Dynamic adaptive offloading method based on WPT-MEC

Lin SU; Xiaochao DANG; Zhanjun HAO; Chunrui RU; Xu SHANG

doi:10.11959/j.issn.2096-3750.2022.00291

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Dynamic adaptive offloading method based on WPT-MEC

Theory and Technology | 更新时间：2024-08-16

- Dynamic adaptive offloading method based on WPT-MEC
- Chinese Journal on Internet of Things Vol. 6, Issue 4, Pages: 128-138(2022)
- 作者机构：
  
  1. 西北师范大学计算机科学与工程学院，甘肃兰州 730070
  2. 甘肃省物联网工程研究中心，甘肃兰州 730070
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00291
  CLC： TP391
- Online First：2022-12，
  
  Published：30 December 2022
- 稿件说明：
移动端阅览
Lin SU, Xiaochao DANG, Zhanjun HAO, et al. Dynamic adaptive offloading method based on WPT-MEC[J]. Chinese Journal on Internet of Things, 2022, 6(4): 128-138.
DOI：

Lin SU, Xiaochao DANG, Zhanjun HAO, et al. Dynamic adaptive offloading method based on WPT-MEC[J]. Chinese Journal on Internet of Things, 2022, 6(4): 128-138. DOI： 10.11959/j.issn.2096-3750.2022.00291.

摘要

针对动态衰落时变的信道状态信息，为解决多用户的任务卸载和资源优化问题，将无线电能传输（WPT

wireless power transmission）技术和移动边缘计算（MEC

mobile edge computing）结合，提出一种基于WPT-MEC的动态自适应卸载（RLDO）方法。无线电能传输技术可为无线终端用户（WEU

wireless end-user）提供能量，有效缓解传统电池供能有限的问题。为使资源利用最大化，设计一个无线电能的 MEC 网络模型，无线终端用户从无线接入点（AP

access point）收集的能量存储至可充电电池内，再利用此能量进行任务计算或任务卸载。该方法通过部署在MEC服务器的全连接深度神经网络（DNN

deep neural network）进行实时的卸载决策。采用完全的二元制卸载策略进行卸载决策。仿真结果表明，在面向多用户时变的无线信道环境下，该方法的计算速率仍可以保持在92%以上。与基本方法相比，在提高计算速率、降低时延和能耗方面具有较大优越性，有效降低了计算复杂度。

Abstract

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.

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references

LAGHARIA A , WU K , LAGHARIR A , et al . A review and state of art of internet of things (IoT) [J ] . Archives of Computational Methods in Engineering , 2021 ( 29 ): 1395 - 1413 .

SUN W , ZHANG H B , WANG R , et al . Reducing offloading latency for digital twin edge networks in 6G [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 10 ): 12240 - 12251 .

MACH P , BECVAR Z . Mobile edge computing:a survey on architecture and computation offloading [J ] . IEEE Communications Surveys ＆Tutorials , 2017 , 19 ( 3 ): 1628 - 1656 .

WU J S , GUO S , HUANG H W , et al . Information and communications technologies for sustainable development goals:state-of-the-art,needs and perspectives [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 3 ): 2389 - 2406 .

ABBAS N , ZHANG Y , TAHERKORDI A , et al . Mobile edge computing:a survey [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 1 ): 450 - 465 .

KAVYASHREE S , CHAVA KUMARI H A . Survey on computation offloading strategies in cellular networks with mobile edge computing [C ] // Data Intelligence and Cognitive Informatics . Singapore:Springer , 2022 : 567 - 575 .

SPINELLI F , MANCUSO V . Toward enabled industrial verticals in 5G:a survey on MEC-based approaches to provisioning and flexibility [J ] . IEEE Communications Surveys ＆ Tutorials , 2020 , 23 ( 1 ): 596 - 630 .

CHEN X H , CAI Y L , LI L Y , et al . Energy-efficient resource allocation for latency-sensitive mobile edge computing [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 2 ): 2246 - 2262 .

TALEB T , SAMDANIS K , MADA B , et al . On multi-access edge computing:a survey of the emerging 5G network edge cloud architecture and orchestration [J ] . IEEE Communications Surveys ＆ Tutorials , 2017 , 19 ( 3 ): 1657 - 1681 .

