With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for ...With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.展开更多
基金Foundation items:National Natural Science Foundation of China(Nos.62301307 and 62072096)Shanghai Pujiang Program,China(No.23PJD041)Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.CGA60)。
文摘With the growth of maritime activities,the number of computationally complex applications is growing exponentially.Mobile edge computing(MEC)is widely recognized as a viable option to address the substantial need for wireless communications and compute-intensive operations in maritime environments.To reduce the processing load and meet the demands of mobile terminals for high bandwidth,low latency and multiple access,MEC systems with unmanned aerial vehicles(UAVs)have been proposed and extensively explored.In this paper,a maritime MEC network that employs a top-UAV(T-UAV)for task offloading supported by digital twin(DT)is considered.To explore the task offloading strategy employed by the edge server,the flight trajectory and resource allocation strategy of the T-UAV is studied in detail.The objective of this study is to minimize latency costs while ensuring that the energy of the T-UAV is sufficient to fulfill services.In order to accomplish this objective,the joint optimization problem is described as a Markov decision process(MDP).To overcome this problem,the priority-based reinforcement learning(RL)algorithm for computation offloading and trajectory planning(PRL-COTP)is developed.The simulation results demonstrate that the proposed approach can significantlyreduce the overall cost of the system in comparison to other benchmarks.
