Digital-Twin-Assisted Task Offloading in UAV-MEC Networks With Energy Harvesting for IoT Devices

We investigate digital twin-assisted task offloading in unmanned-aerial-vehicle (UAV)-mobile edge computing (UAV-MEC) networks with energy harvesting. Digital twin technology leverages a real-time simulated environment to optimize UAV-MEC networks. Considering unpredictable mobile edge computing (MEC) environments and low-power Internet of Things (IoT) devices, we propose a digital twin-assisted task offloading scheme in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and IoT device association. The constraints on computing, caching, energy harvesting, latency, and maximum number of IoT devices an UAV can serve are considered. To solve the formulated problem, we employ a branch-and-bound algorithm to obtain optimal results. We also solve the optimization problem using the relaxed heuristic algorithm. In addition, we propose a difference of convex penalty-based algorithm to solve the problem with reduced computational complexity. This approach provide efficient alternatives to obtain near-optimal solution. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.