Device-edge Collaborative Intelligent Task Inference Mechanism Based on Wireless RF Energy Harvesting
To solve challenges of high bandwidth demand and poor real-time performance in cloud-based intelligent task inference,as well as limited computing power and energy constraints of edge devices,we introduce a wireless Radio Frequency(RF)energy harves-ting technology at the edge devices to achieve independent energy supply.An end-edge collaborative intelligent task inference mecha-nism based on wireless RF energy harvesting is proposed to maximize the completion rate of intelligent task inference at devices.Firstly,an energy harvesting model and edge collaborative inference model are constructed.Secondly,considering time constraints for RF energy harvesting at edge devices,time constraints for device-edge collaborative intelligent task inference,power constraints for transmission at devices,constraints on available energy,and constraint of edge computing resources,we maximize the number of completed intelligent inference tasks.Finally,a joint optimization algorithm based on Deep Deterministic Policy Gradient(DDPG)is proposed to optimize partition decision of Deep Neural Network(DNN)models and allocation of communication and computing resources at edge,aiming to obtain optimal RF energy harvesting time,transmission power,DNN model partition points,and edge computing resource allocation.Sim-ulation results demonstrate the effectiveness of the proposed algorithm in improving the completion rate of intelligent task inference,which outperformes other comparative algorithms significantly.
RF energy acquisitiondevice-edge collaborationintelligent task inferenceDDPG
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