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This book presents the technologies that empower edge intelligence,
along with their use in novel IoT solutions. Specifically, it
presents how 5G/6G, Edge AI, and Blockchain solutions enable novel
IoT-based decentralized intelligence use cases at the edge of the
cloud/edge/IoT continuum. Emphasis is placed on presenting how
these technologies support a wide array of functional and
non-functional requirements spanning latency, performance,
cybersecurity, data protection, real-time performance, energy
efficiency, and more. The various chapters of the book are
contributed by several EU-funded projects, which have recently
developed novel IoT platforms that enable the development and
deployment of edge intelligence applications based on the
cloud/edge paradigm. Each one of the projects employs its own
approach and uses a different mix of networking, middleware, and
IoT technologies. Therefore, each of the chapters of the book
contributes a unique perspective on the capabilities of enabling
technologies and their integration in practical real-life
applications in different sectors. The book is structured in five
distinct parts. Each one of the first four parts focuses on a
specific set of enabling technologies for edge intelligence and
smart IoT applications in the cloud/edge/IoT continuum.
Furthermore, the fifth part provides information about
complementary aspects of next-generation IoT technology, including
information about business models and IoT skills. Specifically: The
first part focuses on 5G/6G networking technologies and their roles
in implementing edge intelligence applications. The second part
presents IoT applications that employ machine learning and other
forms of Artificial Intelligence at the edge of the network. The
third part illustrates decentralized IoT applications based on
distributed ledger technologies. The fourth part is devoted to the
presentation of novel IoT applications and use cases spanning the
cloud/edge/IoT continuum. The fifth part discusses complementary
aspects of IoT technologies, including business models and digital
skills.
In today's competitive global environment, manufacturers are
offered with unprecedented opportunities to build hyper-efficient
and highly flexible plants, towards meeting variable market demand,
while at the same time supporting new production models such as
make-to-order (MTO), configure-to-order (CTO) and engineer-to-order
(ETO). During the last couple of years, the digital transformation
of industrial processes is propelled by the emergence and rise of
the fourth industrial revolution (Industry 4.0). The latter is
based on the extensive deployment of Cyber-Physical Production
Systems (CPPS) and Industrial Internet of Things (IIoT)
technologies in the manufacturing shopfloor, as well as on the
seamless and timely exchange of digital information across supply
chain participants. The benefits of Industry 4.0 have been already
proven in the scope of pilot and production deployments in a number
of different use cases including flexibility in automation,
predictive maintenance, zero defect manufacturing and more. Despite
early implementations and proof-of-concepts, CPPS/IIoT deployments
are still in their infancy for a number of reasons, including: *
Manufacturers' poor awareness about digital manufacturing solutions
and their business value potential, as well as the lack of relevant
internal CPPS/IIoT knowledge. * The high costs that are associated
with the deployment, maintenance and operation of CPPS systems in
the manufacturing shopfloors, which are particularly challenging in
the case of SME (Small Medium Enterprises) manufacturers that lack
the equity capital needed to invest in Industry 4.0. * The time
needed to implement CPPS/IIoT and the lack of a smooth and proven
migration path from existing OT solutions. * The uncertainty over
the business benefits and impacts of IIoT and CPPS technologies,
including the lack of proven methods for the techno-economic
evaluation of Industry 4.0 systems. * Manufacturers' increased
reliance on external integrators, consultants and vendors. * The
absence of a well-developed value chain needed to sustain the
acceptance of these new technologies for digital automation. In
order to alleviate these challenges, three European Commission
funded projects (namely H2020 FAR-EDGE (http://www.far-edge.eu/),
H2020 DAEDALUS (http://daedalus.iec61499.eu) and H2020 AUTOWARE
(http://www.autoware-eu.org/)) have recently joined forces towards
a "Digital Shopfloor Alliance". The Alliance aims at providing
leading edge and standards based digital automation solutions,
along with guidelines and blueprints for their effective
deployment, validation and evaluation. The present book provides a
comprehensive description of some of the most representative
solutions that offered by these three projects, along with the ways
these solutions can be combined in order to achieve multiplier
effects and maximize the benefits of their use. The presented
