IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techni...

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Main Authors: Junaid Qadir, Kok-Lim Alvin Yau, Muhammad Ali Imran, Qiang Ni, Athanasios V. Vasilakos
Format: Article
Language:English
Published: IEEE 2015-01-01
Series:IEEE Access
Online Access:https://ieeexplore.ieee.org/document/7384563/
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spelling doaj-dc1c1eae4f6c4734ba980793d72510dc2021-03-29T19:35:51ZengIEEEIEEE Access2169-35362015-01-0133079308210.1109/ACCESS.2015.25077987384563IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKINGJunaid QadirKok-Lim Alvin YauMuhammad Ali ImranQiang NiAthanasios V. VasilakosWith today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).https://ieeexplore.ieee.org/document/7384563/
collection DOAJ
language English
format Article
sources DOAJ
author Junaid Qadir
Kok-Lim Alvin Yau
Muhammad Ali Imran
Qiang Ni
Athanasios V. Vasilakos
spellingShingle Junaid Qadir
Kok-Lim Alvin Yau
Muhammad Ali Imran
Qiang Ni
Athanasios V. Vasilakos
IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
IEEE Access
author_facet Junaid Qadir
Kok-Lim Alvin Yau
Muhammad Ali Imran
Qiang Ni
Athanasios V. Vasilakos
author_sort Junaid Qadir
title IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
title_short IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
title_full IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
title_fullStr IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
title_full_unstemmed IEEE ACCESS SPECIAL SECTION EDITORIAL: ARTIFICIAL INTELLIGENCE ENABLED NETWORKING
title_sort ieee access special section editorial: artificial intelligence enabled networking
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2015-01-01
description With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS).
url https://ieeexplore.ieee.org/document/7384563/
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