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A new era of complexity science is emerging, in which nature- and
bio-inspired principles are being applied to provide solutions. At
the same time, the complexity of systems is increasing due to such
models like the Internet of Things (IoT) and fog computing. Will
complexity science, applying the principles of nature, be able to
tackle the challenges posed by highly complex networked systems?
Bio-Inspired Optimization in Fog and Edge Computing: Principles,
Algorithms, and Systems is an attempt to answer this question. It
presents innovative, bio-inspired solutions for fog and edge
computing and highlights the role of machine learning and
informatics. Nature- or biological-inspired techniques are
successful tools to understand and analyze a collective behavior.
As this book demonstrates, algorithms, and mechanisms of
self-organization of complex natural systems have been used to
solve optimization problems, particularly in complex systems that
are adaptive, ever-evolving, and distributed in nature. The
chapters look at ways of enhancingto enhance the performance of fog
networks in real-world applications using nature-based optimization
techniques. They discuss challenges and provide solutions to the
concerns of security, privacy, and power consumption in cloud data
center nodes and fog computing networks. The book also examines
how: The existing fog and edge architecture is used to provide
solutions to future challenges. A geographical information system
(GIS) can be used with fog computing to help users in an urban
region access prime healthcare. An optimization framework helps in
cloud resource management. Fog computing can improve the quality,
quantity, long-term viability, and cost-effectiveness in
agricultural production. Virtualization can support fog computing,
increase resources to be allocated, and be applied to different
network layers. The combination of fog computing and IoT or cloud
computing can help healthcare workers predict and analyze diseases
in patients.
Unique selling point: Combines theory with practice and
applications for advanced intelligent healthcare informatics Core
audience: Researchers and academics in healthcare informatics and
machine learning Place in the market: Reference work
This book discusses the evolution of security and privacy issues
and brings related technological tools, techniques, and solutions
into one single source. The book will take readers on a journey to
understanding the security issues and possible solutions involving
various threats, attacks, and defense mechanisms, which include
IoT, cloud computing, Big Data, lightweight cryptography for
blockchain, and data-intensive techniques, and how it can be
applied to various applications for general and specific use.
Graduate and postgraduate students, researchers, and those working
in this industry will find this book easy to understand and use for
security applications and privacy issues.
Data has cemented itself as a building block of daily life.
However, surrounding oneself with great quantities of information
heightens risks to one's personal privacy. Additionally, the
presence of massive amounts of information prompts researchers into
how best to handle and disseminate it. Research is necessary to
understand how to cope with the current technological requirements.
Large-Scale Data Streaming, Processing, and Blockchain Security is
a collection of innovative research that explores the latest
methodologies, modeling, and simulations for coping with the
generation and management of large-scale data in both scientific
and individual applications. Featuring coverage on a wide range of
topics including security models, internet of things, and
collaborative filtering, this book is ideally designed for
entrepreneurs, security analysts, IT consultants, security
professionals, programmers, computer technicians, data scientists,
technology developers, engineers, researchers, academicians, and
students.
Data has cemented itself as a building block of daily life.
However, surrounding oneself with great quantities of information
heightens risks to one's personal privacy. Additionally, the
presence of massive amounts of information prompts researchers into
how best to handle and disseminate it. Research is necessary to
understand how to cope with the current technological requirements.
Large-Scale Data Streaming, Processing, and Blockchain Security is
a collection of innovative research that explores the latest
methodologies, modeling, and simulations for coping with the
generation and management of large-scale data in both scientific
and individual applications. Featuring coverage on a wide range of
topics including security models, internet of things, and
collaborative filtering, this book is ideally designed for
entrepreneurs, security analysts, IT consultants, security
professionals, programmers, computer technicians, data scientists,
technology developers, engineers, researchers, academicians, and
students.
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