system an AMS? 1) the
system obtains the service, i.e., wisdom, as a WaaS from the
smart city domain in the cloud platform about how much it
is supposed to open or close the windows without knowing
the current outdoor conditions, 2) The user can monitor and
control the system using location-independent abilities on
the edge and cloud platforms, 3) a pending problem with
the system is predicted by the vendor using intelligent prog-
nosis embedded in the system using cloud platform; some
of the problems might be fixed in cyber-physical platforms
online and some of them can be fixed by engineers on the
site, in particular hardware problems, 4) patches, updates
or upgrades for the system are performed by the vendor in
the cloud platform. AMSs may still have sensors to obtain
the required information that is not or cannot be created in
the smart domains. In a similar way, a surgeon can operate
a patient from another location using location-independent
monitoring and operating abilities of AMSs, e.g. with the
help of an advanced da Vinci Robot. A pilot can direct an
airplane to land in any location under an emergency situation
using location-independent control abilities. MSs or other
electronic devices can be converted into AMSs by incorporat-
ing the properties mentioned here, particularly as illustrated
in Fig. 7, more detailed in Section VIII along with Fig. 10.
VII. WIRELESS COMMUNICATIONS ASSISTED AMSS
The world’s Internet population is growing significantly
year-over-year, where Internet access reached 47% of world
population totaling up to 3.8 billion people in 2017 [53].
Moreover, more than half of the world’s web traffic is
gleaned from smartphones, and it’s anticipated that about
6.1 billion people will have access to a smartphone by
2020 [53], which will demand advanced wireless commu-
nication techniques in the following years in order to meet
the increased capacity. In the last decade, wireless com-
munication and networking techniques used in the appli-
cations of mechatronics have immensely grown due to the
necessity of mobility, which enables an AMS/poaAMSs to
move around independently and to communicate wirelessly.
Some of these techniques can be observed in the applica-
tions of exploration using mobile robots [70], [71], under-
water systems [72], surgery [73], intelligent buildings [74],
pedestrian localization [75], tracking systems [76], [17],
micromedical robots for capsule endoscope [77], [78], local-
ization for pipeline inspection [79], robotic milling [80],
remote pain monitoring systems [81], stroke rehabilitation
systems [82] and intelligent transportation systems [83].
In the context of wireless mechatronics systems,
mobile robots can be exploited for cooperative infor-
mation gathering, simultaneous localization, map build-
ing (SLAM) and exploration of a fully or partially known
environment [70], [71]. However, Takahashi et al. [70]
observed a serious communication delay due to the nature of
communication method between mesh network and mobile
robot. Adopting 5G technology into such systems can signifi-
cantly reduce the end-to-end delay. Considering huge amount
of data and its processing in IIoT applications involving with
the cloud, the vision of 5G (one thousand times throughput
improvement, 100 billion device connections and close to
zero end-to-end latency) [84], [85] overlaps with the princi-
ples of advanced mechatronics systems and 4IR considering
that industrial robots require rapid decisions without any
communication delay.7
Remote individual health monitoring is also another cru-
cial application of wireless mechatronics within smart health
domains. Yang et al. [81] proposed a wearable device with
a biosensing facial mask to track and monitor pain intensity
of a patient via facial surface electromyogram, where the
wearable devices perform as wireless sensor nodes that are
integrated into an IoT platform. However, managing real time
wireless communication between sensor nodes, cloud server
and web application can be challenging mainly due to the GW
limitations. In such scenarios, heterogeneous communication
networks8[89]–[91] play a significant role in achieving the
required capacity increase. Another appealing application of
heterogeneous wireless MS can be found in [92], where aerial
and ground vehicles are coupled with multiple robotic plat-
forms each of which are characterized by different dynamics
and unique sensing abilities.
One compelling application of wireless mechatronics is
the robotic milling with the aid of wireless force sensing in
order to increase the accuracy of robotic milling, while con-
serving its adaptability [80], where wireless polyvinyldene
flouride sensors are utilized for real time force measure-
ment. Cen et al. [80] concerned with the high transmission
speed constraint between external PC and robot controller.
High data rates can indeed be compensated by a 5G ecosys-
tem encompassing a heterogeneous communication land-
scape, while incorporating communication networking and
IT resources with edge/fog/cloud-enabled services [86]–[88],
such as software and virtualization.
7Motivated readers are referred to [84], [86]–[88] for a better understand-
ing of the underlying technology behind 5G in order to reduce the end-to-end
delay to near zero.
8Heterogeneous networks (HetNets) refer to network architectures
deployed with distinct network devices, which are equipped with diverse
transmission power requirements and different data processing capabilities,
different radio access technology (RATs) support, and are provided for
a variety of backhaul links. For example, a HetNet may be collectively
deployed with multi-RAT networking and scheduling along with cellular-
based networks (potentially 5G) with high transmission power macro eNBs
and low transmission power pico eNBs.
41408 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
FIGURE 10. Framework criteria for the design and the development of AMSs.
The popularity of mobile devices and thus the immense
growth of data traffic has led to breakthroughs in small-
cell ultra-dense, massive-MIMO, millimeter-wave (and/or
terahertz) communications and networks constituting the
underlying technology of 5G, which promise to attain
gigabit wireless access for next-generation systems and
networks [93], [94]. High-data rate, low latency and highly
reliable communications provided by 5G can enable comput-
ing services at the remote cloud server. However, propagation
delay is an inherent constraint of the cloud computing due
to vast distance between end user and the cloud server. For
this particular reason, still cloud computing is not favorable
for emerging time-critical AMSs applications even with 5G
technology. It is commonly known that cloud computing is
insufficient to satisfy the millisecond-scale latency require-
ments of 5G. Solely relying on the data exchange between
end users and remote clouds may create data tsunami and
demolish the backhaul networks [94], [95]. Therefore, it is
vitally important to support cloud computing with MEC
in order to move traffic, computing and networking tasks
VOLUME 7, 2019 41409
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
towards the network edges. Noting that the use of edge or
fog computing aligns with MEC and these terminologies
are often used interchangeably [93], particularly where the
mobility measures take place. With 5G integrated fog com-
puting/MEC, we envision that AMSs will further advance
the WaaS, the InaaS, the poaAMSs, the production process,
the customer (user) feedback and the vendor interventions,
such as location-independent monitoring, configuration and
maintenance. It is also speculated in a recent study by Tariq
et al. [96] that sixth-generation (6G) will be empowered by
artificial intelligence (AI) and anticipated that AI will be col-
laboratively operating with distributed training at the network
edges, which is yet to be solved. In summary, the contribution
and the advances of wireless communication techniques for
the development of AMSs and 4IR is essential. For example,
radio frequency communication related impairments within
a very sophisticated communication network composed of
thousands of AMSs devices are the inherent communication
challenges to tackle with. Therefore, AMSs have to support
ultra-reliable low-latency communication (URLLC) and con-
duct AI-assisted operations at the edges, potentially through
5G-and-beyond (6G), and time-critical applications, such as
remote surgery will require less than 1ms delay, but the
upcoming 5G systems are not yet capable [96]. We anticipate
that real-time transmission of massive amount of data under
1ms delay requirements may be realized upon the release
of 5G-and-beyond (6G).
VIII. FUNDAMENTAL FEATURES OF AMSS
The framework criteria for the design and the development
of AMSs are presented in Fig. 10. Based on this figure,
essential features of AMSs distinguishing them from MSs
and concerns in addressing the high-level challenges in the
life-cycle of AMSs are disclosed as follows:
1. A mechatronics engineer along with a data engineer
and a communication engineer should be in the middle of
the development/integration process as a coordinator and
designer to establish an efficient collaboration within the phi-
losophy of integrated product development between multi-
disciplinary fields for both reducing time over-runs, over-
cost, unreliability and failures, and consequently to build
robust AMSs.
2. AMSs incorporate various components as described
in Figs. 6,7and 8into a harmonized working environment.
3. Wisdom for AMSs is attained from the cloud. For exam-
ple, the information of daily lighting conditions can be set as
InaaS and the wisdom of when to turn on/off the lights can
be set as WaaS for operating the security lights in any home
security system, which can be readily acquired from the smart
city domain. This use of WaaS reduces the complexity of
having excessive number of sensors, such as photoresistors.
