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Дата издания
03.02.2025Область исследования
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Аннотация (краткое описание)
Artificial intelligence (AI), machine learning (ML), and 6G networks may transform the Internet of Things. 6G networks provide minimal device latency and accommodate substantial data throughput. It also addresses network support issues inside the extensive IoT ecosystem. AI and ML enable 6G-enabled IoT platforms to allocate resources, comprehend context, and execute intelligent decisions for predictive maintenance and security. Nevertheless, energy efficiency, data confidentiality, and device interoperability pose challenges as systems become increasingly intricate and interconnected. This article advocates the systematic application of AI and ML to enhance the performance of 6G IoT networks. We illustrate how AI-driven network management may enhance connectivity, operational costs, and real-time decision-making in smart cities, healthcare, and industrial automation. This study investigates the role of federated learning and edge computing in addressing energy and privacy challenges within extensively distributed IoT systems. This paper presents a foundational reference model to inspire research that addresses gaps and integrates the global landscape, focusing on the dependable, efficient, and intelligent utilization of 6G-enabled IoT systemsDOI
10.1109/SATC65530.2025.11137150Тип публикаций
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Scopus
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AI and Machine learning for 6G-enabled IoT.pdf