Digital Twin-Empowered Offloading Optimisation and Resource Allocation for UAV-Assisted IoT Network Systems

With the development of Fifth Generation (5G)/Sixth Generation (6G) -enabled Internet of Things (IoT) networks, different user equipment (UE) dynamically generates massive raw data and delay-sensitive computation tasks to be offloaded and processed at the mobile edge computing (MEC) nodes. In this paper, we propose a comprehensive digital twin-empowered UAV-assisted edge intelligent IoT framework, which enables UEs to offload their delay-sensitive tasks to a UAV-assisted MEC node. We aim to minimise the maximum total service delay including the transmission delay and the processing delay among all UEs. A deep deterministic policy gradient-based offloading and resource allocation optimisation algorithm, named (DDPG-ORAO), is proposed to optimise task offloading decisions among all UEs, which jointly optimising the communication and computation resources allocation among all UEs and all UAV-assisted MEC nodes. Simulation results show that our proposed optimisation algorithm outperforms the benchmarks in terms of the total service delay of all UEs.