Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

Abstract

Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long short-term memory architecture is used during the development of the proposed trusted model. The applicability of the proposed model is validated by comparing its performance with the generic back-propagation neural network. This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model. The obtained results reflect the usefulness and applicability of such an approach and the competitiveness against other existing ones.