4. Emerging wireless communication technologies are
essential for implementing AMSs to avoid latency for time-
critical applications of AMS, and to support huge amount of
data processing and transmission, which is also speculated
for the contribution of 5G-and-beyond (6G) into the artificial
intelligence assisted distributed training at the network edges
in [96].
5. When users are at smart domain ecosystems, an edge
environment is used for monitoring and control. Imple-
mentation of edge for local use provides reduced latency
and increased security. The edge also enables connectivity
amongst AMSs in a dedicated smart domain ecosystem.
6. A location-independent monitoring interface is another
requirement in AMSs using the cloud platform. This allows
users to monitor their system from anywhere and anytime,
and a rapid action can be taken by the vendor if any fault is
present in the system. Additionally, a location-independent
control in order to update and upgrade the systems or to
stop/start any services in poaAMSs should be performed via
the cloud platform to; i) remove faults, ii) provide better
functional performance, and iii) customize products that are
being actively used by the customers. Engineers, alerted
through the cloud, can assess and fix errors via updates to
provide lifetime optimization. Additionally, incorporating the
production plant into this cycle within AoE ecosystem can
effectively help new products to meet the market with less
error-prone components by learning from the experiences
of the products currently being used by the existing cus-
tomers. This interface is primarily needed to establish the
communication between the user/technician and the system
to provide visual feedback about the internal activities (e.g.,
fault detection), to modify the system with new parameters,
and to reset the system if required. In this context, lifetime
optimization of current and emerging AMSs will be provided,
which will result in longer life-cycle for AMSs and better
functioning abilities. There are leading companies that freely
provide these services on the cloud platform to which AMSs
in poaAMSs can be linked using easy-to-use interfaces such
as i) Google with the AndroidThings Console9by which
easy and secure deployment of updates, upgrades can be
concurrently performed for many devices and informative
analytics can be employed to understand how well AMSs are
performing, and ii) Wia Platform with location-independent
actuation services10 including several other options can trig-
ger any desired actions on/from mobile devices, which can
be used as a cloud platform for building effective AMS
with remote actuation abilities away from the poaAMSs
ecosystem and communicating them online anytime
anywhere.
7. A location-independent new service incorporation that is
already in the cloud platform in any domain or on the AMSs to
i) adapt to dynamically changing environmental parameters,
ii) provide better functional performance, and iii) customize
products that are being actively used by customers, mainly
based on changing customer preferences.
8. AMS should serve with no interruption within
autonomous prognosis and diagnosis decision making using
internal sensors, and proactive required alarms should be
9https://developer.android.com/things/console
10https://www.wia.io/
41410 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
triggered accordingly for maintenance, spare part replace-
ment or need of battery change via cloud or edge.
9. AMSs need to use advanced information fusion tech-
niques and data analytics for better decision making and bet-
ter traffic management between the edge and cloud platforms.
10. AMSs should have an open architecture with intelligent
sharing and services, and should be able to integrate with
other systems easily to share data and wisdom in the AoE
ecosystem.
11. AMSs should prioritize the user privacy and security
where there is a trade-off between various options using
appropriate authentication protocols and data encryption
tools in order to preserve the sensitive information on behalf
of the user.
12. AMSs in AoE should have the customizing features
that can learn from user patterns and behave accordingly
in a sequence of triggering events without requiring user
intervention in order to increase QoS perpetually.
IX. CHALLENGES
The emerging AMSs pose new challenges, some of which
cannot be adequately addressed by current state-of-the-art
cyber-security solutions, cloud and host computing models
alone, which exacerbates the current situation. To unleash
AMSs potential, these challenges raising concerns must be
addressed well in terms of both technical infrastructure and
the management of data, and human factors, such as privacy
and security. The main challenges are presented as follows:
1. Lack of cross-functional interdisciplinary knowledge
or experienced mechatronics/system engineers along with
data scientists: AMSs with various intertwined systems are
related to the notion of getting interdisciplinary interactions
between several engineering disciplines to work together har-
moniously and requires knowledge of different skills between
various expertises, which do not simultaneously exist in
companies and hampers the development of AMSs rapidly.
Cooperation of various disciplines in different expertises and
cultures makes the processes difficult since one discipline
may not be well aware of the capabilities of other disciplines,
which reduces the reasonable imagination, such as unpre-
dictability impact of a design change or design/integration
conflicts across disciplines, difficulty predicting about how
advanced mechatronics product act until physical prototypes
exist and difficulty in getting the desired product to the market
fast.
2. Insufficient modelling tools and methods: Engineer-
ing tools for design, deployment, management, opera-
tion, security, and migration are currently the great-
est bottleneck [11]. Insufficient functionalities of current
design tools to communicate seamlessly across disciplines,
to design/explore/model/outline all the components, to iden-
tify system-level problems early and consequently to make
up a robust product is evident.
3. Heterogeneity and incompatible protocols: Electronical
products established by various manufacturers (e.g., Ama-
zon, Google, Samsung, Cisco, Nuimo, Lifx, Sonos, Philips
(Hue), Mixtile etc.) may not be compatible with each other
with respect to dedicated protocols and electronics design,
which makes the integration of new devices a challenging
task. Simple technology standards are not adequate for the
integration and interoperability with many other different
devices. Different devices from variety of manufacturers are
mostly based on distinct protocols [7]. In this respect, making
system components interoperable, so that devices from differ-
ent manufacturers operating in different application domains
can work together, is essential [23]. Innovative directions are
required by governments, national and international leading
organization to guide for agreed upon standards.
4. System integration: The edge computing is a hetero-
geneous platform incorporating various technologies, pro-
tocols, platforms, network topologies, and servers, which
requires skillful and qualified engineers from various disci-
pline, such as data, computer, electronics and communication
engineers. Therefore, it will be difficult to program and man-
age resources for diverse applications running on varying and
heterogeneous platforms at distinct locations [7].
5. Cybersecurity: Cybersecurity (e.g., protection of indus-
trial know how, data security) remains an ongoing prob-
lem despite progress in secure communications and stor-
age of data [97]. There are still competing standards, insuf-
ficient security, complex communications, and proliferat-
ing numbers of poorly tested devices [39]. Based on this
background, security is one of the crucial issues that
needs to be addressed to enable an efficient and reliable
communication [45]. Secure communications and data stor-
age require the use of strong cryptographic techniques which
are often computationally expensive [5]. Remotely managed
control systems require reliable, scalable, and sustainable
solutions for increased usability, management, and rapid
response. Those systems play a significant role in AMSs
and cannot be hijacking-prone. Malwares to attack industrial
automation, to gain control over devices or corporate espi-
onage to steal sensitive data from competitors have to be
avoided.
6. Legal issues: The legal challenges, in particular, the pri-
vacy of people using cyber-physical domains have yet to
be solved [98].11 The responsibilities of vendors giving the
cloud services such as Google, Amazon, Microsoft against
the customers are not well defined within the national and
international laws of electronic commerce.
7. Privacy: Only authorized entities should be able to
use services [23]. The cryptographic mechanisms impose
some overhead in terms of processing and amount of
data transmitted [99]. Furthermore, lack of standards, secure
authentication and authorization policies are yet to be dis-
cussed. What happens if a smartphone operating as an edge
platform is hacked by a cyber attacker; cameras that are meant
for surveillance may turn into cameras that are violating our
privacy.
11The motivated readers are referred to [98] for the analysis of privacy and
security legislations regarding IoT and cloud use.
VOLUME 7, 2019 41411
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
8. Technical difficulties: Pinpointing the problem might
be difficult throughout within heterogeneity. What happens
when a smartphone acting as an edge or GW is not function-
ing (broken, out of charge); more connection to other systems
or devices and smart environments requires more data sharing
and there is a lack of technical expertise to manage AMSs and
fix emerging problems quickly.
