Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (4): 128-138.doi: 10.11959/j.issn.2096-3750.2022.00291

• Theory and Technology • Previous Articles     Next Articles

Dynamic adaptive offloading method based on WPT-MEC

Lin SU1, Xiaochao DANG1,2, Zhanjun HAO1,2, Chunrui RU1, Xu SHANG1   

  1. 1 College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
    2 Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
  • Revised:2022-08-04 Online:2022-12-30 Published:2022-12-01
  • Supported by:
    The National Natural Science Foundation of China(62162056);The Industrial Support Foundations of Gansu(2021CYZC-06)


For the dynamic fading time-varying channel state information, a dynamic adaptive offloading (RLDO) method based on WPT-MEC was proposed to solve the task offloading and resource optimization problems for multiple users by combining wireless power transmission (WPT) technology and mobile edge computing (MEC).The wireless power transmission technology can provide energy to wireless end-user (WEU) and effectively alleviate the problem of limited energy supply from conventional batteries.To maximize the resource utilization, a wireless powered MEC network model was designed where the energy collected by the wireless end-user from the wireless access point (AP) was stored in a rechargeable battery, and then this energy was used for task computation or task offloading.The approach performed real-time offloading decisions through a fully connected deep neural networks (DNN) deployed in the MEC server.A fully binary offloading strategy was used for the offloading decision.Simulation results show that the computation rate of the method can still be maintained above 92% in a multi-user time-varying wireless channel-oriented environment.Compared with the basic method, it has great advantages in improving the calculation rate, reducing the delay and energy consumption,and effectively reduces computational complexity.

Key words: channel state information, mobile edge computing, wireless power transmission, deep neural network, dynamic adaptive offloading

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