Age of Information in Digital Twin Migration

A Digital Twin (DT) is a virtual representation of a real physical system (PS) that interacts with other objects on its behalf. In these interactions, the Age of Information (AoI) is a key performance measure that is dependent on the DT's current network server placement. To maintain acceptable AoI performance as the system evolves, the DT location may have to be moved, which is referred to as Digital Twin migration. In this paper we consider the problem of DT migration in a vehicular system, focusing on minimizing the time-averaged AoI. In this type of system, it is difficult to maintain acceptable AoI performance due to the speed of the vehicles, which can result in frequent abrupt handoffs between different cellular domains. This makes the question of when to initiate DT migration an important one. The problem is formulated as a Markovian stopping problem and an optimal online algorithm is proposed using dynamic programming and the statistics of vehicular motion. A more computationally intensive adaptive version of this algorithm is also proposed where the dynamic programming tables are recomputed at each time step. A best-in-expectation algorithm is introduced that gives sub-optimal AoI performance but is more computationally efficient than in the optimal version. These algorithms are also compared to heuristics that do immediate migration and migration at handoff. An offline algorithm is formulated that provides a lower bound on the average AoI that is attainable. Performance results show that the proposed algorithm can significantly improve the efficiency of Digital Twin migrations compared to the other approaches while guaranteeing the minimized time-averaged AoI.