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.

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context of mobile edge computing (MEC) has garnered significant interest within the sector. However, UAVs also face constraints in terms of resources like storage and computational power. Therefore, it is vital to develop effective UAV assistance solutions to provide long-term demands of in-network services. The dynamic scheduling and computation offloading of UAVs is the subject of this paper. Specifically, we propose a deep deterministic policy gradient algorithm based on a greedy strategy (DDPGG) to jointly optimize dynamic scheduling, device association, and task allocation of UAVs, with the goal of minimizing the weighted sum of total system energy consumption and time delay. The problem is formulated as a nonlinear programming problem involving mixed integers. The simulation results demonstrate that the DDPGG algorithm we have proposed exhibits a higher level of performance in comparison to its competitors.

The emergence of unmanned aerial vehicles (UAVs) ushers in a new era for mobile edge computing (MEC), significantly expanding its range of service and potential applications. Due to the limited storage capacity and energy budget of UAVs, it is crucial to determine a reasonable service caching and task offloading strategy. Service caching means that task-related programs and the associated databases are cached on edge servers. In this paper, we consider the time latency and energy consumption caused by frequent changes to the service caching, aiming to jointly optimize the computational offloading, resource allocation, and service caching in multi-UAV assisted MEC systems at different time scales. The objective of this optimization is to reduce the overall system delay while staying within the energy limitations of both the UAVs and ground devices. An improved service caching policy (SCP) is proposed, which is based on task popularity and utilizes the greedy dual size frequency (GDSF) algorithm. The SCP is combined with the twin delayed deep deterministic policy gradient (TD3) algorithm to propose an innovative dual timescale TD3 (DTTD3) algorithm. The numerical outcomes obtained from a substantial number of simulation experiments demonstrate that DTTD3 outperforms existing benchmark methods in terms of convergence and parameter optimization.