基于效用最大化的无线可充电传感器网络有向充电调度方案

王杨; 张鑫; 赵传信; 方群; 艾世成

doi:10.11999/JEIT200129

基于效用最大化的无线可充电传感器网络有向充电调度方案

doi: 10.11999/JEIT200129

王杨^1, ,,
张鑫¹,
赵传信¹,
方群¹,
艾世成²

1.
安徽师范大学计算机与信息学院芜湖 241002
2.
东南大学计算机科学与工程学院南京 211189

基金项目: 国家自然科学基金(61871412)，安徽省自然科学基金重点项目(KJ2019A0938)，安徽省社科规划基金(AHSKY2017D42)，安徽高校自然科学重点项目研究项目(KJ2017A552, KJ2019A0979)

详细信息

作者简介:
王杨：男，1971年生，教授，硕士生导师，研究方向为可充电传感网、机器学习、系统优化

张鑫：男，1996年生，硕士生，研究方向为可充电传感网

赵传信：男，1977年生，教授，博士生导师，研究方向可充电传感网

方群：男，1972年生，教授，硕士生导师，研究方向为物联网

艾世成：男，1994年生，硕士生，研究方向为网络优化

通讯作者:
王杨　wycap@126.com

中图分类号: TP393
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出版历程
- 收稿日期: 2020-02-26
- 修回日期: 2020-11-26
- 网络出版日期: 2020-12-02
- 刊出日期: 2021-05-18

Directional Charging Schedule Scheme Based on Charging Utility Maximization for Wireless Rechargeable Sensor Network

1.
School of Computer and Information, Anhui Normal University, Wuhu 241002, China
2.
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

Funds: The National Natural Science Foundation of China (61871412), The Key Project of Natural Science Foundation of Anhui Province (KJ2019A0938), Anhui Province Major Humanities and Social Science Fund Project (AHSKY2017D42), The Key Natural Science Projects of Anhui University (KJ2017A552, KJ2019A0979)

摘要

摘要: 针对当前无线可充电传感器网络(WRSNs)一对一移动充电方式存在充电效率低、定向充电模型缺乏问题，该文提出了一种基于充电效用最大化(MUC)的一对多有向充电调度方案。方案首先筛选网络中充电增益最大的有向覆盖子集；然后根据有向覆盖子集确定充电锚点，并进而规划充电器的移动路径；最后在满足移动充电器能量和充电周期约束条件下优化移动充电器的充电时间。实验结果表明，该方案与平均能量充电(AEC)、固定能量充电(FEC)相比，充电效率分别提高了13.7%和32.7%；与最多节点覆盖(MNC)、最大平均增益覆盖(MAGC)子集筛选方案相比，充电效率分别提高了4.4%和35.9%；同时在网络饿死节点数目上与MNC, MAGC方案相比也显著降低。
- 无线可充电传感器网络 /
- 充电效用最大化 /
- 有向充电 /
- 有向覆盖子集
Abstract: The one-to-one charging method for Wireless Rechargeable Sensor Networks (WRSNs) mobile chargers has some problems such as low charging efficiency and lack of directional charging model. To cope with the problems, a one-to-many directed charging scheduling scheme based on Maximizing Utility Charging (MUC) is proposed. In this scheme, the directed coverage subsets with the largest charging gain in the network is first searched; Then the charging anchor points are determined according to the directed coverage subset and the charger movement path is planned; Finally, the constraints of mobile charger energy and charging cycle are considered and the charging time is optimized. Experimental results show that in comparation with Average Energy Charge (AEC) and Fixed Energy Charge (FEC) charging time optimization schemes, the charging efficiency of this scheme is increased by 13.7% and 32.7% respectively. In comparation with Maximum Node Coverage (MNC) and Maximum Average Gain Coverage (MAGC) subset screening schemes, the charging efficiency is increased by 4.4% and 35.9% respectively. In addition, the number of starved nodes in the network is significantly reduced compared with the MNC, MAGC schemes.
- Wireless Rechargeable Sensor Network(WRSN) /
- Maximum Utility Charging(MUC) /
- Directional charge /
- Directed coverage subset

HTML全文

图 1 网络系统模型

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图 2 有向充电模型

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图 3 覆盖集合提取示例图

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图 4 电池充电获能图

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图 5 不同规模下算法充电效用对比图

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图 6 不同节点规模下充电算法饿死节点数对比图

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图 7 参数$p$对不同充电算法的影响

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图 8 不同节点分布对充电算法的影响

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图 9 不同覆盖算法下覆盖子集筛选图

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图 10 不同节点分布下覆盖子集筛选图

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图 11 不同节点规模下子集筛选算法充电效用对比图

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图 12 不同节点规模下子集筛选算法饿死节点数对比图

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表 1 参数设置

参数	值
移动充电器能量EI	80000 J
传感器能量B	1000 J
移动充电器移动速度v	2 m/s
移动充电器移动能耗C_v	5 J/s
移动充电器充电能耗C	2 J/s
充电周期T	60 min
充电参数p	200
最大充电距离D	35 m
有向覆盖角A	π/3

下载: 导出CSV

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