9. Hardware (HW) and Software (SW) compatibility: The
integration of HW and SW in AMSs is not a sequential,
rather parallel process, one effects and changes the design
and integration process of the other significantly. The lead-
ing SW companies are not able to produce their operating
systems or SWs compatible with the CBCDs emerging in the
market quickly. For instance, it has been 8 months since the
Raspberry Pi 3 B+was marketed and still Android Things
operating system is not able to work on this model. The
companies developing HW (e.g., SBCDs) should collaborate
with the leading SW companies to make their products com-
patible with many other products in order to accelerate the
sustainability of the products in the market.
10. Egde, cloud and IoT related problems: As an emerg-
ing field of study, edge computing is still in its infancy
and faces many challenges in its implementation and
standardization [100]. IoT devices have vulnerabilities due to
lack of transport encryption, insecure Web interfaces, inade-
quate software protection, and insufficient authorization [39].
The variety of standards and technologies in these elements
and the way they should interoperate is a main challenge that
can impede the development of IoT applications [45] and the
development of AMSs that need to communicate with those
devices.
11. Big data management challenge: Managing huge
amount of data created in the cloud that consist of highly-
valued information merged with unnecessary and erroneous
data is also a challenging task [38]. A recent research by IBM
shows that 1% of data collected by organizations is used for
analysis [101] in which required WaaS and InaaS cannot be
revealed sufficiently. Therefore, it can be safely concluded
that the big data need more powerful data analytics. Big data
that need to be processed and stored is an important character-
istic of the hyper-world, so how to effectively manage, mine,
and utilize the big data as WaaS and InaaS in order to create
successful AMSs, improve the ability and quality of AMSs
are the immediate concerns.
X. DISCUSSIONS
In future mobile networks, such as 5G emerging smart ser-
vices are expected to support billions of smart devices with
unique characteristics and traffic patterns [102] to achieve a
flexible and efficient communication, consequently an excel-
lent synergy. WaaS will be the core architecture of IT appli-
cations in the coming age of the hyper-world [57]. It is worth
noting that industrial companies are re-evaluating the way
they do business and feeling the increasing pressure to adapt
to the changing environment with cyber-physical systems by
recognizing the benefit of connecting devices and services to
the Internet in which too many smart ideas to be explored and
exploited and excessive room is available for IT experts and
creative people. Transforming the systems and products into
more intelligent, more autonomous AMSs is necessary for
leading companies to produce quality products with increased
customizable functionalities and maintain a competitive edge
through better product performance in order to meet the mar-
ket dynamics, in particular, changing consumer habituation
and demands. Therefore, companies need more mechatronics
engineers along with more data scientists to develop robust
AMSs by exploiting system engineering design throughout
modelling, simulation, analyzing, refining, prototyping, val-
idating, deployment and life-cycle improvement of AMSs
with respect to the underlying disparate engineering disci-
plines. In this context, we envision that new companies highly
expertised in designing and developing AMSs and next gen-
eration of CPSs will emerge to help other companies develop
their own AMSs.
High-level topics concerning today’s production of goods
and services include sustainability, flexibility, efficiency, and
competitiveness [11]. The new consistent revolution will rise
over the use of the created wisdom. The increasing volumes of
information created in cloud IoT analytics is being turned into
wisdom using data mining, AI (e.g., reinforcement learning),
ML/deep learning, pattern recognition, vision/image/speech
processing etc. This, in turn, has led to an aggregation of data
that is now being employed to optimize all steps of the process
to ensure higher quality goods, which can be manufactured at
lower cost [5]. To the extent of our knowledge, optimization
of all steps of the production process has not been dissemi-
nated in the literature, which can be extracted from the frame-
work of AoE discussed and proposed in our study. Regard-
ing exponentially increasing smart applications in increasing
smart domains, WaaS is expected be the core architecture of
IT applications in the coming era of the hyper-world. It is
also anticipated to bring huge economic value for intelligent
IT industry by realizing the pay-as-you-go model [51].
With 4IR, the prevalence of robotics in society will be
one of the most notable changes. Robots are already uti-
lized in manufacturing facilities across the world. However,
the use of robotics is not limited to manufacturing. Recent
developments include service robots to help people with
vision problems, low-cost robots to assist with grocery shop-
ping, and autonomous robots that can inspect the structural
health of nuclear plants and underground mines [21]. The
healthcare industry is already using robotics to help per-
form surgery (e.g., da Vinci Robot), as well as to transport
and manufacture medication [21]. More customizable robust
products using advanced intelligent machines within 4IR
will be developed using more intelligent embedded soft-
ware techniques with the help of the framework criteria of
AMSs and AoE proposed in our study. Consequently, more
advanced miniaturized and interconnected AMSs will emerge
in the market in the following years, particularly, using
AI, MEMS, nano technologies and emerging cutting-edge
communication technologies within the AoE. To prevent
41412 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
chaos in the hyper-connected world, businesses need to make
every effort to reduce the complexity of the connected sys-
tems, enhance the security and standardization of applica-
tions, and to ensure the safety and privacy of users anytime,
anywhere and on any device [39]. By focusing on user inter-
action and configurability, on lifetime optimization, on intel-
ligent analysis of created big data, on location-independent
monitoring and control, on data security and reduced system
complexity using WaaS, the overall appeal of the final prod-
uct is grown regarding the design of AMSs within the AoE
ecosystem that is proposed in this treatise.
XI. CONCLUSIONS AND DIRECTIONS FOR FUTURE
RESEARCH
Mechatronics thinking has led to CPS and consequently smart
domains which are now transforming MSs into AMSs in
smart platforms. The need for a high product diversification
based on the continuously changing market and customer
demands within customizations, reduced production quanti-
ties, reduced concept-to-market lead times and lifetime opti-
mization of products has resulted in a shift in the approaches
adopted for manufacturing, particularly through the availabil-
ity of smart domains. Against this background, in this study,
we analyzed the transformation from MSs into AMSs with
respect to the recent advances in 4IR, cyber-physical smart
domains and platforms, communication technologies, and an
architectural design is proposed along with the fundamental
features of AMS. By adopting the approaches proposed in
this study, 1) companies will be able to track and monitor
the performance of their smart products in real time and
will be able to fix their problems, most of which on-line,
2) the efficacy of smart products will increase through effec-
tive product lifecycle management, 3) the structure of these
products will be less complex, consequently less error-prone,
4) the customization will be easier, 5) the cost will decrease
significantly with less number of sensors within less complex
structures using WaaSs and InaaSs, 6) customer satisfaction
will increase within smoothly working environment with
24/7 seamlessly working products.
The user driven development of new and innovative AMSs
will be rapidly replacing the current products in the mar-
ket. AMSs within the distributed cloud approaching to the
edges and 4IR will have a great impact on the economy by
transforming many enterprises from digital businesses into
intelligent businesses and facilitating new business models,
improving efficiency and increasing employee and customer
engagement within AoE. We envision that the invention of
efficient and robust models in AMSs will further advance the
future research and innovative products based on the concepts
of AMSs presented in this treatise in a perpetual revolution of
the industry. Additionally, the transformation of the big data
created both on the edge and cloud platforms into wisdom to
establish more services as WaaS and InaaS will be immensely
focused on along with the overcoming challenges mentioned
in Section IX, which will foster the development of more
functional successor models of AMSs and consequently the
technological advances of AMSs ought to make our life better
and simpler.
REFERENCES
[1] G. S. Hegde, mechatronics. London, U.K.: Jones and Bartlett, 2010.
[2] A. Kuttan, Introduction to Mechatronics. New Delhi, India: Oxford Univ.
Press, 2007.
[3] R. Isermann, ‘‘On the design and control of mechatronic systems-a sur-
vey,’’ IEEE Trans. Ind. Electron., vol. 43, no. 1, pp. 4–15, Feb. 1996.
[4] D. G. Alciatore and M. B. Histand, Introduction to Mechatronics and
Measurement Systems, 5th ed. New York, USA: McGraw-Hill, 2018.
[5] D. Bradley, D. Russell, I. Ferguson, J. Isaacs, A. MacLeod, and R. White,
‘‘The Internet of Things—The future or the end of mechatronics,’’
Mechatronics, vol. 27, pp. 57–74, Apr. 2015. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S0957415815000215
[6] A. Sharma, T. Goyal, E. S. Pilli, A. P. Mazumdar, M. C. Govil, and
R. C. Joshi, ‘‘A secure hybrid cloud enabled architecture for Internet
of Things,’’ in Proc. IEEE 2nd World Forum Internet Things (WF-IoT),
Dec. 2015, pp. 274–279.
