On-Demand Efficient Clustering for Next Generation IoT Applications: A Hybrid NN Approach

The internet-of-things(IoT) extends the traditional Internet and realizes the interconnection of all things in a smart way. With the rapid development of 5G and beyond communication technology, the number of users and demands for IoT applications has increased significantly along with resource constraints networking in the communication systems. The next generation IoT applications challenges these issue using smarter cooperative communication with large heterogeneous clusters along in par with traditional IoT systems. The proposed work illustrates a hybrid neural network (NN) model for dynamic clustering for efficient next generation IoT applications. A dynamic cluster model based on hybrid NN optimization is proposed along with the Gaussian copula technique for realizing the correlation between the clusters for efficient cooperative communication. The mathematical analysis and simulation results show that the model maximizes the amount of information in the cluster and balances the resource allocation among nodes to improve the life of the entire network.

Automated summary

The Internet of Things (IoT) extends the traditional Internet by connecting all things in a smart way. Next-generation IoT applications challenge resource-constrained networks and traditional IoT systems through smarter cooperative communication.