From Cloud Down to Things: An Overview of Machine Learning in Internet of Things

Abstract

Due to the large number of things and information devices, not all IoT applications can be satisfied by processing data in the cloud. Due to the cloud's constrained ability to process and share data, edge computing, or the act of initiating IoT edge data processing and connected devices' transformation from intelligent devices to gadgets, was developed. Machine learning is the key instrument. It is important to include information inference as a continuum in the cloud-to-things approach. Reviewing machine functions that are connected to the Internet, from the cloud all the way down to embedded devices. Many uses for machines learning to handle application data management and processing responsibilities are examined. The most current machine learning apps for IoT are gathered, and they all agree on their feedback and application space. The type of data, the machine learning methods used, and the locations belong to the continuum from clouds to objects. The issues and future directions of IoT machine learning research are spoken about. Additionally, employing methods for categorization using machine learning, papers on “machine” learning in IoT are meticulously retrieved and reviewed. Next, with the expansion of recognized subjects and application domains, difficulties and search are moving in the direction of effective machine learning for the IoT. In addition, articles on the IoT's “machine” learning are painstakingly retrieved, then classified using machine learning methods.