Internet of things information analysis using fusion based learning with deep neural network

Abstract

The development of the Internet of Things (IoT) is driven by recent technical advances and a growing melding of fields including sensing and actuating technology, integrated networks, radio and data processing. The vast number of IoT sensors produce high volume data for a wide variety of applications, such as intelligent house, intelligent health, intelligent engineering, smart transport, intelligent grid and intelligent agriculture. In order to promote improved decision-making, efficiency and precision, reviewing these data is a vital mechanism that renders IoT a successful market concept and paradigm-enhancing quality of living. While it is promising to improve the quality of our lives to obtain covered knowledge and inferences from IoT data, this is a complex task which traditional paradigms cannot carry out. Deep learning can play a key role in developing smarter IoT, as it has demonstrated promising effects in a number of areas such as image recognition, knowledge gathering, voice recognition, natural language processing, indoor location, physiological and psychological condition identification, etc. In this context it becomes imperative to explore the capacity of Deep Learning for IoT data processing.