[7] W. Yu et al., ‘‘A survey on the edge computing for the Internet of Things,’
IEEE Access, vol. 6, pp. 6900–6919, 2018.
[8] S. Taylor, ‘‘The next generation of the Internet revolutionizing
the way we work, live, play, and learn,’’ CISCO, San
Francisco, CA, USA, Tech. Rep., 2013. [Online]. Available:
https://www.cisco.com/c/dam/en_us/about/ac79/docs/sp/Next-
Generation-of-the-Internet.pdf
[9] J. Li, M. Peng, A. Cheng, Y. Yu, and C. Wang, ‘‘Resource allocation
optimization for delay-sensitive traffic in fronthaul constrained cloud
radio access networks,’IEEE Syst. J., vol. 11, no. 4, pp. 2267–2278,
Dec. 2017.
[10] W. S. Levine, Control System Fundamentals, 1st ed. Boca Raton, FL,
USA: CRC Press, 2000.
[11] J. Delsing, ‘‘Local cloud Internet of Things automation: Technology and
business model features of distributed Internet of Things automation
solutions,’IEEE Ind. Electron. Mag., vol. 11, no. 4, pp. 8–21, Dec. 2017.
[12] J. Lee, B. Bagheri, and H.-A. Kao, ‘‘A cyber-physical systems
architecture for industry 4.0-based manufacturing systems,’Manuf.
Lett., vol. 3, pp. 18–23, Jan. 2015. [Online]. Available: http://www.
sciencedirect.com/science/article/pii/S221384631400025X
[13] M. Hermann, T. Pentek, and B. Otto, ‘‘Design principles for industrie 4.0
scenarios,’’ in Proc. 49th Hawaii Int. Conf. Syst. Sci. (HICSS), Jan. 2016,
pp. 3928–3937.
[14] B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, ‘‘Smart factory
of industry 4.0: Key technologies, application case, and challenges,’’
IEEE Access, vol. 6, pp. 6505–6519, 2017.
[15] G. Peralta, M. Iglesias-Urkia, M. Barcelo, R. Gomez, A. Moran, and
J. Bilbao, ‘‘Fog computing based efficient IoT scheme for the industry
4.0,’’ in Proc. IEEE Int. Workshop Electron., Control, Meas., Signals
Appl. Mechatronics (ECMSM), May 2017, pp. 1–6.
[16] M. C. S.-W. Lin and S. Mellor. (2016). The Industrial Internet
of Things Volume G1: Reference Architecture. [Online]. Available:
https://www.iiconsortium.org/IIC_PUB_G1_V1.80_2017-01-31.pdf
[17] J. K. Winn and B. Wright, The Law Electron. Commerce, 4th ed. NY,
USA: Wolters Kluwer, 2019.
[18] O. Jaradat, I. Sljivo, I. Habli, and R. Hawkins, ‘‘Challenges of safety
assurance for industry 4.0,’’ in Proc. 13th Eur. Dependable Comput. Conf.
(EDCC), Sep. 2017, pp. 103–106.
[19] (2018). Robots Double Worldwide by 2020. [Online]. Available:
https://ifr.org/ifr-press-releases/news/robots-double-worldwide-by-2020
[20] P. Patel, M. I. Ali, and A. Sheth, ‘‘On using the intelligent edge for IoT
analytics,’IEEE Intell. Syst., vol. 32, no. 5, pp. 64–69, Sep. 2017.
[21] (2019). IEEE is Fueling the Fourth Industrial Revolution. [Online].
Available: https://innovate.ieee.org/innovation-spotlight-ieee-fueling-
fourth-industrial-revolution/
[22] Z. Sheng, C. Mahapatra, C. Zhu, and V. C. M. Leung, ‘‘Recent advances in
industrial wireless sensor networks toward efficient management in IoT,’’
IEEE Access, vol. 3, pp. 622–637, Jun. 2015.
[23] E. Simmon, S. K. Sowe, and K. Zettsu, ‘‘Designing a cyber-physical cloud
computing architecture,’IT Prof., vol. 17, no. 3, pp. 40–45, May 2015.
[24] E. Borgia, ‘‘The Internet of Things vision: Key features, appli-
cations and open issues,’Comput. Commun., vol. 54, pp. 1–31,
Dec. 2014. [Online]. Available: http://www.sciencedirect.com/science/
article/pii/S0140366414003168
VOLUME 7, 2019 41413
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
[25] M. J. Kaur and P. Maheshwari, ‘‘Building smart cities applications using
IoT and cloud-based architectures,’’ in Proc. Int. Conf. Ind. Inform.
Comput. Syst. (CIICS), Mar. 2016, pp. 1–5.
[26] P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. K. Ganapathiraju,
‘‘Everything you wanted to know about smart health care: Evaluating the
different technologies and components of the Internet of Things for better
health,’IEEE Consum. Electron. Mag., vol. 7, no. 1, pp. 18–28, Jan. 2018.
[27] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, ‘‘Internet
of Things (IoT): A vision, architectural elements, and future direc-
tions,’Future Generat. Comput. Syst., vol. 29, no. 7, pp. 1645–1660,
2013. [Online]. Available: http://www.sciencedirect.com/science/article/
pii/S0167739X13000241
[28] SecureEnergy. (2018). America’s Workforce and the Self-Driving
Future: Realizing Productivity Gains and Spurring Economic
Growth. [Online]. Available: https://avworkforce.secureenergy.org/wp-
content/uploads/2018/06/SAFE_AV_Policy_Brief.pdf
[29] M. Chiang and T. Zhang, ‘‘Fog and IoT: An overview of research oppor-
tunities,’IEEE Internet Things J., vol. 3, no. 6, pp. 854–864, Dec. 2016.
[30] R. S. Montero, E. Rojas, A. A. Carrillo, and I. M. Llorente, ‘‘Extending
the cloud to the network edge,’Computer, vol. 50, no. 4, pp. 91–95,
Apr. 2017.
[31] Y. Ai, M. Peng, and K. Zhang, ‘‘Edge computing technologies for Internet
of Things: A primer,’’ Digit. Commun. Netw., vol. 4, no. 2, pp. 77–86,
2018. [Online]. Available: http://www.sciencedirect.com/science/article/
pii/S2352864817301335
[32] M. Peng and K. Zhang, ‘‘Recent advances in fog radio access networks:
Performance analysis and radio resource allocation,’IEEE Access, vol. 4,
pp. 5003–5009, Sep. 2016.
[33] M. Yousif, ‘‘Cloudy, foggy and edgy,’’ IEEE Cloud Comput., vol. 4, no. 2,
pp. 4–5, Mar. 2017.
[34] O. Consortium. (2016). Openfog Architecture Overview. [Online]. Avail-
able: https://www.alibabacloud.com/blog/bringing-iot-to-the-cloud-fog-
computing-and-cloudlets_593824
[35] A. Munir, P. Kansakar, and S. U. Khan, ‘‘IFCIoT: Integrated fog
cloud IoT: A novel architectural paradigm for the future Internet of
Things,’IEEE Consum. Electron. Mag., vol. 6, no. 3, pp. 74–82,
Jul. 2017.
[36] Y. Jararweh, A. Doulat, O. AlQudah, E. Ahmed, M. Al-Ayyoub, and
E. Benkhelifa, ‘‘The future of mobile cloud computing: Integrating
cloudlets and mobile edge computing,’’ in Proc. 23rd Int. Conf. Telecom-
mun. (ICT), May 2016, pp. 1–5.
[37] G. Tanganelli, C. Vallati, and E. Mingozzi, ‘‘Edge-centric distributed
discovery and access in the Internet of Things,’’ IEEE Internet Things
J., vol. 5, no. 1, pp. 425–438, Feb. 2018.
[38] D. Brock, ‘‘The electronic product code (EPC)—A naming scheme for
physical objects,’’ Massachusetts Inst. Technol., Cambridge, MA, USA,
Tech. Rep. MIT-AUTOID-WH-002, 2001.
