Open AccessA Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications
Author(s) -
R. Valanarasu
Publication year - 2021
Publication title -
iro journal on sustainable wireless systems
Language(s) - English
Resource type - Journals
ISSN - 2582-3167
DOI - 10.36548/jsws.2021.3.001
Subject(s) - automation , computer science , internet of things , reliability (semiconductor) , the internet , artificial intelligence , quality (philosophy) , data science , machine learning , world wide web , engineering , mechanical engineering , power (physics) , philosophy , physics , epistemology , quantum mechanics
Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.