Digital Twin Model Selection for Feature Accuracy

Digital twins (DTs) can be used to represent the behavior of real physical systems (PSs) in their interaction with other objects. Each DT periodically communicates with its PS and uses these updates to implement features that reflect the real behavior of the PS. A given feature can be implemented using different models that create the feature with differing levels of system accuracy. In this article, we study the DT model selection problem, where the DTs of multiple PSs are hosted at an execution server (ES). The objective is to maximize the minimum feature accuracy for the requested features by making appropriate model selections subject to the synchronization and ES execution constraints. The model selection problem is first formulated as an NP-complete integer program. It is then decomposed into multiple subproblems, each consisting of a modified Knapsack problem. A polynomial-time approximation algorithm is proposed using dynamic programming to solve it efficiently, by violating its constraints by at most a given factor. A generalization of the model selection problem is then given and an approximation algorithm using relaxation and dependent rounding is proposed to solve the problem efficiently with guaranteed constraint violations. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions.