Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks

Abstract

Digital twin (DT) technologies have emerged as a solution for realtime data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique characteristics and capabilities of a DT framework that enables realization of such promises as online learning of a physical environment, real-time monitoring of assets, Monte Carlo heuristic search for predictive prevention, on-policy, and off-policy reinforcement learning in real-time. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing and enabled by artificial intelligence (AI) services for modeling and decision-making processes. The DT framework separates the control functions, deployed as a system of logically centralized process, from the physical devices under control, much like software-defined networking (SDN) in fifth generation (5G) wireless networks. To clarify the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system, we discuss the application of implementing zero trust architecture (ZTA) as a necessary security framework in future data-driven communication networks.

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“…Common evaluation indexes in regression include mean absolute error (MAE), 103−106 meansquared error (MSE), 107−110 root-mean-squared error (RMSE), 111,112 and coefficient of determination (R 2 ). 69,113 Among these metrics, a better fit is indicated by a value closer to 0 for MAE, MSE, and RMSE and a value closer to 1 for R 2 .…”

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