Buch, Englisch, 214 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 512 g
ISBN: 978-981-16-9608-4
Verlag: Springer Nature Singapore
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1 Introduction
1.1 Context and motivation
1.2 Characteristics of IoT topology
1.3 Attack modes against network topology
1.4 Book organization
Chapter 2 Preliminaries of robustness optimization
2.1 Metrics of topology robustness
2.2 Related work
2.3 Existing challanges
Chapter 3 Robustness optimization based on self-organization3.1 Path planning based on the greedy principle
3.2 Construction of highly robust topology
3.3 Robust time synchronization scheme
Chapter 4 Evolution-based robustness optimization4.1 Robustness optimization scheme with multi-population co-evolution
4.2 An adaptive robustness evolution algorithm with self-competition
Chapter 5 Robustness optimization based on swarm intelligence
5.1 Topology optimization strategy with ant colony algorithm
5.2 Topology optimization strategy with particle swarm algorithm
Chapter 6 Robustness optimization based on multi-objective cooperation
6.1 Multi-objective optimization based on layered-cooperation
Chapter 7 Robustness optimization based on self-learning
7.1 Malicious node identification scheme based on gaussian mixture model
7.2 Highly robust topology learning model based on neural network
7.3 Highly robust topology generation strategy based on time series convolutional network
Chapter 8 Robustness optimization based on node self-learning
8.1 Node self-learning mechanism based on reinforcement learning
Chapter 9 Future research directions
9.1 Homogeneous networks
9.2 Heterogeneous networks
9.3 Smart IoT