solutions include standards-based digital automation solutions,
following different deployment paradigms, such as cloud and edge
computing systems. Moreover, they also comprise a rich set of
digital simulation solutions, which are explored in conjunction
with the H2020 MAYA project (http://www.maya-euproject.com/). The
latter facilitate the testing and evaluation of what-if scenarios
at low risk and cost, but also without disrupting shopfloor
operations. As already outlined, beyond leading edge scientific and
technological development solutions, the book comprises a rich set
of complementary assets that are indispensable to the successful
adoption of IIoT/CPPS in the shopfloor. The book is structured in
three parts as follows: * The first part of the book is devoted to
digital automation platforms. Following an introduction to Industry
4.0 in general and digital automation platforms in particular, this
part presents the digital automation platforms of the FAR-EDGE,
AUTOWARE and DAEDALUS projects. * The second part of the book
focuses on the presentation of digital simulation and digital
twins' functionalities. These include information about the models
that underpin digital twins, as well as the simulators that enable
experimentation with these processes over these digital models. *
The third part of the book provides information about complementary
assets and supporting services that boost the adoption of digital
automation functionalities in the Industry 4.0 era. Training
services, migration services and ecosystem building services are
discussed based on the results of the three projects of the Digital
Shopfloor Alliance. The target audience of the book includes: *
Researchers in the areas of Digital Manufacturing and more
specifically in the areas of digital automation and simulation, who
wish to be updated about latest Industry 4.0 developments in these
areas. * Manufacturers, with an interest in the next generation of
digital automation solutions based on Cyber-Physical systems. *
Practitioners and providers of Industrial IoT solutions, which are
interested in the implementation of use cases in automation,
simulation and supply chain management. * Managers wishing to
understand technologies and solutions that underpin Industry 4.0,
along with representative applications in the shopfloor and across
the supply chain.
Internet-of-Things (IoT) Analytics are an integral element of most
IoT applications, as it provides the means to extract knowledge,
drive actuation services and optimize decision making. IoT
analytics will be a major contributor to IoT business value in the
coming years, as it will enable organizations to process and fully
leverage large amounts of IoT data, which are nowadays largely
underutilized. The Building Blocks of IoT Analytics is devoted to
the presentation the main technology building blocks that comprise
advanced IoT analytics systems. It introduces IoT analytics as a
special case of BigData analytics and accordingly presents leading
edge technologies that can be deployed in order to successfully
confront the main challenges of IoT analytics applications. Special
emphasis is paid in the presentation of technologies for IoT
streaming and semantic interoperability across diverse IoT streams.
Furthermore, the role of cloud computing and BigData technologies
in IoT analytics are presented, along with practical tools for
implementing, deploying and operating non-trivial IoT applications.
Along with the main building blocks of IoT analytics systems and
applications, the book presents a series of practical applications,
which illustrate the use of these technologies in the scope of
pragmatic applications. Technical topics discussed in the book
include: Cloud Computing and BigData for IoT analytics Searching
the Internet of Things Development Tools for IoT Analytics
Applications IoT Analytics-as-a-Service Semantic Modelling and
Reasoning for IoT Analytics IoT analytics for Smart Buildings IoT
analytics for Smart Cities Operationalization of IoT analytics
Ethical aspects of IoT analytics This book contains both research
oriented and applied articles on IoT analytics, including several
articles reflecting work undertaken in the scope of recent European
Commission funded projects in the scope of the FP7 and H2020
programmes. These articles present results of these projects on IoT
analytics platforms and applications. Even though several articles
have been contributed by different authors, they are structured in
a well thought order that facilitates the reader either to follow
the evolution of the book or to focus on specific topics depending
on his/her background and interest in IoT and IoT analytics
technologies. The compilation of these articles in this edited
volume has been largely motivated by the close collaboration of the
co-authors in the scope of working groups and IoT events organized
by the Internet-of-Things Research Cluster (IERC), which is
currently a part of EU's Alliance for Internet of Things Innovation
(AIOTI).
