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The IoT topology defines the way various components communicate
with each other within a network. Topologies can vary
greatly in terms of security, power consumption,
cost, and complexity. Optimizing the IoT
topology for different applications and requirements
can help to boost the network’s performance and save
costs. More importantly, optimizing the topology robustness can
ensure security and prevent network
failure at the foundation level. In this context, this
book examines the optimization
schemes for topology robustness in the
IoT, helping readers to construct a robustness
optimization framework, from self-organizing to
intelligent networking. The book provides the relevant
theoretical framework and the latest empirical
research on robustness optimization of IoT
topology. Starting with the self-organization of
networks, it gradually moves to genetic evolution.
It also discusses the application of neural
networks and reinforcement learning to endow the
node with self-learning ability to allow intelligent
networking. This book is intended for students, practitioners,
industry professionals, and researchers who are eager to
comprehend the vulnerabilities of IoT topology. It helps them
to master the research framework for IoT topology
robustness optimization and to build more efficient and
reliable IoT topologies in their industry.
The IoT topology defines the way various components communicate
with each other within a network. Topologies can vary greatly in
terms of security, power consumption, cost, and complexity.
Optimizing the IoT topology for different applications and
requirements can help to boost the network's performance and save
costs. More importantly, optimizing the topology robustness can
ensure security and prevent network failure at the foundation
level. In this context, this book examines the optimization schemes
for topology robustness in the IoT, helping readers to construct a
robustness optimization framework, from self-organizing to
intelligent networking. The book provides the relevant theoretical
framework and the latest empirical research on robustness
optimization of IoT topology. Starting with the self-organization
of networks, it gradually moves to genetic evolution. It also
discusses the application of neural networks and reinforcement
learning to endow the node with self-learning ability to allow
intelligent networking. This book is intended for students,
practitioners, industry professionals, and researchers who are
eager to comprehend the vulnerabilities of IoT topology. It helps
them to master the research framework for IoT topology robustness
optimization and to build more efficient and reliable IoT
topologies in their industry.
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