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A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.

The advent of next-generation wireless networks and the Internet of Things (IoT) has introduced numerous challenges in terms of quality of service (QoS), user data rates, throughput, and security. These challenges necessitate innovative solutions to optimize performance and ensure robust security. M...

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Main Authors: Alam Khan, Mohammad Aftab, Mad Kaidi,, Hazilah
Format: Article
Language:English
Published: International Information and Engineering Technology Association 2023
Subjects:
T Technology (General)
T58.6-58.62 Management information systems
Online Access:http://eprints.utm.my/105066/1/HazilahMadKhaidi2023_AComprehensiveSurveyofMachineLearningTechniques.pdf
http://eprints.utm.my/105066/
http://dx.doi.org/10.18280/isi.280416
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spelling my.utm.1050662024-04-02T06:47:22Z http://eprints.utm.my/105066/ A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things. Alam Khan, Mohammad Aftab Mad Kaidi,, Hazilah T Technology (General) T58.6-58.62 Management information systems The advent of next-generation wireless networks and the Internet of Things (IoT) has introduced numerous challenges in terms of quality of service (QoS), user data rates, throughput, and security. These challenges necessitate innovative solutions to optimize performance and ensure robust security. Machine Learning (ML) has emerged as an influential tool in this regard, offering the potential to fully harness the capabilities of next-generation wireless networks and the IoT. With an ever-increasing number of connected devices and the commensurate data proliferation, ML presents an effective means of analyzing and processing this data. One significant challenge addressed by ML is network optimization. Through the analysis of network traffic patterns, congestion points are identified, and potential network performance issues are predicted. Security, a critical concern in next-generation wireless networks and the IoT, is another facet where ML proves instrumental by detecting and mitigating security breaches. This is achieved by analyzing data to identify anomalous behaviour and potential threats. Moreover, ML facilitates informed decision-making in IoT systems. By scrutinizing real-time data generated by IoT devices, ML algorithms reveal valuable insights, trends, and correlations. This capability enables IoT-enabled systems to make data-driven decisions, thus enhancing the efficiency of various applications such as smart cities, industrial automation, healthcare, and environmental monitoring. This study undertakes a systematic review of the impact of ML techniques, such as reinforcement learning, deep learning, transfer learning, and federated learning, on next-generation wireless networks, placing a particular emphasis on the IoT. The literature is reviewed systematically and studies are categorized based on their implications. The aim is to highlight potential challenges and opportunities, providing a roadmap for researchers and scholars to explore new approaches, overcome challenges, and leverage potential opportunities in the future. International Information and Engineering Technology Association 2023-08 Article PeerReviewed application/pdf en http://eprints.utm.my/105066/1/HazilahMadKhaidi2023_AComprehensiveSurveyofMachineLearningTechniques.pdf Alam Khan, Mohammad Aftab and Mad Kaidi,, Hazilah (2023) A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things. Ingenierie des Systemes d'Information, 28 (4). pp. 959-967. ISSN 1633-1311 http://dx.doi.org/10.18280/isi.280416 DOI: 10.18280/isi.280416
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
T58.6-58.62 Management information systems
spellingShingle T Technology (General)
T58.6-58.62 Management information systems
Alam Khan, Mohammad Aftab
Mad Kaidi,, Hazilah
A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
description The advent of next-generation wireless networks and the Internet of Things (IoT) has introduced numerous challenges in terms of quality of service (QoS), user data rates, throughput, and security. These challenges necessitate innovative solutions to optimize performance and ensure robust security. Machine Learning (ML) has emerged as an influential tool in this regard, offering the potential to fully harness the capabilities of next-generation wireless networks and the IoT. With an ever-increasing number of connected devices and the commensurate data proliferation, ML presents an effective means of analyzing and processing this data. One significant challenge addressed by ML is network optimization. Through the analysis of network traffic patterns, congestion points are identified, and potential network performance issues are predicted. Security, a critical concern in next-generation wireless networks and the IoT, is another facet where ML proves instrumental by detecting and mitigating security breaches. This is achieved by analyzing data to identify anomalous behaviour and potential threats. Moreover, ML facilitates informed decision-making in IoT systems. By scrutinizing real-time data generated by IoT devices, ML algorithms reveal valuable insights, trends, and correlations. This capability enables IoT-enabled systems to make data-driven decisions, thus enhancing the efficiency of various applications such as smart cities, industrial automation, healthcare, and environmental monitoring. This study undertakes a systematic review of the impact of ML techniques, such as reinforcement learning, deep learning, transfer learning, and federated learning, on next-generation wireless networks, placing a particular emphasis on the IoT. The literature is reviewed systematically and studies are categorized based on their implications. The aim is to highlight potential challenges and opportunities, providing a roadmap for researchers and scholars to explore new approaches, overcome challenges, and leverage potential opportunities in the future.
format Article
author Alam Khan, Mohammad Aftab
Mad Kaidi,, Hazilah
author_facet Alam Khan, Mohammad Aftab
Mad Kaidi,, Hazilah
author_sort Alam Khan, Mohammad Aftab
title A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
title_short A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
title_full A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
title_fullStr A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
title_full_unstemmed A comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
title_sort comprehensive survey of machine learning techniques in next-generation wireless networks and the internet of things.
publisher International Information and Engineering Technology Association
publishDate 2023
url http://eprints.utm.my/105066/1/HazilahMadKhaidi2023_AComprehensiveSurveyofMachineLearningTechniques.pdf
http://eprints.utm.my/105066/
http://dx.doi.org/10.18280/isi.280416
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score 13.211869

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