DDPG-based intelligent rechargeable fog computation offloading for IoT

被引:0
作者
Siguang Chen
Xinwei Ge
Qian Wang
Yifeng Miao
Xiukai Ruan
机构
[1] Nanjing University of Posts and Telecommunications,Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things
[2] Nanjing University of Posts and Telecommunications,Jiangsu Engineering Research Center of Communication and Network Technology
[3] Wenzhou University,Institute of Intelligent Locks
来源
Wireless Networks | 2022年 / 28卷
关键词
Fog computing; Computation offloading; SWIPT; Energy harvesting; Deep reinforcement learning;
D O I
暂无
中图分类号
学科分类号
摘要
In view of the existing computation offloading research on fog computing network scenarios, most scenarios focus on reducing energy consumption and delay and lack the joint consideration of smart device rechargeability. This paper proposes a deep deterministic policy gradient-based intelligent rechargeable fog computation offloading mechanism that is combined with simultaneous wireless information and power transfer technology. Specifically, an optimization problem that minimizes the total energy consumption for completing all tasks in a multiuser scenario is formulated, and the joint optimization of the task offloading ratio, uplink channel bandwidth, power split ratio and computing resource allocation is fully considered. Based on the above nonconvex optimization problem with a continuous action space, a communication, computation and energy harvesting co-aware intelligent computation offloading algorithm is developed. It can achieve the optimal energy consumption and delay, and similar to a double deep Q-network, an inverting gradient updating-based dual actor-critic neural network design can improve the convergence and stability of the training process. Finally, the simulation results validate that the proposed mechanism can converge quickly and can effectively reduce the energy consumption with the lowest task delay.
引用
收藏
页码:3293 / 3304
页数:11
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