This open access book presents how cutting-edge digital
technologies like Big Data, Machine Learning, Artificial
Intelligence (AI), and Blockchain are set to disrupt the financial
sector. The book illustrates how recent advances in these
technologies facilitate banks, FinTech, and financial institutions
to collect, process, analyze, and fully leverage the very large
amounts of data that are nowadays produced and exchanged in the
sector. To this end, the book also describes some more the most
popular Big Data, AI and Blockchain applications in the sector,
including novel applications in the areas of Know Your Customer
(KYC), Personalized Wealth Management and Asset Management,
Portfolio Risk Assessment, as well as variety of novel Usage-based
Insurance applications based on Internet-of-Things data. Most of
the presented applications have been developed, deployed and
validated in real-life digital finance settings in the context of
the European Commission funded INFINITECH project, which is a
flagship innovation initiative for Big Data and AI in digital
finance. This book is ideal for researchers and practitioners in
Big Data, AI, banking and digital finance.
This open access book presents how cutting-edge digital
technologies like Big Data, Machine Learning, Artificial
Intelligence (AI), and Blockchain are set to disrupt the financial
sector. The book illustrates how recent advances in these
technologies facilitate banks, FinTech, and financial institutions
to collect, process, analyze, and fully leverage the very large
amounts of data that are nowadays produced and exchanged in the
sector. To this end, the book also describes some more the most
popular Big Data, AI and Blockchain applications in the sector,
including novel applications in the areas of Know Your Customer
(KYC), Personalized Wealth Management and Asset Management,
Portfolio Risk Assessment, as well as variety of novel Usage-based
Insurance applications based on Internet-of-Things data. Most of
the presented applications have been developed, deployed and
validated in real-life digital finance settings in the context of
the European Commission funded INFINITECH project, which is a
flagship innovation initiative for Big Data and AI in digital
finance. This book is ideal for researchers and practitioners in
Big Data, AI, banking and digital finance.
In this first part of the INFINITECH book series, which is a series
of three books, the principles of the modern economy that lead to
make the modern financial sector and the FinTech’s the most
disruptive areas in today’s global economy are discussed.
INFINITECH envision many opportunities emerging for activating new
channels of innovation in the local and global scale while at the
same time catapult opportunities for more disruptive user-centric
services. At the same time, INFINITECH is the result of a sharing
vision from a representative global group of experts, providing a
common vision and identifying impacts in the financial and
insurance sectors.
In this second part of the INFINITECH book series, which is a
series of three books, the basic concepts for Fintech referring to
the diversity in the use of technology to underpin the delivery of
financial services are reviewed. The demand and the supply side in
16 the financial sector are demonstrated, and thus further discuss
why Fintech is the focus if industry nowadays and the meaning for
waves of digitization. Financial technology (FinTech) and insurance
technology (InsuranceTech) are rapidly transforming the financial
and insurance services industry. An overview of Reference
Architecture (RA) for BigData, IoT and AI applications in the
financial and insurance sectors (INFINITECH-RA) are also provided.
Moreover, the book reviews the concept of innovation and its
application in INFINITECH, and innovative technologies provided by
the project for financial sector practical examples.
This third and final part of the INFINITECH book series begins by
providing a definition for Fintech, namely: the use of technology
to underpin the delivery of financial services. The book further
discusses why Fintech is the focus of industry nowadays as the
waves of digitization and the way financial technology (FinTech)
and insurance technology (InsuranceTech) are rapidly transforming
the financial and insurance services industry. The book also
introduces technology assets that follow the Reference Architecture
(RA) for BigData, IoT and AI applications. Moreover, the series of
assets includes the domain area where applications from the
INFINITECH innovation project and the concept of innovation for
financial sector are described. Further described is the INFINITECH
Marketplace and its components including details of available
assets, as well as a description of solutions developed in
INFINITECH.
