國立虎尾科技大學 |

Robustness Optimization for IoT Topology

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robustness Optimization for IoT Topology/ by Tie Qiu, Ning Chen, Songwei Zhang.
作者:	Qiu, Tie.
其他作者:	Chen, Ning.
面頁冊數:	XIV, 214 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer networks . -
電子資源:	https://doi.org/10.1007/978-981-16-9609-1
ISBN:	9789811696091

Robustness Optimization for IoT Topology
Qiu, Tie.

Robustness Optimization for IoT Topology[electronic resource] /by Tie Qiu, Ning Chen, Songwei Zhang. - 1st ed. 2022. - XIV, 214 p. 1 illus.online resource.

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

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

1365720
Computer networks .

LC Class. No.: TK5105.5-5105.9

Dewey Class. No.: 004.6

Robustness Optimization for IoT Topology
LDR:03111nam a22003975i 4500 001 1087266
003 DE-He213
005 20220610230111.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811696091 $9 978-981-16-9609-1
024 7 $a 10.1007/978-981-16-9609-1 $2 doi
035 $a 978-981-16-9609-1
050 4 $a TK5105.5-5105.9
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 004.6 $2 23
100 1 $a Qiu, Tie. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1201865
245 1 0 $a Robustness Optimization for IoT Topology $h [electronic resource] / $c by Tie Qiu, Ning Chen, Songwei Zhang.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a XIV, 214 p. 1 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
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.
650 0 $a Computer networks . $3 1365720
650 0 $a Artificial intelligence. $3 559380
650 0 $a Electronic digital computers—Evaluation. $3 1365864
650 1 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a System Performance and Evaluation. $3 669346
700 1 $a Chen, Ning. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 815297
700 1 $a Zhang, Songwei. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1394277
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811696084
776 0 8 $i Printed edition: $z 9789811696107
776 0 8 $i Printed edition: $z 9789811696114
856 4 0 $u https://doi.org/10.1007/978-981-16-9609-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)