Advancing intelligent networks through distributed optimization:
The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smar...
Gespeichert in:
| Weitere Verfasser: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania
IGI Global
2024
|
| Schriftenreihe: | Advances in computer and electrical engineering (ACEE) book series
|
| Schlagworte: | |
| Online-Zugang: | DE-91 DE-1050 Volltext |
| Zusammenfassung: | The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire device's learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems' optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities. |
| Beschreibung: | 1 Online-Ressource (623 Seiten) |
| DOI: | 10.4018/979-8-3693-3739-4 |
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| institution | BVB |
| language | English |
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| spelling | Advancing intelligent networks through distributed optimization S. Suman Rajest, Salvatore Moccia, Bhopendra Singh, R. Regin, Joseph Jeganathan, editors Hershey, Pennsylvania IGI Global 2024 1 Online-Ressource (623 Seiten) txt rdacontent c rdamedia cr rdacarrier Advances in computer and electrical engineering (ACEE) book series The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire device's learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems' optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities. Artificial intelligence Cloud computing Computer networks Management Cyberinfrastructure Internet of things Jeganathan, Joseph edt Moccia, Salvatore 1968- edt Rajest, S. Suman 1988- edt Regin, R 1985- edt Singh, Bhopendra 1974- edt Erscheint auch als Druck-Ausgabe 9798369337394 https://doi.org/10.4018/979-8-3693-3739-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Advancing intelligent networks through distributed optimization Artificial intelligence Cloud computing Computer networks Management Cyberinfrastructure Internet of things |
| title | Advancing intelligent networks through distributed optimization |
| title_auth | Advancing intelligent networks through distributed optimization |
| title_exact_search | Advancing intelligent networks through distributed optimization |
| title_full | Advancing intelligent networks through distributed optimization S. Suman Rajest, Salvatore Moccia, Bhopendra Singh, R. Regin, Joseph Jeganathan, editors |
| title_fullStr | Advancing intelligent networks through distributed optimization S. Suman Rajest, Salvatore Moccia, Bhopendra Singh, R. Regin, Joseph Jeganathan, editors |
| title_full_unstemmed | Advancing intelligent networks through distributed optimization S. Suman Rajest, Salvatore Moccia, Bhopendra Singh, R. Regin, Joseph Jeganathan, editors |
| title_short | Advancing intelligent networks through distributed optimization |
| title_sort | advancing intelligent networks through distributed optimization |
| topic | Artificial intelligence Cloud computing Computer networks Management Cyberinfrastructure Internet of things |
| topic_facet | Artificial intelligence Cloud computing Computer networks Management Cyberinfrastructure Internet of things |
| url | https://doi.org/10.4018/979-8-3693-3739-4 |
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