Dynamic adaptive offloading method based on WPT-MEC
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 comput...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2022-12-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00291/ |
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| _version_ | 1850092567422566400 |
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| author | Lin SU Xiaochao DANG Zhanjun HAO Chunrui RU Xu SHANG |
| author_facet | Lin SU Xiaochao DANG Zhanjun HAO Chunrui RU Xu SHANG |
| author_sort | Lin SU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-444683bfefa546019a6d3ead4ca0d6dc |
| institution | DOAJ |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-444683bfefa546019a6d3ead4ca0d6dc2025-08-20T02:42:05ZzhoChina InfoCom Media Group物联网学报2096-37502022-12-01612813859580744Dynamic adaptive offloading method based on WPT-MECLin SUXiaochao DANGZhanjun HAOChunrui RUXu SHANGFor 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.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00291/channel state informationmobile edge computingwireless power transmissiondeep neural networkdynamic adaptive offloading |
| spellingShingle | Lin SU Xiaochao DANG Zhanjun HAO Chunrui RU Xu SHANG Dynamic adaptive offloading method based on WPT-MEC 物联网学报 channel state information mobile edge computing wireless power transmission deep neural network dynamic adaptive offloading |
| title | Dynamic adaptive offloading method based on WPT-MEC |
| title_full | Dynamic adaptive offloading method based on WPT-MEC |
| title_fullStr | Dynamic adaptive offloading method based on WPT-MEC |
| title_full_unstemmed | Dynamic adaptive offloading method based on WPT-MEC |
| title_short | Dynamic adaptive offloading method based on WPT-MEC |
| title_sort | dynamic adaptive offloading method based on wpt mec |
| topic | channel state information mobile edge computing wireless power transmission deep neural network dynamic adaptive offloading |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00291/ |
| work_keys_str_mv | AT linsu dynamicadaptiveoffloadingmethodbasedonwptmec AT xiaochaodang dynamicadaptiveoffloadingmethodbasedonwptmec AT zhanjunhao dynamicadaptiveoffloadingmethodbasedonwptmec AT chunruiru dynamicadaptiveoffloadingmethodbasedonwptmec AT xushang dynamicadaptiveoffloadingmethodbasedonwptmec |