Adaptive Multi-UAV Trajectory Planning Leveraging Digital Twin Technology for Urban IIoT Applications

In this paper, flying mobile computing is considered to serve terrestrial Intelligent Internet-of-Things (IIoT) in a dynamic scenario. Existing work mainly trains the trajectory model on board, leading to UAVs' endurance reduction due to excessive energy consumption for training models. And the dynamic change and characteristics of requirements have not been considered while training trajectory. Therefore, to save energy and improve the efficiency of UAVs, we use the replication and prediction ability of DT to assist the UAV in planning the optimal trajectory, and propose an Incremental and Distributed Update (IDU) mode combined with DT to optimize its energy consumption. To cope with dynamic change of requirements, a Self-adaptive Trajectory Decision (STD) scheme is proposed, which uses the DT to plan different ranks of trajectories according to the prediction result to cope with the dynamic requirements. UAVs just need to receive this trajectory model and make a simple trajectory selection according to the real-time scenario. To plan the optimal trajectory by DT, we consider using the Dueling DQN with Prioritized Experience Replay (PER) function to train while considering the characteristics of requirements. Simulation results demonstrate the effectiveness of optimization for the DT, the STD scheme can cope with different changes in requirements and each trajectory is optimal for the corresponding scenario.

I. Introduction

With the development of Internet-of-Things and communication technology, the number of advanced Intelligent Internet of Things (IIoT) is growing exponentially [1]. These IIoTs are deployed anywhere to collect data and generate latency-/computing-sensitive requirements. In order to meet this kind of requirements, some progressive technologies for 6G network have been proposed, such as Microserver-based Edge Computing (MEC), Cellular Vehicle-to-Everything (C-V2X), and UAV-assisted Terrestrial Mobile Computing (TMC) [2], etc. The similarity between these technologies is to deploy the micro server closer to users or IIoTs compare with the traditional central computation mode. These micro servers can help users or IIoTs to compute some tasks and return results with low latency, this advantage can facilitate the Quality of Service (QoS) significantly. Compared to MEC and C-V2X, UAV-assisted TMC draws the most attention due to the advantage of mobility and Line of Sight (LoS) communication [3].