Task Scheduling and Privacy Protection for Multi-UAV Dynamic Environment

The next generation mobile communication systems will experience huge transformation for multiple applications, which can provide pervasive intelligence and release more network resources for multiple terrestrial users. Moreover, digital twin (DT) technique helps each terrestrial user enable the mapping from physical world to digital space for the sake of reducing transmission latency. Thus, the integration between the terrestrial network and DT can expedite the computation offloading. Nevertheless, time-varying channel gains and dynamic UAV locations severely hinder better quality of service. In this paper, we envision a UAV-DT integrated task scheduling model to maximize the processed number of bits while minimizing the privacy protection overhead, which can further reduce the channel interference. Based on long-term task queues, we present a Lyapunov stability theory based multi-agent federated reinforcement learning (MAFRL) algorithm to optimize the CPU cycle frequency, transmission power and block size, which facilitate the integration between the communication, computation and block resources. Furthermore, we propose a blockchain-based verification mechanism to strengthen the privacy protection, and then demonstrate the performance upper bounds in terms of convergent task queues. Finally, massive simulation results show that the proposed MAFRL framework has approximately 1.7% performance gains in terms of the processed number of bits compared with state-of-the-art baseline methods.