Machine learning and data analytics for the IoT
This work addresses challenges in IoT data processing for machine learning, but it appears incremental as it builds on existing reviews and proposes a framework without demonstrating new SOTA results.
The paper tackles the problem of barriers in IoT infrastructures and protocols that prevent intelligent IoT applications from adaptively learning from each other, by proposing a framework to enable such adaptive learning and presenting a case study based on real literature.
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.