Cybertwin-driven Resource Provisioning for IoE Applications at 6G-enabled Edge Networks

Abstract

Cybertwin leverages the capabilities of networks and serves in multiple functionalities, by identifying digital records of activities of humans and things, from the Internet of Everything (IoE) applications. Cybertwin emerges as a promising solution along with next-generation communication networks, i.e., 6G technology, however, it increases additional challenges at the edge networks. Motivated by the above-mentioned perspectives, in this paper, we introduce a new cybertwin-driven edge framework using 6G-enabled technology with an intelligent service provisioning strategy, for supporting a massive scale of IoE applications. The proposed strategy distributes the incoming tasks from IoE applications using the Deep Reinforcement Learning technique based on their dynamic service requirements. Besides that, an Artificial Intelligence-driven technique, i.e., the Support Vector Machines (SVM) classifier model is applied at the edge network to analyze the data and achieve high accuracy. The simulation results over the real-time financial datasets demonstrate the effectiveness of the proposed service provisioning strategy and SVM model over the baseline algorithms in terms of various performance metrics. The proposed strategy reduces the energy consumption by 15% over the baseline algorithms, while increasing the prediction accuracy by 12% over the classification models.