Springer, 2022. — 223 p. — ISBN: 981169608X.
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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