基于多目标优化的无线传感器网络移动充电及数据收集算法

吕增威; 魏振春; 韩江洪; 孙仁浩; 夏成凯

doi:10.11999/JEIT180897

基于多目标优化的无线传感器网络移动充电及数据收集算法

doi: 10.11999/JEIT180897

吕增威¹,
魏振春^{1, 2, 3, ,},
韩江洪^{1, 2, 3},
孙仁浩¹,
夏成凯¹

1.
合肥工业大学计算机与信息学院合肥 230601
2.
安全关键工业测控技术教育部工程研究中心合肥 230000
3.
工业安全与应急技术安徽省重点实验室合肥 230002

基金项目: 国家自然科学基金(61502142, 61701162)

详细信息

作者简介:
吕增威：男，1989年生，博士生，研究方向为无线传感器网络、智能算法

魏振春：男，1978年生，副教授、硕士生导师，研究方向为无线传感器网络、智能计算、机器学习

韩江洪：男，1954年生，教授、博士生导师，研究方向为无线通信、无线传感器网络、智能计算

孙仁浩：男，1993年生，硕士，研究方向为无线传感器网络、嵌入式系统

夏成凯：男，1994年生，硕士生，研究方向为无线传感器网络、智能计算

通讯作者:
魏振春　weizc@hfut.edu.cn

中图分类号: TN925.3
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出版历程
- 收稿日期: 2018-09-18
- 修回日期: 2019-03-04
- 网络出版日期: 2019-03-26
- 刊出日期: 2019-08-01

A Mobile Charging and Data Collecting Algorithm Based on Multi-objective Optimization

Zengwei LÜ¹,
Zhenchun WEI^{1, 2, 3
, ,},
Jianghong HAN^{1, 2, 3},
Renhao SUN¹,
Chengkai XIA¹

1.
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
2.
Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei 230000, China
3.
Key Laboratory of Industry Safety and Emergency Technology, Anhui Province, Hefei 230002, China

Funds: The National Natural Science Foundation of China (61502142, 61701162)

摘要

摘要: 近年来，通过引入移动设备(ME)为无线传感器网络(WSNs)进行无线充电和数据收集成为一个研究热点。传统方法一般先根据节点的充电需求优先级确定移动路径，再根据该路径依次对节点进行数据收集。该文同时考虑充电需求和数据收集两个维度，以最大化ME的总能量利用率和最小化数据收集平均时延为目标，建立多目标一对多充电及数据收集模型。在ME携带的行驶能量和充电能量不足的前提下，设计路径规划策略和均衡化充电策略，并改进多目标蚁群算法对该文问题进行求解。实验结果表明，该文算法在多种场景下的目标值、Pareto解的数量、Pareto解集的均匀性、分布范围等性能指标均优于NSGA-II算法。
- 无线可充电传感器网络 /
- 数据收集 /
- 多目标优化
Abstract: Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in Wireless Sensor Networks (WSNs) is a hot topic. Existing studies determine usually the traveling path of ME according to the charging requirements of sensor nodes firstly, and then handle the data collecting. In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and data collecting model for ME is established with two optimization objectives, maximizing the total energy utilization and minimizing the average delay of data collecting. Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithm is proposed to solve the problem. Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior over NSGA-II algorithm.
- Wireless Rechargeable Sensor Networks (WRSNs) /
- Data collecting /
- Multiple-objective optimization

HTML全文

图 1 网络模型

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图 2 路径规划示意图

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图 3 3种场景下VN-MOAC算法和NSGA-II算法性能统计盒状图

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图 4 50个节点和100个节点下算法的收敛性对比

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图 5 VN-MOAC算法在不同迭代次数下的Pareto前沿

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表 1 VN-MOAC算法和NSGA-II算法计算结果比较

指标	网络场景	$\phi $ (%)		$\overline {\Delta \tau } $ (s)		${\rm{RN}}$		${\rm{SP}}$		$M_3^*$
指标	网络场景	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II
最优值	L1	94.02	92.31	1663.82	1812.30	45	29	38.50	45.67	392.65	378.86
	L2	88.94	85.78	769.24	1176.02	35	25	161.93	180.14	391.60	342.24
	L3	95.85	93.36	1608.94	1813.72	49	27	85.69	105.50	373.73	294.36
最差值	L1	75.98	73.75	5184.72	5361.46	26	17	790.03	883.10	31.59	20.73
	L2	69.30	67.16	3784.31	3898.69	18	13	794.47	869.26	42.25	24.29
	L3	76.35	71.24	5197.55	5408.48	21	12	690.69	726.31	83.73	73.19
平均值	L1	85.01	83.47	3145.27	3359.45	38	21	389.43	510.69	192.43	167.94
	L2	80.17	75.44	2189.46	2411.95	26	18	391.85	416.68	220.62	202.72
	L3	85.98	82.76	3268.98	3333.71	37	20	363.16	393.92	187.60	166.90
中值	L1	84.86	82.84	3137.80	3145.03	38	22	410.53	527.60	125.62	95.47
	L2	82.34	74.66	2285.31	2329.71	27	19	352.32	406.76	204.86	171.06
	L3	87.81	82.48	3308.81	3365.68	38	21	347.66	384.47	182.56	172.99

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