[39] I. Lee and K. Lee, ‘‘The Internet of Things (IoT): Applications, invest-
ments, and challenges for enterprises,’Bus. Horizons, vol. 58, no. 4,
pp. 431–440, 2015. [Online]. Available: http://www.sciencedirect.com/
science/article/pii/S0007681315000373
[40] K. Rose, S. D. Eldridge, and L. Chapin, ‘‘The Internet of Things: An
overview understanding the issues and challenges of a more connected
world,’’ Internet Soc., Geneve, Switzerland, Tech. Rep. 2, 2015.
[41] CISCO. (2011). The Internet of Things How the Next Evolution of the
Internet is Changing Everything. [Online]. Available: http://www.cisco.
com/c/dam/enus/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf
[42] Z. Sheng, S. Yang, Y. Yu, A. Vasilakos, J. McCann, and K. Leung,
‘‘A survey on the IETF protocol suite for the Internet of Things: Stan-
dards, challenges, and opportunities,’IEEE Wireless Commun., vol. 20,
no. 6, pp. 91–98, Dec. 2013.
[43] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, ‘‘Inter-
net of Things: Vision, applications and research challenges,’’ Ad Hoc
Netw., vol. 10, no. 7, pp. 1497–1516, 2012. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S1570870512000674
[44] Q. Zhang, Z. Yu, W. Shi, and H. Zhong, ‘‘Demo abstract: EVAPS:
Edge video analysis for public safety,’’ in Proc. IEEE/ACM Symp. Edge
Comput. (SEC), Oct. 2016, pp. 121–122.
[45] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash,
‘‘Internet of Things: A survey on enabling technologies, protocols,
and applications,’IEEE Commun. Surveys Tuts., vol. 17, no. 4,
pp. 2347–2376, 4th Quart., 2015.
[46] M. Chen, V. C. M. Leung, R. Hjelsvold, and X. Huang, ‘‘Smart
and interactive ubiquitous multimedia services,’’ Comput. Commun.,
vol. 35, no. 15, pp. 1769–1771, 2012. [Online]. Available: http://www.
sciencedirect.com/science/article/pii/S0140366412002538
[47] R. Poovendran, ‘‘Cyber-physical systems: Close encounters between
two parallel worlds [point of view],’’ Proc. IEEE, vol. 98, no. 8,
pp. 1363–1366, Aug. 2010.
[48] I. Akyildiz and M. C. Vuran, Wireless Sensor Network. New York, NY,
USA: Wiley, 2010.
[49] Y. Zhang, R. Yu, S. Xie, W. Yao, Y. Xiao, and M. Guizani, ‘‘Home
M2M networks: Architectures, standards, and QoS improvement,’’ IEEE
Commun. Mag., vol. 49, no. 4, pp. 44–52, Apr. 2011.
[50] P. Karhula, J. Makela, H. Rivas, and M. Valta, ‘‘Internet of Things
connectivity with gateway functionality virtualization,’’ in Proc. Global
Internet Things Summit (GIoTS), Jun. 2017, pp. 1–6.
[51] J. Chen et al., ‘‘WaaS—Wisdom as a service,’’ IEEE Intell. Syst., vol. 29,
no. 6, pp. 40–47, Nov. 2014.
[52] S. Tayeb, S. Latifi, and Y. Kim, ‘‘A survey on IoT communication and
computation frameworks: An industrial perspective,’’ in Proc. IEEE 7th
Annu. Comput. Commun. Workshop Conf. (CCWC), Jan. 2017, pp. 1–6.
[53] DOMO. (2011). Data Never Sleeps 6.0. [Online]. Available:
https://www.domo.com/learn/data-never-sleeps-6
[54] M. Amadeo, C. Campolo, A. Iera, and A. Molinaro, ‘‘Information centric
networking in IoT scenarios: The case of a smart home,’’ in Proc. IEEE
Int. Conf. Commun. (ICC), Jun. 2015, pp. 648–653.
[55] Y. Zhang, H. Huang, Y. Xiang, L. Y. Zhang, and X. He, ‘‘Harnessing the
hybrid cloud for secure big image data service,’IEEE Internet Things J.,
vol. 4, no. 5, pp. 1380–1388, Oct. 2017.
[56] L. Mearian. (2016). Internet of Things data to top 1.6 Zettabytes
by 2022. [Online]. Available: https://campustechnology.com/articles/
2015/04/15/internet-of-thingsdata-to-top-1-6-zettabytes-by-2020.aspx
[57] J. Chen et al., ‘‘WaaS—Wisdom as a service,’’ in Wisdom Web Things,
N. Zhong, J. Ma, J. Liu, R. Huang, and X. Tao, Eds. Cham, Switzerland:
Springer, 2016, ch. 2, pp. 27–46.
[58] W. Bolton, Mechatronics: Electronic Control Systems in Mechanical and
Electrical Engineering, 5th ed. Saddle River, NJ, USA: McGraw-Hill,
2015.
[59] M. Sitti, A. Menciassi, A. J. Ijspeert, K. H. Low, and S. Kim, ‘‘Survey
and introduction to the focused section on bio-inspired mechatronics,’
IEEE/ASME Trans. Mechatronics, vol. 18, no. 2, pp. 409–418, Apr. 2013.
[60] RSComponents. (2018). The Race for AI: Who’s Acquiring all
the AI Startups. [Online]. Available: https://uk.rs-online.com/web/
generalDisplay.html?id=i/race-for-ai
[61] M. W. Condry and C. B. Nelson, ‘‘Using smart edge IoT devices for
safer, rapid response with industry IoT control operations,’’ Proc. IEEE,
vol. 104, no. 5, pp. 938–946, May 2016.
[62] N. Kaur and S. K. Sood, ‘‘An energy-efficient architecture for the Internet
of Things (IoT),’IEEE Syst. J., vol. 11, no. 2, pp. 796–805, Jun. 2017.
[63] L. Mearian. (2016). Self-Driving Cars Could Create 1GB of
Data a Second. [Online]. Available: https://www.computerworld.
com/article/2484219/emerging-technology/self-driving-cars-could-
create-1gb-of-data-a-second.html
[64] E. Oyekanlu, ‘‘Predictive edge computing for time series of industrial
IoT and large scale critical infrastructure based on open-source software
analytic of big data,’’ in Proc. IEEE Int. Conf. Big Data (Big Data),
Dec. 2017, pp. 1663–1669.
[65] H. Yetgin, K. T. K. Cheung, M. El-Hajjar, and L. H. Hanzo, ‘‘A survey
of network lifetime maximization techniques in wireless sensor
networks,’IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 828–854,
2nd Quart., 2017.
[66] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, ‘‘The case for
VM-based cloudlets in mobile computing,’IEEE Pervasive Comput.,
vol. 8, no. 4, pp. 14–23, Oct. 2009.
[67] N. Apthorpe, D. Reisman, and N. Feamster, ‘‘A smart home is no castle:
Privacy vulnerabilities of encrypted IoT traffic,’’ CoRR, vol. 1705,
pp. 1–6, May 2017. [Online]. Available: http://arxiv.org/abs/1705.06805
[68] S. Balampanis, S. Sotiriadis, and E. G. M. Petrakis, ‘‘Internet of Things
architecture for enhanced living environments,’’ IEEE Cloud Comput.,
vol. 3, no. 6, pp. 28–34, Nov. 2016.
[69] Z. Chen et al., ‘‘A cloud computing based network monitoring
and threat detection system for critical infrastructures,’Big
Data Res., vol. 3, pp. 10–23, Apr. 2016. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S2214579615000520
[70] J. Takahashi, T. Yamaguchi, K. Sekiyama, and T. Fukuda,
‘‘Communication timing control and topology reconfiguration of a
sink-free meshed sensor network with mobile robots,’IEEE/ASME
Trans. Mechatronics, vol. 14, no. 2, pp. 187–197, Apr. 2009.
