Robustness Optimization for IoT Topology (eBook)
XIV, 214 Seiten
Springer Nature Singapore (Verlag)
978-981-16-9609-1 (ISBN)
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.
Dr. Tie Qiu is currently a full professor in the School of Computer Science and Technology at Tianjin University, China. Prior to this, he was an assistant professor and associate professor in the School of Software at Dalian University of Technology. He was a visiting professor in the Department of Electrical and Computer Engineering at Iowa State University in the USA (2014-2015). He serves as an associate editor of IEEE Transactions on Network Science and Engineering (TNSE) and IEEE Transactions on Systems, Man, and Cybernetics: Systems; area editor of Ad Hoc Networks (Elsevier); associate editor of Computers and Electrical Engineering (Elsevier) and Human-centric Computing and Information Sciences (Springer); and guest editor of Future Generation Computer Systems. He serves as general chair, program chair, workshop chair, publicity chair, publication chair, and TPC member of a number of international conferences. He has authored/co-authored 9 books and over 150 scientific papers in international journals and conference proceedings, such as IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Industrial Informatics, IEEE Communications Surveys & Tutorials, IEEE Communications, INFOCOM, and GLOBECOM. His 10 papers are listed as ESI highly cited papers. He has contributed to the development of 4 copyrighted software systems and holds 16 patents. He is a distinguished member of the China Computer Federation (CCF) and a senior member of IEEE and ACM.
Ning Chen is a PhD candidate at Tianjin University. His research focuses on the Internet of Things, including robustness optimization, wireless sensor networks, artificial intelligence, big data analysis, smart city, and Internet of Vehicles. He has published more than 10 papers in leading journals, including two ESI highly cited papers.
Mr. Songwei Zhang is currently a technical engineer at Tianjin University. He has extensive experience in the robustness optimization of Internet of Things topology.
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.
| Erscheint lt. Verlag | 11.6.2022 |
|---|---|
| Zusatzinfo | XIV, 214 p. 1 illus. |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Informatik ► Weitere Themen ► Hardware | |
| Schlagworte | Artificial Intelligence • Evolutional Algorithm • internet of things • Robustness Optimization • Smart City |
| ISBN-10 | 981-16-9609-8 / 9811696098 |
| ISBN-13 | 978-981-16-9609-1 / 9789811696091 |
| Haben Sie eine Frage zum Produkt? |
PDF (Wasserzeichen)Größe: 13,4 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich
