基于多目標(biāo)優(yōu)化的無(wú)線(xiàn)傳感器網(wǎng)絡(luò)移動(dòng)充電及數(shù)據(jù)收集算法

呂增威; 魏振春; 韓江洪; 孫仁浩; 夏成凱

doi:10.11999/JEIT180897

基于多目標(biāo)優(yōu)化的無(wú)線(xiàn)傳感器網(wǎng)絡(luò)移動(dòng)充電及數(shù)據(jù)收集算法

doi: 10.11999/JEIT180897

呂增威¹,
魏振春^{1, 2, 3, ,},
韓江洪^{1, 2, 3},
孫仁浩¹,
夏成凱¹

1.
合肥工業(yè)大學(xué)計(jì)算機(jī)與信息學(xué)院??合肥??230601
2.
安全關(guān)鍵工業(yè)測(cè)控技術(shù)教育部工程研究中心??合肥??230000
3.
工業(yè)安全與應(yīng)急技術(shù)安徽省重點(diǎn)實(shí)驗(yàn)室??合肥??230002

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61502142, 61701162)

詳細(xì)信息

作者簡(jiǎn)介:
呂增威：男，1989年生，博士生，研究方向?yàn)闊o(wú)線(xiàn)傳感器網(wǎng)絡(luò)、智能算法

魏振春：男，1978年生，副教授、碩士生導(dǎo)師，研究方向?yàn)闊o(wú)線(xiàn)傳感器網(wǎng)絡(luò)、智能計(jì)算、機(jī)器學(xué)習(xí)

韓江洪：男，1954年生，教授、博士生導(dǎo)師，研究方向?yàn)闊o(wú)線(xiàn)通信、無(wú)線(xiàn)傳感器網(wǎng)絡(luò)、智能計(jì)算

孫仁浩：男，1993年生，碩士，研究方向?yàn)闊o(wú)線(xiàn)傳感器網(wǎng)絡(luò)、嵌入式系統(tǒng)

夏成凱：男，1994年生，碩士生，研究方向?yàn)闊o(wú)線(xiàn)傳感器網(wǎng)絡(luò)、智能計(jì)算

通訊作者:
魏振春　weizc@hfut.edu.cn

中圖分類(lèi)號(hào): TN925.3
計(jì)量
- 文章訪(fǎng)問(wèn)數(shù): 3010
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-09-18
- 修回日期: 2019-03-04
- 網(wǎng)絡(luò)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: 近年來(lái)，通過(guò)引入移動(dòng)設(shè)備(ME)為無(wú)線(xiàn)傳感器網(wǎng)絡(luò)(WSNs)進(jìn)行無(wú)線(xiàn)充電和數(shù)據(jù)收集成為一個(gè)研究熱點(diǎn)。傳統(tǒng)方法一般先根據(jù)節(jié)點(diǎn)的充電需求優(yōu)先級(jí)確定移動(dòng)路徑，再根據(jù)該路徑依次對(duì)節(jié)點(diǎn)進(jìn)行數(shù)據(jù)收集。該文同時(shí)考慮充電需求和數(shù)據(jù)收集兩個(gè)維度，以最大化ME的總能量利用率和最小化數(shù)據(jù)收集平均時(shí)延為目標(biāo)，建立多目標(biāo)一對(duì)多充電及數(shù)據(jù)收集模型。在ME攜帶的行駛能量和充電能量不足的前提下，設(shè)計(jì)路徑規(guī)劃策略和均衡化充電策略，并改進(jìn)多目標(biāo)蟻群算法對(duì)該文問(wèn)題進(jìn)行求解。實(shí)驗(yàn)結(jié)果表明，該文算法在多種場(chǎng)景下的目標(biāo)值、Pareto解的數(shù)量、Pareto解集的均勻性、分布范圍等性能指標(biāo)均優(yōu)于NSGA-II算法。
- 無(wú)線(xiàn)可充電傳感器網(wǎng)絡(luò) /
- 數(shù)據(jù)收集 /
- 多目標(biāo)優(yōu)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

圖 1 網(wǎng)絡(luò)模型

下載: 全尺寸圖片幻燈片

圖 2 路徑規(guī)劃示意圖

下載: 全尺寸圖片幻燈片

圖 3 3種場(chǎng)景下VN-MOAC算法和NSGA-II算法性能統(tǒng)計(jì)盒狀圖

下載: 全尺寸圖片幻燈片

圖 4 50個(gè)節(jié)點(diǎn)和100個(gè)節(jié)點(diǎn)下算法的收斂性對(duì)比

下載: 全尺寸圖片幻燈片

圖 5 VN-MOAC算法在不同迭代次數(shù)下的Pareto前沿

下載: 全尺寸圖片幻燈片

表 1 VN-MOAC算法和NSGA-II算法計(jì)算結(jié)果比較

指標(biāo)	網(wǎng)絡(luò)場(chǎng)景	$\phi $ (%)		$\overline {\Delta \tau } $ (s)		${\rm{RN}}$		${\rm{SP}}$		$M_3^*$
指標(biāo)	網(wǎng)絡(luò)場(chǎng)景	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II	VN-MOAC	NSGA-II
最優(yōu)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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