[71] Y. Gao, H. Chen, Y. Li, C. Lyu, and Y. Liu, ‘‘Autonomous Wi-Fi relay
placement with mobile robots,’IEEE/ASME Trans. Mechatronics,
vol. 22, no. 6, pp. 2532–2542, Dec. 2017.
41414 VOLUME 7, 2019
K. Kuru, H. Yetgin: Transformation to Advanced Mechatronics Systems Within New Industrial Revolution
[72] C. Detweiler, M. Doniec, I. Vasilescu, and D. Rus, ‘‘Autonomous depth
adjustment for underwater sensor networks: Design and applications,’
IEEE/ASME Trans. Mechatronics, vol. 17, no. 1, pp. 16–24, Feb. 2012.
[73] C. D. W. Ward, A. L. Trejos, M. D. Naish, R. V. Patel, and
C. M. Schlachta, ‘‘The WHaSP: A wireless hands-free surgical pointer
for minimally invasive surgery,’’ IEEE/ASME Trans. Mechatronics,
vol. 17, no. 3, pp. 434–442, Jun. 2012.
[74] N. K. Suryadevara, S. C. Mukhopadhyay, S. D. T. Kelly, and S. P. S. Gill,
‘‘WSN-based smart sensors and actuator for power management in
intelligent buildings,’IEEE/ASME Trans. Mechatronics, vol. 20, no. 2,
pp. 564–571, Apr. 2015.
[75] H. Hur and H.-S. Ahn, ‘‘Unknown input observer-based filterings for
mobile pedestrian localization using wireless sensor networks,’IEEE
Sensors J., vol. 14, no. 8, pp. 2590–2600, Aug. 2014.
[76] Y. Lai and J. Cheng, ‘‘A cloud-storage RFID location tracking system,’
IEEE Trans. Magn., vol. 50, no. 7, pp. 1–4, Jul. 2014.
[77] R. Zhang, H. Liu, Q. Shao, G. Li, X. Fang, and H. Li, ‘‘Effects of wireless
power transfer on capacitive coupling human body communication,’’
IEEE/ASME Trans. Mechatronics, vol. 20, no. 3, pp. 1440–1447,
Jun. 2015.
[78] J. Gao et al., ‘‘Design and testing of a motor-based capsule robot powered
by wireless power transmission,’IEEE/ASME Trans. Mechatronics,
vol. 21, no. 2, pp. 683–693, Apr. 2016.
[79] D. Wu, D. Chatzigeorgiou, K. Youcef-Toumi, and R. Ben-Mansour,
‘‘Node localization in robotic sensor networks for pipeline inspection,’’
IEEE Trans. Ind. Informat., vol. 12, no. 2, pp. 809–819, Apr. 2016.
[80] L. Cen, S. N. Melkote, J. Castle, and H. Appelman, ‘‘A wireless
force-sensing and model-based approach for enhancement of machining
accuracy in robotic milling,’IEEE/ASME Trans. Mechatronics, vol. 21,
no. 5, pp. 2227–2235, Oct. 2016.
[81] G. Yang et al., ‘‘IoT-based remote pain monitoring system: From device
to cloud platform,’IEEE J. Biomed. Health Informat., vol. 22, no. 6,
pp. 1711–1719, Nov. 2018.
[82] G. Yang et al., ‘‘An IoT-enabled stroke rehabilitation system based on
smart wearable armband and machine learning,’IEEE J. Transl. Eng.
Health Med., vol. 6, pp. 1–10, 2018.
[83] Y. Wang, B. Kong, H. Leung, H. Jiang, and S. Chen, ‘‘An experimental
vehicular wireless system and link performance analysis,’IEEE/ASME
Trans. Mechatronics, vol. 17, no. 3, pp. 508–518, Jun. 2012.
[84] I. Parvez, A. Rahmati, I. Guvenc, A. I. Sarwat, and H. Dai, ‘‘A survey
on low latency towards 5G: RAN, core network and caching solutions,’’
IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 3098–3130,
4th Quart. 2018.
[85] G. Yang, M. Xiao, M. Alam, and Y. Huang, ‘‘Low-latency heterogeneous
networks with millimeter-wave communications,’’ IEEE Commun. Mag.,
vol. 56, no. 6, pp. 124–129, Jun. 2018.
[86] T. S. Rappaport et al., ‘‘Millimeter wave mobile communications
for 5G cellular: It will work!’’ IEEE Access, vol. 1, pp. 335–349,
May 2013.
[87] F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski,
‘‘Five disruptive technology directions for 5G,’’ IEEE Commun. Mag.,
vol. 52, no. 2, pp. 74–80, Feb. 2014.
[88] A. Gupta and E. R. K. Jha, ‘‘A survey of 5G network: Architecture and
emerging technologies,’IEEE Access, vol. 3, pp. 1206–1232, Jul. 2015.
[89] R. Q. Hu and Y. Qian, ‘‘An energy efficient and spectrum efficient
wireless heterogeneous network framework for 5G systems,’’ IEEE
Commun. Mag., vol. 52, no. 5, pp. 94–101, May 2014.
[90] S. Barbarossa, S. Sardellitti, and P. D. Lorenzo, ‘‘Communicating while
computing: Distributed mobile cloud computing over 5G heterogeneous
networks,’IEEE Signal Process. Mag., vol. 31, no. 6, pp. 45–55,
Nov. 2014.
[91] Q. C. Li, H. Niu, A. T. Papathanassiou, and G. Wu, ‘‘5G network
capacity: Key elements and technologies,’’ IEEE Veh. Technol. Mag.,
vol. 9, no. 1, pp. 71–78, Mar. 2014.
[92] N. Bezzo, B. Griffin, P. Cruz, J. Donahue, R. Fierro, and J. Wood,
‘‘A cooperative heterogeneous mobile wireless mechatronic system,’
IEEE/ASME Trans. Mechatronics, vol. 19, no. 1, pp. 20–31, Feb. 2014.
[93] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, ‘‘A survey
on mobile edge computing: The communication perspective,’’ IEEE
Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322–2358, 4th Quart., 2017.
[94] E. K. Markakis, K. Karras, A. Sideris, G. Alexiou, and E. Pallis, ‘‘Com-
puting, caching, and communication at the edge: The cornerstone for
building a versatile 5G ecosystem,’’ IEEE Commun. Mag., vol. 55, no. 11,
pp. 152–157, Nov. 2017.
[95] T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, ‘‘On
multi-access edge computing: A survey of the emerging 5G network
edge cloud architecture and orchestration,’IEEE Commun. Surveys
Tuts., vol. 19, no. 3, pp. 1657–1681, 3rd Quart., 2017.
[96] F. Tariq, M. Khandaker, K.-K. Wong, M. Imran, M. Bennis, and
M. Debbah, ‘‘A speculative study on 6G,’IEEE Commun. Mag., to be
published. [Online]. Available: https://arxiv.org/abs/1902.06700
[97] B. Hammack, S. Kranz, and B. Carpenter, Cryptography and Network
Security: Principles and Practice, 6th ed. Glenview, IL, USA: Pearson,
2013.
[98] R. H. Weber, ‘‘Internet of Things—New security and privacy
challenges,’Comput. Law Secur. Rev., vol. 26, no. 1, pp. 23–30,
2010. [Online]. Available: http://www.sciencedirect.com/science/article/
pii/S0267364909001939
[99] L. A. B. Pacheco, E. Alchieri, and P. A. S. Barreto, ‘‘Enhancing and
evaluating an architecture for privacy in the integration of Internet of
Things and cloud computing,’’ in Proc. IEEE 16th Int. Symp. Netw.
Comput. Appl. (NCA), Oct. 2017, pp. 1–8.
[100] J. Ren, H. Guo, C. Xu, and Y. Zhang, ‘‘Serving at the edge: A scalable
IoT architecture based on transparent computing,’IEEE Netw., vol. 31,
no. 5, pp. 96–105, Aug. 2017.
[101] D. Newman, ‘‘Ttop 10 digital transformation trends for 2019,’’
Forbes, Jersey City, NJ, USA, Tech. Rep., 2018. [Online]. Available:
https://www.forbes.com/sites/danielnewman/2018/09/11/top-10-digital-
transformation-trends-for-2019/
[102] B. Nguyen, N. Choi, M. Thottan, and J. V. der Merwe, ‘‘SIMECA:
SDN-based IoT mobile edge cloud architecture,’’ in Proc. IFIP/IEEE
Symp. Integr. Netw. Service Manage. (IM), May 2017, pp. 503–509.
