| Abstract |
The rapid development of emerging technologies, such as digital twin (DT), has added momentum towards the development of smart cities. Along with the flourishing of DT, reality DT is an important member. However, reality DT model needs to be updated periodically so that the physical entity can be maintained efficiently. Since some physical entities may not be able to upload the entire status information actively, we propose to use unmanned ground vehicle (UGV) as an information collector to assist in updating the reality DTs. Furthermore, considering the large amount of updating data, we leverage mobile edge computing (MEC) technology to collaboratively process the data. In addition, we also propose the concept age of digital twins (AoDT) as a metric to quantify the freshness of DT model. Thus, an AoDT minimization problem is established, which jointly optimizes offloading decisions, UGV waypoints selections, and target points' visiting orders. Considering the difficulty of the problem, we propose a low-complexity iterative algorithm to solve it. Finally, simulation results show that the proposed algorithm can effectively reduce the AoDT, comparing to the benchmark algorithms. © 2024 IEEE. |
