The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

物联网（IoT）应用的数量激增，产生了智能数据处理所需的大量数据。但是，不断变化的物联网基础设施（即云，边缘，雾）和物联网应用层协议在发送/接收消息中的局限性成为创建智能物联网应用程序的障碍。这些障碍使当前的智能物联网应用无法自适应地向其他物联网应用学习。在本文中，我们严格审查了如何处理物联网生成的数据以进行机器学习分析，并重点介绍了在物联网环境中推进智能解决方案方面的当前挑战。此外，我们提出了一个框架，以使IoT应用程序能够自适应地向其他IoT应用程序学习，并就如何将该框架应用于文献中的实际研究提出了案例研究。最后，我们讨论了影响物联网未来智能应用的关键因素。