KAYA KURU received the B.Sc. degree from
Turkish National Defence University, the major
(ADP) degree in computer engineering from
Middle East Technical University (METU),
the M.B.A. degree from Selcuk University, and the
M.Sc. and Ph.D. degrees in computer science from
METU. He has completed his postdoctoral studies
with the School of Electronics and Computer
Science, University of Southampton, U.K. He has
been awarded the academic degree of Associate
Professor in computer-information systems engineering by the Council of
Higher Education, Turkey. He has worked with the IT Department in a
university as a DBA, a SW developer, the SW Development Manager, and
the IT Manager, for 15 years. His research interests include development of
embedded autonomous and hybrid intelligent systems using AI, ML, DL,
and image processing techniques within the cyber physical domains on the
cloud platform. He is currently teaching Advanced Mechatronics Systems,
Advanced Topics in 4IR, Intelligent Machines, Electromechanical Systems,
Intelligent Systems Design and Development, and Further Engineering
Mathematics. Recently, he obtained an i4i Product Development Grant
funded by NIHR.
HALIL YETGIN received the B.Eng. degree in
computer engineering from Selcuk University,
Turkey, in 2008, the M.Sc. degree in wireless com-
munications from the University of Southampton,
U.K., in 2010, and the Ph.D. degree in wireless
communications from the Next Generation Wire-
less Research Group, University of Southampton,
in 2015. He is currently an Assistant Professor
with the Department of Electrical and Electron-
ics Engineering, Bitlis Eren University, Turkey.
His research interests include the development of intelligent communica-
tion systems, energy efficient cross-layer design, resource allocation of the
future wireless communication networks, UAV communication networks,
and underwater wireless sensor networks. He was a recipient of the full schol-
arship granted by the Republic of Turkey, Ministry of National Education.
VOLUME 7, 2019 41415
... Within the articles selected during the SLR process, it is clear that in Industry 4.0, manufacturing moves towards agility and mass-customization. For example, agent-based manufacturing is proposed, which divides the manufacturing system into multiple departments, such as cloud-controlled suggestion, product, machining and conveying agents as discussed by Tang et al., and Kuru et al., [64,65]. As can be expected, the customization adds a layer of complexity as manufacturing and supply chain face rapid product changes and disturbances as a result. ...
... Moreover, CPS is often coupled with cloud computing to allow realtime monitoring of the manufacturing process [20]. With CPS, the main issues identified are integration of IT/OT due to lack of experts [44,65]. ...
Article
Full-text available
Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.
... The aim in Advanced Mechatronics Systems (AMSs) development is to produce high-quality intelligent autonomous products and maintain a competitive edge through better product performance by forging effective sensing, self-learning, self-optimisation, self-configuration, self-diagnosis, and precise autonomous decision making and actuation. This is performed with no or less human intervention using effective location-independent monitoring, control, and management applications with products [21]. With the advanced wireless communication techniques and improved battery technologies, AMSs are capable of becoming independent and working with other massive AMSs to construct robust, customisable, energy-efficient, autonomous, intelligent, and immersive platforms [21]. ...
... This is performed with no or less human intervention using effective location-independent monitoring, control, and management applications with products [21]. With the advanced wireless communication techniques and improved battery technologies, AMSs are capable of becoming independent and working with other massive AMSs to construct robust, customisable, energy-efficient, autonomous, intelligent, and immersive platforms [21]. Miniaturisation of components and consequently devices using MEMS (Micro-Electro-Mechanical Systems) technology is imperative for ergonomic and functional use. ...
Article
Full-text available
Post-void alarm systems to monitor bedwetting in nocturnal enuresis (NE) have been deemed unsatisfactory. The aim of this study is to develop a safe, comfortable and non-invasive pre-void wearable alarm and associated technologies using advanced mechatronics. Each stage of development includes patient and public involvement and engagement (PPI). The early stages of the development involved children with and without NE (and parents) who were tested at a hospital under the supervision of physicians, radiologists, psychologists, and nurses. The readings of the wearable device were simultaneously compared with B-mode images and measurements, acquired from a conventional ultrasound device, and were found to correlate highly. The results showed that determining imminent voiding need is viable using non-invasive sensors. Following on from “proof of concept,” a bespoke advanced mechatronics device has been developed. The device houses custom electronics, an ultrasound system, intelligent software, a user-friendly smartphone application, bedside alarm box, and a dedicated undergarment, along with a self-adhesive gel pad—designed to keep the MEMS sensors aligned with the abdomen. Testing of the device with phantoms and volunteers has been successful in determining bladder volume and associated voiding need. Five miniaturised, and therefore more ergonomic, versions of the device are being developed, with an enabled connection to the cloud platform for location independent control and monitoring. Thereafter, the enhanced device will be tested with children with NE at their homes for 14 weeks, to gain feedback relating to wearability and data collection involving the cloud platform. Design of the MyPAD advanced mechatronics system
... The mechanical advances (e.g., machine tools and power generators) intend to diminish or ideally eliminate the human's physical stress. On the other hand, electronics technology and IT intend to limit human mental strain [36]. In this sense, mechatronics can relieve humans from physical and mental strain in various aspects of life. ...
Article
Full-text available
Industry 4.0 has risen as an integrated digital manufacturing environment, and it has created a novel research perspective that has thrust research to interdisciplinarity and exploitation of ICT advances. This work presents and discusses the main aspects of Industry 4.0 and how intelligence can be embedded in manufacturing to create the smart factory. It briefly describes the main components of Industry 4.0, and it focuses on the security challenges that the fully interconnected ecosystem of Industry 4.0 has to meet and the threats for each component. Preserving security has a crucial role in Industry 4.0, and it is vital for its existence, so the main research directions on how to ensure the confidentiality and integrity of the information shared among the Industry 4.0 components are presented. Another view is in light of the security issues that come as a result of enabling new technologies.
... It is composed of the fields of mechanical engineering, electronics and electrical systems for information processing by using intelligent computer control elements (IT technology). These systems find application in various production processes and operations and should help to improve and/or alter the performance and functionality of mechanical systems by integrating sensors and electronic processing [19,28]. ...
Article
Full-text available
The digital revolution is changing the world. Robots, big data and artificial intelligence are the key technologies of the future and the basis of important innovations for the future development of the economy and society. In companies, this fact requires strategic rethinking and adjustments in ever-shorter time cycles. The creation of an agile and collaborative production to achieve the goals is often a basic requirement. With adaptation to technical progress, requirements and goals change continuously. To be and remain competitive, companies are forced to have at least the same technological standard as their competitors. In order to meet these challenges today, the use of highly efficient mechatronic systems such as robots is necessary. The paper analyses business ethics relevant aspects of robotics by using a survey with 88 respondents.
... There are several reference models or frameworks devoted to optimize production [5]. For instance, in [6], a service-oriented cyber-physical Advanced Mechatronics System (AMS) was presented along with its current and future challenges. These AMS frameworks show the transformation from mechanical systems to mechatronic systems to advanced mechatronics systems, their interaction with the environment, and other AMSs considering the smart domains and their applications to provide new solutions. ...
Article
Full-text available
This paper presents the design and development of an IoT device, called MEIoT weather station, which combines the Educational Mechatronics and IoT to develop the required knowledge and skills for Industry 4.0. MEIoT weather station connects to the internet, measures eight weather variables, and upload the sensed data to the cloud. The MEIoT weather station is the first device working with the IoT architecture of the National Digital Observatory of Intelligent Environments. In addition, an IoT open platform, GUI-MEIoT, serves as a graphic user interface. GUI-MEIoT is used to visualize the real-time data of the weather variables, it also shows the historical data collected, and allows to export them to a csv file. Finally, an OBNiSE architecture application to Engineering Education is presented with a dynamic system case of study that includes the instructional design carried out within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work main contribution to the state of art is the design and integration of the OBNiSE architecture within the EMCF offering the possibility to add more IoT devices for several smart domains such as smart campus, smart cities, smart people and smart industries.