Modern critical infrastructures comprise of many interconnected
cyber and physical assets, and as such are large scale
cyber-physical systems. Hence, the conventional approach of
securing these infrastructures by addressing cyber security and
physical security separately is no longer effective. Rather more
integrated approaches that address the security of cyber and
physical assets at the same time are required. This book presents
integrated (i.e. cyber and physical) security approaches and
technologies for the critical infrastructures that underpin our
societies. Specifically, it introduces advanced techniques for
threat detection, risk assessment and security information sharing,
based on leading edge technologies like machine learning, security
knowledge modelling, IoT security and distributed ledger
infrastructures. Likewise, it presets how established security
technologies like Security Information and Event Management (SIEM),
pen-testing, vulnerability assessment and security data analytics
can be used in the context of integrated Critical Infrastructure
Protection. The novel methods and techniques of the book are
exemplified in case studies involving critical infrastructures in
four industrial sectors, namely finance, healthcare, energy and
communications. The peculiarities of critical infrastructure
protection in each one of these sectors is discussed and addressed
based on sector-specific solutions. The advent of the fourth
industrial revolution (Industry 4.0) is expected to increase the
cyber-physical nature of critical infrastructures as well as their
interconnection in the scope of sectorial and cross-sector value
chains. Therefore, the demand for solutions that foster the
interplay between cyber and physical security, and enable
Cyber-Physical Threat Intelligence is likely to explode. In this
book, we have shed light on the structure of such integrated
security systems, as well as on the technologies that will underpin
their operation. We hope that Security and Critical Infrastructure
Protection stakeholders will find the book useful when planning
their future security strategies.
In recent years, the rising complexity of Internet of Things (IoT)
systems has increased their potential vulnerabilities and
introduced new cybersecurity challenges. In this context, state of
the art methods and technologies for security risk assessment have
prominent limitations when it comes to large scale, cyber-physical
and interconnected IoT systems. Risk assessments for modern IoT
systems must be frequent, dynamic and driven by knowledge about
both cyber and physical assets. Furthermore, they should be more
proactive, more automated, and able to leverage information shared
across IoT value chains. This book introduces a set of novel risk
assessment techniques and their role in the IoT Security risk
management process. Specifically, it presents architectures and
platforms for end-to-end security, including their implementation
based on the edge/fog computing paradigm. It also highlights
machine learning techniques that boost the automation and
proactiveness of IoT security risk assessments. Furthermore,
blockchain solutions for open and transparent sharing of IoT
security information across the supply chain are introduced.
Frameworks for privacy awareness, along with technical measures
that enable privacy risk assessment and boost GDPR compliance are
also presented. Likewise, the book illustrates novel solutions for
security certification of IoT systems, along with techniques for
IoT security interoperability.In the coming years, IoT security
will be a challenging, yet very exciting journey for IoT
stakeholders, including security experts, consultants, security
research organizations and IoT solution providers. The book
provides knowledge and insights about where we stand on this
journey. It also attempts to develop a vision for the future and to
help readers start their IoT Security efforts on the right foot.
