联合充电和数据收集的WCE多目标路径规划算法

doi:10.11959/j.issn.1000-436x.2018216

Abstract

Abstract:

Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.

Key words: wireless rechargeable sensor network, joint energy replenishment and data collection, path planning

CLC Number:

TP393

Zhenchun WEI,Renhao SUN,Zengwei LYU,Jianghong HAN,Lei SHI,Junyi XU. Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization[J]. Journal on Communications, 2018, 39(10): 22-33.

Figures/Tables 14

指标	算法	最优值	最差值	平均值	中间值
Φ	ES-MOAC	86.19%	56.02%	71.28%	71.43%
	NSGA-II	85.42%	52.21%	68.19%	67.28%
$\bar{Δ τ} / s$	ES-MOAC	1 380.42	8 971.85	4 404.85	4 411.43
	NSGA-II	1 541.12	9 341.52	4 642.56	4 566.49
ER	ES-MOAC	49	25	36	35
	NSGA-II	34	15	26	26
SP	ES-MOAC	2 416.57	25.41	765.86	487.96
	NSGA-II	2 845.11	60.43	1 025.25	833.30
$M_{3}^{*}$	ES-MOAC	5 655.37	8.98	512.88	121.83
	NSGA-II	6 520.66	9.82	539.42	129.01

指标	算法	最优值	最差值	平均值	中间值
Φ	ES-MOAC	94.02%	75.83%	84.56%	84.76%
	NSGA-II	92.56%	73.60%	82.61%	83.58%
$\bar{Δ τ} / s$	ES-MOAC	3 631.86	9 962.58	6 021.37	6 268.39
	NSGA-II	3 858.73	9 968.41	6 356.45	6 243.50
ER	ES-MOAC	43	28	37	41
	NSGA-II	29	15	20	21
SP	ES-MOAC	40.31	765.63	367.54	540.51
	NSGA-II	43.06	876.50	502.47	685.70
$M_{3}^{*}$	ES-MOACNSGA-II	348.59356.08	11.7212.45	138.63142.45	159.40163.84

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节点序号	坐标/m	节点序号	坐标/m
1	（342,112）	11	（609,579）
2	（198,192）	12	（827,504）
3	（758,119）	13	（757,734）
4	（921,320）	14	（287,643）
5	（946,742）	15	（253,922）
6	（854,847）	16	（103,777）
7	（680,979）	17	（559,490）
8	（656,769）	18	（277,396）
9	（128,490）	19	（54,489）
10	（486,813）	20	（465,280）

单轮行驶路径	WCE行驶能量开销/J	WCE充电能量开销/J
0-1-13-4-12-0	18 098.38	13 574.76
0-2-20-17-11-8-0	18 469.46	15 462.04
0-18-16-10-7-0	18 783.94	16 601.46
0-3-6-5-0	17 503.66	16 945.39
0-19-9-14-15-0	17 462.01	17 492.82

单轮行驶路径	WCE行驶能量开销/J	WCE充电能量开销/J
0-19-9-16-15-10-14-18-2-0	19 982.67	5 487.37
0-1-20-17-11-8-7-0	19 540.78	5 647.44
0-3-4-12-5-6-13-0	19 734.08	5 782.05

Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization

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