JAMALIPOUR A , BI Y . Introduction to wireless powered communication network wireless powered communication networks [EB ] . 2019 .

CANNON B L , HOBURG J F , STANCIL D D , et al . Magnetic resonant coupling as a potential means for wireless power transfer to multiple small receivers [J ] . IEEE Transactions on Power Electronics , 2009 , 24 ( 7 ): 1819 - 1825 .

DAHUANG , URZHUMOV Y , SMITH D R , et al . Magnetic superlens-enhanced inductive coupling for wireless power transfer [J ] . Journal of Applied Physics , 2012 , 111 ( 6 ): 064902 .

BI S Z , HO C K , ZHANG R . Wireless powered communication:opportunities and challenges [J ] . IEEE Communications Magazine , 2015 , 53 ( 4 ): 117 - 125 .

RANA M M , XIANG W . IoT communications network for wireless power transfer system state estimation and stabilization [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 5 ): 4142 - 4150 .

FENG W M , TANG J , ZHAO N , et al . Hybrid beamforming design and resource allocation for UAV-aided wireless-powered mobile edge computing networks with NOMA [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 11 ): 3271 - 3286 .

PSOMAS C , KRIKIDIS I . Wireless powered mobile edge computing:offloading or local computation? [J ] . IEEE Communications Letters , 2020 , 24 ( 11 ): 2642 - 2646 .

LI L , XU G C , LIU P , et al . Jointly optimize the residual energy of multiple mobile devices in the MEC-WPT system [J ] . Future Internet , 2020 , 12 ( 12 ): 233 .

LI Y , XIA S C , ZHENGM Y , et al . Lyapunov optimization-based trade-off policy for mobile cloud offloading in heterogeneous wireless networks [J ] . IEEE Transactions on Cloud Computing , 2022 , 10 ( 1 ): 491 - 505 .

GAO L F , MOH M . Joint computation offloading and prioritized scheduling in mobile edge computing [C ] // Proceedings of 2018 International Conference on High Performance Computing ＆ Simulation (HPCS) . Piscataway:IEEE Press , 2018 : 1000 - 1007 .

ZHANG J , ZHOU L , TANG Q , et al . Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 2 ): 3688 - 3699 .

YANG T T , FENG H L , GAO S , et al . Two-stage offloading optimization for energy-latency tradeoff with mobile edge computing in maritime Internet of Things [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 7 ): 5954 - 5963 .

SODHRO A H , LUO Z W , SANGAIAH A K , et al . Mobile edge computing based QoS optimization in medical healthcare applications [J ] . International Journal of Information Management , 2019 , 45 : 308 - 318 .

WANG F , XU J , WANG X , et al . Joint offloading and computing optimization in wireless powered mobile-edge computing systems [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 3 ): 1784 - 1797 .

HUANG L , FENG X , ZHANG C , et al . Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing [J ] . Digital Communications and Networks , 2019 , 5 ( 1 ): 10 - 17 .

HUANG L , BI S Z , ZHANG Y J A . Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks [J ] . IEEE Transactions on Mobile Computing , 2020 , 19 ( 11 ): 2581 - 2593 .

NGUYEN P X , TRAN D H , ONIRETI O , et al . Backscatter-assisted data offloading in OFDMA-based wireless-powered mobile edge computing for IoT networks [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 11 ): 9233 - 9243 .

ZHANG K , ZHU Y X , LENG S D , et al . Deep learning empowered task offloading for mobile edge computing in urban informatics [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 5 ): 7635 - 7647 .

CHEN J , XING HL , XIAO ZW , et al . A DRL agent for jointly optimizing computation offloading and resource allocation in MEC [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 24 ): 17508 - 17524 .

LI J , GAO H , LYU T , et al . Deep reinforcement learning based computation offloading and resource allocation for MEC [C ] // Proceedings of 2018 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway:IEEE Press , 2018 : 1 - 6 .

ZHANG R , HO C K . MIMO broadcasting for simultaneous wireless information and power transfer [J ] . 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011 , 2011 : 1 - 5 .

ZHANG J , HU X P , NING Z L , et al . Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 4 ): 2633 - 2645 .

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