Novel framework for downsizing the massive data in internet of things using artificial intelligence

Abstract

The increasing demands of large-scale network system towards data acquisition and control from multiple sources has led to the proliferated adoption of internet of things (IoT) that is further witnessed with massive generation of voluminous data. Review of literature showcases the scope and problems associated with data compression approaches towards massive scale of heterogeneous data management in IoT. Therefore, the proposed study addresses this problem by introducing a novel computational framework that is capable of downsizing the data by harnessing the potential problem-solving characteristic of artificial intelligence (AI). The scheme is presented in form of triple-layered architecture considering layer with IoT devices, fog layer, and distributed cloud storage layer. The mechanism of downsizing is carried out using deep learning approach to predict the probability of data to be downsized. The quantified outcome of study shows significant data downsizing performance with higher predictive accuracy.