Unloading Strategy of Dynamic Computing Tasks in UAV-Assisted Railway Edge Computing Network
In the UAV-assisted railway moving edge computing(MEC)system,an effective method for computing task unloading and power control is proposed with service delay and task failure execution cost as the performance index,while ensuring the stability of energy consumption of UAVs and the re-quirement of computing delay constraints.With the Lyapunov optimization theory,the optimization prob-lem based on long-term performance index is transformed into the sub-problems of multiple time slots.A dynamic computational unloading algorithm based on domain adaptive learning is proposed to make unloa-ding decisions for computing tasks in each time slot.Meanwhile,in each time slot,the proposed algorithm also optimizes the transmission power of the service data download when the unloading decision determines that the computing task is executed on the local device.The experimental results show that the proposed algorithm can effectively reduce the service delay and improve the efficiency of task completion.
railway edge computingtask unloadingenergy consumption of UAVlow latencydo-main adaptive algorithm
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