| Autor: |
Menon S; School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India., Anand D; School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India., Kavita; Department of Computer Science and Engineering, Uttaranchal University, Dehradun 248007, India., Verma S; Department of Computer Science and Engineering, Uttaranchal University, Dehradun 248007, India., Kaur M; School of Computer Science and Engineering, Guru Gobind Singh College for Women, Chandigarh 160019, India., Jhanjhi NZ; School of Computer Science (SCS), Taylor's University, Subang Jaya 47500, Malaysia., Ghoniem RM; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia., Ray SK; School of Computer Science (SCS), Taylor's University, Subang Jaya 47500, Malaysia. |
| Abstrakt: |
With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings. |
