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Robustness optimization for IoT topology

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robustness optimization for IoT topologyby Tie Qiu, Ning Chen, Songwei Zhang.
Author:	Qiu, Tie.
other author:	Chen, Ning.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xiv, 214 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Internet of things.
Online resource:	https://doi.org/10.1007/978-981-16-9609-1
ISBN:	9789811696091$q(electronic bk.)

Robustness optimization for IoT topology
Qiu, Tie.

Robustness optimization for IoT topology[electronic resource] /by Tie Qiu, Ning Chen, Songwei Zhang. - Singapore :Springer Nature Singapore :2022. - xiv, 214 p. :ill., digital ;24 cm.

1.Introduction -- 2.Preliminaries of robustness optimization -- 3.Robustness optimization based on self-organization -- 4.Evolution-based robustness optimization -- 5.Robustness optimization based on swarm intelligence -- 6.Robustness optimization based on multi-objective cooperation -- 7.Robustness optimization based on self-learning -- 8.Robustness optimization based on node self-learning -- 9.Future research directions.

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.

ISBN: 9789811696091$q(electronic bk.)

Standard No.: 10.1007/978-981-16-9609-1doiSubjects--Topical Terms:

670954
Internet of things.

LC Class. No.: TK5105.8857 / .Q58 2022

Dewey Class. No.: 004.678

Robustness optimization for IoT topology
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520 $a 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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