Machine Learning (ML) techniques have changed the analysis of massive data in the Internet of Things (IoT) environment very effectively. In the IoT theme of applications, reducing latency and energy consumption are the two crucial network Quality of Service (QoS) parameters and the most significant challenges because they directly impact the users’ experience. Enabling intelligence at the IoT fog computing framework with ML classifiers' help determines the computing requirements that, in turn, help to execute the vast data collected in the IoT fog computing for real-time operations efficiently. In this paper, the exploration of ML algorithms on the resource constraint IoT fog computing framework and the determination of the suitable ML classifier for reducing latency and energy levels with the usage of ambient sensors in the IoT theme are presented.

机器学习 (ML) 技术非常有效地改变了物联网 (IoT) 环境中海量数据的分析。在物联网应用主题中，降低延迟和能耗是两个关键的网络服务质量 (QoS) 参数，也是最重大的挑战，因为它们直接影响用户体验。在 ML 分类器的帮助下，在 IoT 雾计算框架中实现智能决定了计算需求，进而有助于高效执行 IoT 雾计算中收集的大量数据以进行实时操作。在本文中，