Efficient Offloading in UAV-MEC IoT Networks: Leveraging Digital Twins and Energy Harvesting

Digital twin technology leverages a real-time simulated environment to optimize unmanned aerial vehicles (UAVs)–mobile edge computing (MEC) networks. Considering unpredictable MEC environments and low-power Internet of things (IoT) devices, this paper proposes 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 the maximum number of IoT devices a UAV can serve are considered. To solve the formulated problem, we employ a branch and bound algorithm to obtain optimal results. Additionally, we propose a relaxed heuristic algorithm to solve the problem with reduced computational complexity. This approach provides efficient alternatives to obtain near-optimal solutions. Through simulations, we demonstrate the effectiveness of the heuristic algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.