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| 001 | 978-981-16-9609-1 | ||
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| 005 | 20240423125232.0 | ||
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_a10.1007/978-981-16-9609-1 _2doi |
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_aQiu, Tie. _eauthor. _0(orcid)0000-0003-2324-2523 _1https://orcid.org/0000-0003-2324-2523 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aRobustness Optimization for IoT Topology _h[electronic resource] / _cby Tie Qiu, Ning Chen, Songwei Zhang. |
| 250 | _a1st ed. 2022. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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| 300 |
_aXIV, 214 p. 1 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1.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 | _aThe 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 | _aComputer networks . | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 |
_aElectronic digital computers _xEvaluation. |
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| 650 | 1 | 4 | _aComputer Communication Networks. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aSystem Performance and Evaluation. |
| 700 | 1 |
_aChen, Ning. _eauthor. _0(orcid)0000-0001-6806-4287 _1https://orcid.org/0000-0001-6806-4287 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aZhang, Songwei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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_iPrinted edition: _z9789811696107 |
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_iPrinted edition: _z9789811696114 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-9609-1 |
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