Improving Efficiency in IoT Data Streaming Through the Integration of Machine Learning: A Review

Abstract

Cloud Internet of Things as a term has emerged recently, it’s a combination of Cloud Computing and IoT (Internet of things) services with their combined features to solve the problem of collection and storage of massive data streaming. there are a lot of basic ordinary solutions to solve an IoT’s bottlenecks and defiances but it is still not enough to make the devices perfect and the smartest. To arrive at this level of perfection, should use the data collected from each environment to build the ideal smarter decision system through the integration of Machine Learning methods with big data technologies and use this system to improve the efficiency of IoT devices. and of course, this will take time to become our reality. But the Internet of Things now is one big resource of massive data collection and also a most common field in the 21st century in research, which helps to develop and improve it quickly and gives us a small vision of the future. The research objective of this work is to explain and discuss the bottlenecks and challenges faced by Internet of Things devices during real-time data streaming to the cloud platforms and study how can Machine Learning algorithms be utilized to optimize the performance of IoT devices and face the challenges and weaknesses of the survey of literature review and research of other researchers over recent 7 years.