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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 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 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
The primary idea behind deploying sensor networks is to utilize the
distributed sensing capability provided by tiny, low powered and
low cost devices.Multiple sensing devices can be used cooperatively
and collaboratively to capture events or monitor space more
effectively than a single sensing device. Most of the applications
envisioned with sensor networks demand highly reliable, accurate
and fault-tolerant data acquisition process.The integrity of data
alone can have tremendous effects on the performance of any data
acquisition system.Due to the low manufacturing cost, the sensors
lend themselves to be deployed in large numbers with a high spatial
distribution.Such a large deployment scheme often generates
enormous amount of data that needs to be efficiently summarized and
delivered for analysis and processing. The primary aim of this book
is to provide innovative solutions to serveral problem domain -
efficient data acquisition, large data processing, decision making
and monitoring that all help in optimizing the performance of the
given sensor network in real-world deployment scenarios.
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