Modern critical infrastructures can be considered as large scale
Cyber Physical Systems (CPS). Therefore, when designing,
implementing, and operating systems for Critical Infrastructure
Protection (CIP), the boundaries between physical security and
cybersecurity are blurred. Emerging systems for Critical
Infrastructures Security and Protection must therefore consider
integrated approaches that emphasize the interplay between
cybersecurity and physical security techniques. Hence, there is a
need for a new type of integrated security intelligence i.e.,
Cyber-Physical Threat Intelligence (CPTI).This book presents novel
solutions for integrated Cyber-Physical Threat Intelligence for
infrastructures in various sectors, such as Industrial Sites and
Plants, Air Transport, Gas, Healthcare, and Finance. The solutions
rely on novel methods and technologies, such as integrated
modelling for cyber-physical systems, novel reliance indicators,
and data driven approaches including BigData analytics and
Artificial Intelligence (AI). Some of the presented approaches are
sector agnostic i.e., applicable to different sectors with a fair
customization effort. Nevertheless, the book presents also peculiar
challenges of specific sectors and how they can be addressed. The
presented solutions consider the European policy context for
Security, Cyber security, and Critical Infrastructure protection,
as laid out by the European Commission (EC) to support its Member
States to protect and ensure the resilience of their critical
infrastructures. Most of the co-authors and contributors are from
European Research and Technology Organizations, as well as from
European Critical Infrastructure Operators. Hence, the presented
solutions respect the European approach to CIP, as reflected in the
pillars of the European policy framework. The latter includes for
example the Directive on security of network and information
systems (NIS Directive), the Directive on protecting European
Critical Infrastructures, the General Data Protection Regulation
(GDPR), and the Cybersecurity Act Regulation. The sector specific
solutions that are described in the book have been developed and
validated in the scope of several European Commission (EC)
co-funded projects on Critical Infrastructure Protection (CIP),
which focus on the listed sectors. Overall, the book illustrates a
rich set of systems, technologies, and applications that critical
infrastructure operators could consult to shape their future
strategies. It also provides a catalogue of CPTI case studies in
different sectors, which could be useful for security consultants
and practitioners as well.
The successful deployment of AI solutions in manufacturing
environments hinges on their security, safety and reliability which
becomes more challenging in settings where multiple AI systems
(e.g., industrial robots, robotic cells, Deep Neural Networks
(DNNs)) interact as atomic systems and with humans. To guarantee
the safe and reliable operation of AI systems in the shopfloor,
there is a need to address many challenges in the scope of complex,
heterogeneous, dynamic and unpredictable environments.
Specifically, data reliability, human machine interaction,
security, transparency and explainability challenges need to be
addressed at the same time. Recent advances in AI research (e.g.,
in deep neural networks security and explainable AI (XAI) systems),
coupled with novel research outcomes in the formal specification
and verification of AI systems provide a sound basis for safe and
reliable AI deployments in production lines. Moreover, the legal
and regulatory dimension of safe and reliable AI solutions in
production lines must be considered as well.To address some of the
above listed challenges, fifteen European Organizations collaborate
in the scope of the STAR project, a research initiative funded by
the European Commission in the scope of its H2020 program (Grant
Agreement Number: 956573). STAR researches, develops, and validates
novel technologies that enable AI systems to acquire knowledge in
order to take timely and safe decisions in dynamic and
unpredictable environments. Moreover, the project researches and
delivers approaches that enable AI systems to confront
sophisticated adversaries and to remain robust against security
attacks.This book is co-authored by the STAR consortium members and
provides a review of technologies, techniques and systems for
trusted, ethical, and secure AI in manufacturing. The different
chapters of the book cover systems and technologies for industrial
data reliability, responsible and transparent artificial
intelligence systems, human centered manufacturing systems such as
human-centred digital twins, cyber-defence in AI systems, simulated
reality systems, human robot collaboration systems, as well as
automated mobile robots for manufacturing environments. A variety
of cutting-edge AI technologies are employed by these systems
including deep neural networks, reinforcement learning systems, and
explainable artificial intelligence systems. Furthermore, relevant
standards and applicable regulations are discussed. Beyond
reviewing state of the art standards and technologies, the book
illustrates how the STAR research goes beyond the state of the art,
towards enabling and showcasing human-centred technologies in
production lines. Emphasis is put on dynamic human in the loop
scenarios, where ethical, transparent, and trusted AI systems
co-exist with human workers. The book is made available as an open
access publication, which could make it broadly and freely
available to the AI and smart manufacturing communities.
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