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| Title | A Comprehensive Survey Of Machine Learning Techniques In Next-Generation Wireless Networks And The Internet Of Things |
|---|---|
| ID_Doc | 975 |
| Authors | Khan M.A.A.; Kaidi H.M. |
| Year | 2023 |
| Published | Ingenierie des Systemes d'Information, 28, 4 |
| DOI | http://dx.doi.org/10.18280/isi.280416 |
| Abstract | 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. © 2023 International Information and Engineering Technology Association. All rights reserved. |
| Author Keywords | 5G and beyond; deep learning; Internet of Things (IoT); Machine Learning (ML); next-generation wireless networks; quality of service; reinforcement learning |
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