The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

中文翻译：

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B5G 网络中智能环境的机器学习：连接性和 QoS


通过互联网连接的物联网 (IoT) 设备的数量正在过度增长。物联网在动态性和不确定性方面的异质性和复杂性使这一情况急剧复杂化并引入了漏洞。需要对物联网进行智能管理，以在动态环境中保持连接、提高服务质量 (QoS) 并实时降低能耗。机器学习 (ML) 在智能应用程序的 QoS 增强、连接和配置方面发挥着关键作用。因此，本次调查的重点是使用机器学习来增强物联网应用。我们还深入概述了可以使用机器学习增强的各种物联网应用，例如智能城市、智能家居和智能医疗保健。对于每个应用程序，我们都会介绍使用 ML 的优势。最后，我们阐明了未来物联网研究的机器学习挑战，并根据现有工作回顾了当前的文献。