An Analytical Review Of Iot Based Machine Learning Techniques

Abstract

The development of internet-connected sensory devices, which deliver observations and datasets of the physical world, has facilitated rapid developments in hardware , software and communication technologies. It is projected that by 2020, between 25 billion and 50 billion will be used for the number of Internet-connected devices. The amount of data published will increase as these numbers rise and the technologies mature. The Internet of Things ( IoT) technology of internet-connected devices continues to extend the existing internet by linking the physical and cyber worlds. As well as growing length, the IoT produces broad data with various modalities and varying data quality, defined by its velocity in terms of time and position dependence. Smart data processing and analysis are the key to the development of intelligent IoT applications. This article assesses the different methods for machine learning which address the challenges posed by IoT data, considering the key use of smart cities. A taxonomy of machine learning algorithms describing how the various techniques are applied to the data to capture higher level knowledge is the main contribution of this study. There will also be discussion of the potential and difficulties of computer education in IoT data analysis.