Abstract
Unmanned Aerial Vehicles (UAVs) have been an attractive device to serve target tracking scenarios, such as hit- and-run tracking and border patrol. Nonetheless, it is difficult to implement real-time UAV topology optimization due to communication resources of UAVs and random moving speeds of targets. To address the problem, we propose a Digital Twins-assisted topology optimization framework (TopoDT). We formulate a UAV topology optimization model based on Lyapunov theory in the framework. The model is decoupled into two subproblems using our proposed TopoDT topology optimization algorithm. The DT model can allow UAVs to implement neighbor selection to construct and optimize small-scale local topologies for tracking low-speed moving targets. In addition, it allows UAVs to construct large-scale global topologies for tracking high-speed moving targets based on trajectory derivation. The system simulation results demonstrate that our solution reduces the end-to-end latency by 63.0% while decreasing the hop counts by 50% compared to state-of-the-art benchmarks.