Digital Twin Model Selection for Feature Accuracy in Wireless Edge Networks

Digital twins (DTs) are virtual implementations of real physical systems (PSs) that interact with other objects on their behalf. Each PS periodically communicates with its digital twin so that the state of the DT is always sufficiently current. Using these updates, a DT can provide features that represent the real behavior of its PS using models that yield differing levels of system accuracy. In this paper, we study the DT model selection problem in wireless networks where the DTs of multiple PSs are hosted at an edge server (ES). The accuracy obtained from a given model is a function of its required amount of PS input data, the updating frequency, and the amount of computational capacity needed at the ES. The objective is to maximize the minimum achieved accuracy among the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The problem is first formulated as an NP-complete integer program. The paper then uses relaxation and dependent rounding, and introduces a polynomial time approximation algorithm to obtain good solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solution.