Article
Full-text available
Industry 4.0 (I4.0) is the fourth industrial revolution and a synonym for intelligent manufacturing. It drives the convergence of several cutting-edge technologies to provoke autonomous, fully integrated, collaborated, highly automated, and customized industries. Edge Computing (EC), a highly distributed framework, emerged a couple of years ago and embraced the industry to leverage the benefit of low latency and near real-time performance. It brings computation and storage in the close proximity of end devices and reduces the cloud overhead. In addition to improved operational efficiency, storage, and latency, EC further reduces the cost, improves productivity with higher quality maintenance and customer satisfaction. At the digital-to-digital stage of the Physical-Digital-Physical (PDP) loop, adapting EC can furnish tremendous benefits and further accelerate the next stages of the loop. This survey identifies the past and present works oriented towards Intelligent Manufacturing integrated with the EC platform and categorizes the research based on architecture, intelligence platform, edge objectives, and application. Herein, the authors have incorporated; (1) The progress in I4.0 following the PDP loop; (2) The discussion on EC in I4.0 and their Research Trend; (3) Methods to bring intelligence to the edge. To the best of our knowledge, it is the first review article that focuses on the applications and objectives of EC in Intelligent Manufacturing. It also outlines the optimum solutions to bring intelligence to the edge by overcoming the resource and complexity-bound with accuracy and latency constraints for the decision-making processes. Future directions include the less explored research areas, challenges in edge deployment in industries, and the integration of trending technologies such as Blockchain, Software Defined Networking, and 5 G with EC to excite the EC researchers. A few collaborative edge scenarios are discussed for the promotion and application of EC in I4.0. Nevertheless, efficient edge deployments face many challenges since studies are still limited to conceptual levels or design steps, and future orientation to application strategies for Smart Manufacturing is required.
Chapter
This work presents the results of an analysis of the main expected potential problems that may occur in the implementation of the Industry 4.0 reform. It is proved that the pace and level of development of this reform will largely be determined by the effectiveness of the used mechatronic systems. It has also been established that as a result of systematic miniaturization of the nodes of radio-electronic equipment and microelectronic equipment and microelectronic technology, the main problem of these reforms and the implementation of complex technological processes is instrumental support, especially cutting micro-tools. Therefore, the examples of these micro-tools show methods for improving their performance characteristics.
Article
The recent advances in the cyber–physical domains, cloud and edge platforms along with the advanced communication technologies play a crucial role in connecting the globe more than ever, which is creating large volumes of data at astonishing rates and a tsunami of computation within hyper-connectivity. Data analytic tools are evolving rapidly to harvest these explosive increasing data volumes. Deriving meaningful insights from voluminous geo-distributed data of all kinds as a strategic asset is fuelling the innovation, facilitating e-commerce and revolutionizing the industry and businesses in the transition from digital to the intelligent way of doing business. In this perspective, in this study, a philosophical industrial and technological direction involving Deep Insight-as-a-Service (DINSaaS) on Forged Cloud Platforms (FCP) along with Advanced Insight Analytics (AIA), primarily motivated by the global benefit is systematically analysed within sophisticated theoretical knowledge, and consequently, a conceptual geo-distributed framework is proposed to (1) guide the national/international leading organizations, governments, cloud service providers and leading companies in order to establish a scalable framework within the hyperscale geo-distributed infrastructure in which exponentially increasing voluminous Big Data (BD) can be harvested effectively and efficiently, (2) inspire the transformation of BD into wiser abstract formats in Specialized Insight Domains (SID), (3) provide fusion and networking of insights rather than BD in order to obtain globally generated distributed intelligence and help make better decisions and near-real-time predictions, in particular for time-critical latency-sensitive applications, and (4) direct all the stakeholders to rivet the high-quality products and services within Automation of Everything (AoE) by exploiting continuously created and updated insights in dedicated taxonomic SID within large-scale geo-distributed datacenters.
Article
Full-text available
Surface electromyography (sEMG) signal plays an important role in hand function recovery training. In this study, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband, machine learning algorithms, and a 3D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The smart wearable armband was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy (CA) poor performance can be mitigated. A new method was put forward to find the optimal feature set. Machine learning (ML) algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms (CCEAs) and principal components analysis (PCA). According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, the user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.
Article
Full-text available
Heterogeneous network (HetNet) is a key enabler to largely boost network coverage and capacity in the forthcoming fifth-generation (5G) and beyond. To support the explosively growing mobile data volumes, wireless communications with millimeter-wave (mm-wave) radios have attracted massive attention, which is widely considered as a promising candidate in 5G HetNets. In this article, we give an overview on the end-to-end latency of HetNets with mm-wave communications. In general, it is rather challenging for formulating and optimizing the delay problem with buffers in mm-wave communications, since conventional graph-based network optimization techniques are not applicable when queues are considered. Toward this end, we develop an adaptive low-latency strategy, which uses cooperative networking to reduce the end-to-end latency. Then, we evaluate the performance of the introduced strategy. Results reveal the importance of proper cooperative networking in reducing the end-to-end latency. In addition, we have identified several challenges in future research for low-latency mm-wave HetNets.
Conference Paper
Full-text available
The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the large-scale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent. An open source software (Python SQLite) situated on Linux operating system is used to create the edge database and the dictionary so that inter-platform portability will be achieved and most IIoT machines will be able to use the platform. Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal significant differences between generated machine signal and reference signal. This database-dictionary approach is a new paradigm since it is different from legacy methods in which databases are situated only in the cloud with huge memory and servers. The open source deployment will also help to satisfy the criteria of Industrial IoT Consortium and the Open Fog Architecture.
Article
Full-text available
The Internet-of-Things (IoT) has taken over the business spectrum, and its applications vary widely from agriculture and health care to transportation. A hospital environment can be very stressful, especially for senior citizens and children. With the ever-increasing world population, the conventional patient-doctor appointment has lost its effectiveness. Hence, smart health care becomes very important. Smart health care can be implemented at all levels, starting from temperature monitoring for babies to tracking vital signs in the elderly.
Article
Full-text available
This article presents a unified computing, caching, and communication (3C) solution for the upcoming 5G environment that will allow service, content, and function providers to deploy their services/ content/functions near the end users (EUs); to allow network providers to virtually deploy their connectivity services over commodity hardware; and to enable end users to renounce their role as passive 5G stakeholders and become active ones by offering their 3C resources to the 5G ecosystem. In this direction, we foresee the exploitation of a peer-topeer- like middleware/app solution that upon installation will enhance the end user devices with the ability to form virtual fogs capable of providing their 3C resources to the 5G ecosystem. Additionally, we propose the introduction of heterogeneous nodes (e.g., FPGAs and GPUs) at the networks edge, which will boost the processing capabilities without paying a premium in power consumption. This will enable efficient and thorough filtering of the information that makes it all the way up to the cloud. In summary, this article proposes an architecture that exploits and advances the edge and extreme edge 3C paradigms toward enabling the 5G ecosystem to meet its own criterion for low end-to-end latencies and, as such, enable it to provide and sustain high QoS/QoE levels.
Article
The hype concerning digitalization is increasing the demand for new generations of automation systems. Concepts like Reference Architecture Model Industry 4.0 (RAMI 4.0) give us models but do not tell us how to facilitate actual implementations. This article discusses the transition from legacy automation technology as defined by ISA-95 to highly distributed Internet of Things (IoT)-and system of systems (SoS)-based automation systems that fully utilize Internet technologies, thus enabling the implementation of Industry 4.0 and RAMI 4.0 models. Distributed IoT automation systems have a number of general requirements concerning real-time performance, security, engineering cost, scalability, and interoperability. Meeting these requirements is necessary to enable possibilities for a realworld implementation of IoT automation. A key concept is local automation clouds. The discussion is based on a particular example of such an automation integration platform, the Arrowhead Framework.
Article
Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed firstly, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the Overall Equipment Effectiveness (OEE) of the equipment is significantly improved.
Article
The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge”, near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, etc.) in edge computing and traditional cloud computing architectures.