Behavior Modeling-Based QoS Preserving Model With Intelligent Trust Ranking for 6G

In the generation-next WCN (Wireless communication Network), trust assessment is a key criterion for any node to enable QoS communication. Compared to previous versions, 6G is ready to serve a large number of users who are seeking a variety of novel use cases that are power-hungry. Hence, a power-optimized network is essential. For the QoS service within the network, the trustworthiness of nodes by assigning trust rank, particularly within the networks’ platforms for healthcare and defence, is crucial. In a 6G network, there will be a greater emphasis on providing users with a high-quality experience, including data rate and latency. Therefore, compared to previous generation networks, latency in a 6G network should be extremely low and data rate extremely high which can be achieved by providing trust ranking to the user. In this paper, an RS data set has been prepared using a trust ranking behavior model for all users presented in a proposed layer-based wireless network. Machine learning is used to provide trust ranking to all users, where different ways of communication are present in another layer. There are three trust rankings in the model. The maximum level of trust is shown by the third trust rank, while the least amount of trust is indicated by the first trust rank. The particular services provided to the appropriate node depend on the level of trust that has been acquired. The suggested SVM (Support Vector Machine) based prediction model preserves the three-way trade-off between QoS, accuracy, and processing time. The outcomes of the simulation show that potential consumers are intelligently identified using the suggested SVM-based trust-ranking approach for QoS network communication.