融合射频能量采集的协同节能计算迁移研究

doi:10.11959/j.issn.2096-3750.2023.00311

物联网学报 ›› 2023, Vol. 7 ›› Issue (1): 83-92.doi: 10.11959/j.issn.2096-3750.2023.00311

融合射频能量采集的协同节能计算迁移研究

汤蓓¹, 王倩¹, 陈思光¹^,²

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

修回日期:2022-11-12 出版日期:2023-03-30 发布日期:2023-03-01
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
基金资助:
国家自然科学基金资助项目(61971235);中国博士后科学基金资助项目(2018M630590);江苏省博士后科研资助计划(2021K501C);江苏省“333高层次人才培养工程”资助项目;南京邮电大学“1311人才计划”资助项目

Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism

Bei TANG¹, Qian WANG¹, Siguang CHEN¹^,²

¹ School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
² Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Revised:2022-11-12 Online:2023-03-30 Published:2023-03-01
Supported by:
The National Natural Science Foundation of China(61971235);The China Postdoctoral Science Foundation(2018M630590);The Jiangsu Planned Projects for Postdoctoral Research Funds(2021K501C);The “333 High-level Talents Training Project” of Jiangsu Province;The “1311Talents Plan” of NJUPT

摘要/Abstract

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%。

关键词: 计算迁移, 能量采集, 资源分配, 功率分割

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.

Key words: computation offloading, energy harvesting, resource allocation, power splitting

中图分类号:

TP393

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.

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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

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