融合射频能量采集的协同节能计算迁移研究

汤蓓; 王倩; 陈思光

doi:10.11959/j.issn.2096-3750.2023.00311

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融合射频能量采集的协同节能计算迁移研究

理论与技术 | 更新时间：2024-08-16

- 融合射频能量采集的协同节能计算迁移研究
- Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism
- 物联网学报 2023年7卷第1期页码：83-92
- 作者机构：
  
  1. 南京邮电大学物联网学院，江苏南京 210003
  2. 南京邮电大学江苏省宽带无线通信和物联网重点实验室，江苏南京 210003
- 作者简介：
  
  [ "汤蓓（1997- ），女，南京邮电大学硕士生，主要研究方向为雾/边缘计算、智能物联网" ]
  [ "王倩（1996- ），女，南京邮电大学博士生，主要研究方向为雾/边缘计算、智能物联网" ]
  [ "陈思光（1984-），男，博士，南京邮电大学教授，主要研究方向为雾/边缘计算、智能物联网" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61971235);中国博士后科学基金资助项目;The China Postdoctoral Science Foundation(2018M630590);江苏省博士后科研资助计划;The Jiangsu Planned Projects for Postdoctoral Research Funds(2021K501C);江苏省“333高层次人才培养工程”资助项目;The “333 High-level Talents Training Project” of Jiangsu Province;南京邮电大学“1311人才计划”资助项目;The “1311Talents Plan” of NJUPT
- DOI：10.11959/j.issn.2096-3750.2023.00311
  中图分类号： TP393
- 纸质出版日期：2023-03-30，
  
  网络出版日期：2023-03，
- 稿件说明：
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汤蓓, 王倩, 陈思光. 融合射频能量采集的协同节能计算迁移研究[J]. 物联网学报, 2023,7(1):83-92.

BEI TANG, QIAN WANG, SIGUANG CHEN. Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism. [J]. Chinese journal on internet of things, 2023, 7(1): 83-92.
汤蓓, 王倩, 陈思光. 融合射频能量采集的协同节能计算迁移研究[J]. 物联网学报, 2023,7(1):83-92. DOI： 10.11959/j.issn.2096-3750.2023.00311.

BEI TANG, QIAN WANG, SIGUANG CHEN. Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism. [J]. Chinese journal on internet of things, 2023, 7(1): 83-92. DOI： 10.11959/j.issn.2096-3750.2023.00311.

摘要

为适应垂直市场差异化能源需求，保障物联网设备的高效可持续性运行，研究了一种融合射频能量采集的协同节能计算迁移机制。具体地，基于对计算迁移决策、上行带宽资源分配、下行带宽资源分配及基站功率分割的联合优化考量，构建了一个最小化系统总能耗的优化问题。为有效求解该优化问题，融合惩罚函数的概念设计了新的评价指标，并提出了一种基于自适应粒子群优化的协同节能计算迁移算法。该算法构造了动态变化的惯性权重和线性调节的惩罚因子，可在迭代搜索过程中实时变更粒子群落的空间分布密度，以生成可容忍惩罚下的最优计算迁移策略；进一步地，为避免粒子越过探索范围，引入了速度边界限制，可降低无效解的产生概率，提升搜索有效性。最后，仿真结果表明所提算法能够实现较高的收敛效率及求解精度，且与其他常见基准方案相比，完成任务处理的系统总能耗可分别降低34.09%、14.72%和6.86%。

Abstract

In order to fit the differentiated energy demands in vertical markets and ensure that internet of things (IoT) devices can hold an efficient and sustainable operation mode

a radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism was studied.Specifically

a system energy consumption minimization problem was formulated under the joint optimization consideration of computation offloading decision

uplink bandwidth resource allocation

downlink bandwidth resource allocation and base station power splitting.Meanwhile

by combining the concept of penalty function

a new evaluation index was introduced

and then an adaptive particle swarm optimization-based collaborative energy saving computation offloading (APSO-CESCO) algorithm was proposed to solve the problem.The proposed algorithm constructed dynamic inertia weight and linearly adjusted penalty factor

which could alternate the spatial distribution density of the particle community in real-time during the iterative search process

and the optimal computation offloading policy with tolerable punishment could be well-generated.Furthermore

to prevent particles from exceeding exploration range

the velocity boundary was introduced which could also reduce the generation probability of invalid solutions and improve the actual exploration effectiveness.Finally

the simulation results show that the proposed algorithm can achieve higher convergence efficiency and solution accuracy

and compared with other common benchmark schemes

the system energy consumption can be reduced by 34.09%

14.72%

and 6.86%

respectively.

关键词

计算迁移能量采集资源分配功率分割

Keywords

computation offloadingenergy harvestingresource allocationpower splitting

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