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This book paves the way for researchers working on the sustainable
interdependent networks spread over the fields of computer science,
electrical engineering, and smart infrastructures. It provides the
readers with a comprehensive insight to understand an in-depth big
picture of smart cities as a thorough example of interdependent
large-scale networks in both theory and application aspects. The
contributors specify the importance and position of the
interdependent networks in the context of developing the
sustainable smart cities and provide a comprehensive investigation
of recently developed optimization methods for large-scale
networks. There has been an emerging concern regarding the optimal
operation of power and transportation networks. In the second
volume of Sustainable Interdependent Networks book, we focus on the
interdependencies of these two networks, optimization methods to
deal with the computational complexity of them, and their role in
future smart cities. We further investigate other networks, such as
communication networks, that indirectly affect the operation of
power and transportation networks. Our reliance on these networks
as global platforms for sustainable development has led to the need
for developing novel means to deal with arising issues. The
considerable scale of such networks, due to the large number of
buses in smart power grids and the increasing number of electric
vehicles in transportation networks, brings a large variety of
computational complexity and optimization challenges. Although the
independent optimization of these networks lead to locally optimum
operation points, there is an exigent need to move towards
obtaining the globally-optimum operation point of such networks
while satisfying the constraints of each network properly. The book
is suitable for senior undergraduate students, graduate students
interested in research in multidisciplinary areas related to future
sustainable networks, and the researchers working in the related
areas. It also covers the application of interdependent networks
which makes it a perfect source of study for audience out of
academia to obtain a general insight of interdependent networks.
This book provides a thorough treatment of privacy and security
issues for researchers in the fields of smart grids, engineering,
and computer science. It presents comprehensive insight to
understanding the big picture of privacy and security challenges in
both physical and information aspects of smart grids. The authors
utilize an advanced interdisciplinary approach to address the
existing security and privacy issues and propose legitimate
countermeasures for each of them in the standpoint of both
computing and electrical engineering. The proposed methods are
theoretically proofed by mathematical tools and illustrated by
real-world examples.
This book focuses on the theory and application of interdependent
networks. The contributors consider the influential networks
including power and energy networks, transportation networks, and
social networks. The first part of the book provides the next
generation sustainability framework as well as a comprehensive
introduction of smart cities with special emphasis on energy,
communication, data analytics and transportation. The second part
offers solutions to performance and security challenges of
developing interdependent networks in terms of networked control
systems, scalable computation platforms, and dynamic social
networks. The third part examines the role of electric vehicles in
the future of sustainable interdependent networks. The fourth and
last part of this volume addresses the promises of control and
management techniques for the future power grids.
This book provides a thorough treatment of privacy and security
issues for researchers in the fields of smart grids, engineering,
and computer science. It presents comprehensive insight to
understanding the big picture of privacy and security challenges in
both physical and information aspects of smart grids. The authors
utilize an advanced interdisciplinary approach to address the
existing security and privacy issues and propose legitimate
countermeasures for each of them in the standpoint of both
computing and electrical engineering. The proposed methods are
theoretically proofed by mathematical tools and illustrated by
real-world examples.
This is the only book to cover infrastructure aspects of sensor
networks in a comprehensive fashion. The only other books on sensor
networks do not cover this topic or do so only superficially as
part of a less-focussed multi-authored treatment.
This textbook presents multiple facets of design, development and
deployment of deep learning networks for both students and industry
practitioners. It introduces a deep learning tool set with deep
learning concepts interwoven to enhance understanding. It also
presents the design and technical aspects of programming along with
a practical way to understand the relationships between programming
and technology for a variety of applications. It offers a tutorial
for the reader to learn wide-ranging conceptual modeling and
programming tools that animate deep learning applications. The book
is especially directed to students taking senior level
undergraduate courses and to industry practitioners interested in
learning about and applying deep learning methods to practical
real-world problems.Â
This book studies mathematical theories of machine learning. The
first part of the book explores the optimality and adaptivity of
choosing step sizes of gradient descent for escaping strict saddle
points in non-convex optimization problems. In the second part, the
authors propose algorithms to find local minima in nonconvex
optimization and to obtain global minima in some degree from the
Newton Second Law without friction. In the third part, the authors
study the problem of subspace clustering with noisy and missing
data, which is a problem well-motivated by practical applications
data subject to stochastic Gaussian noise and/or incomplete data
with uniformly missing entries. In the last part, the authors
introduce an novel VAR model with Elastic-Net regularization and
its equivalent Bayesian model allowing for both a stable sparsity
and a group selection.
This book studies mathematical theories of machine learning. The
first part of the book explores the optimality and adaptivity of
choosing step sizes of gradient descent for escaping strict saddle
points in non-convex optimization problems. In the second part, the
authors propose algorithms to find local minima in nonconvex
optimization and to obtain global minima in some degree from the
Newton Second Law without friction. In the third part, the authors
study the problem of subspace clustering with noisy and missing
data, which is a problem well-motivated by practical applications
data subject to stochastic Gaussian noise and/or incomplete data
with uniformly missing entries. In the last part, the authors
introduce an novel VAR model with Elastic-Net regularization and
its equivalent Bayesian model allowing for both a stable sparsity
and a group selection.
Advances in the miniaturization of microelectromechanical systems
have led to battery-powered sensor nodes that have sensing,
communication and p- cessingcapabilities.
Thesesensornodescanbenetworkedinanadhocmanner to perform
distributed sensing and information processing. Such ad hoc s- sor
networks provide greater fault tolerance and sensing accuracy and
are typically less expensive compared to the alternative of using
only a few large isolated sensors. These networks can also be
deployed in inhospitable terrains or in hostile environments to
provide continuous monitoring and processing capabilities. A
typical sensor networkapplication is inventorytracking in
factorywa- houses. A single sensor node can be attached to each
item in the warehouse. These sensor nodes can then be used for
tracking the location of the items as they are moved within the
warehouse. They can also provide information on the location of
nearby items as well as the history of movement of various items.
Once deployed, the sensor network needs very little human interv-
tion and can function autonomously. Another typical application of
sensor networks lies in military situations. Sensor nodes can be
air-dropped behind enemy lines or in inhospitable terrain. These
nodes can self-organize th- selves and provide unattended
monitoring of the deployed area by gathering information about
enemy defenses and equipment, movement of troops, and areas of
troop concentration. They can then relay this information back to a
friendly base station for further processing and decision making.
Sensor nodes are typically characterizedby small form-factor,
limited b- tery power, and a small amount of memory
This book presents an overview on security and privacy issues in
dynamic sensor networks and Internet of Things (IoT) networks and
provides a novel tamper evident technique to counter and defend
against these security related issues. The mission of this book is
to explain the evolution of techniques and strategies in securing
information transfer and storage thus facilitating a digital
transition towards the modern tamper evident systems. The goal is
also to aid business organizations that are dependent on the
analysis of the large volumes of generated data in securing and
addressing the associated growing threat of attackers relentlessly
waging attacks and the challenges in protecting the
confidentiality, integrity and provenance of data. The book also
provides a comprehensive insight into the secure communication
techniques and tools that have evolved and the impact they have had
in supporting and flourishing the business through the cyber era.
This book also includes chapters that discuss the most primitive
encryption schemes to the most recent use of homomorphism in
ensuring the privacy of the data thus leveraging greater use of new
technologies like cloud computing and others.
This book provides the basics needed to develop sensor network
software and supplements it with many case studies covering network
applications. It also examines how to develop onboard applications
on individual sensors, how to interconnect these sensors, and how
to form networks of sensors, although the major aim of this book is
to provide foundational principles of developing sensor networking
software and critically examine sensor network applications.
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