基于改进遗传算法的电厂粉尘监测节点覆盖控制研究

doi:10.16265/j.cnki.issn1003-3033.2024.09.0960

摘要/Abstract

摘要：

为有效降低粉尘环境监测中存在盲区和管控缺失的风险,优化火电厂粉尘环境监测系统的节点覆盖控制,延长无线传感器网络(WSN)寿命,提出一种基于改进遗传算法的节能优化方法。首先构建基于节点覆盖率、节点布设总能耗和节点通信传输总能耗的网络覆盖质量目标函数;然后针对传统遗传算法存在局部最优和编码重复的问题,提出整数编码的染色体组合方案、自适应调节交叉和变异概率的方法,以及精英保留策略;最后通过仿真对比分析,确定优化后的节点数量和分布方案。结果表明:改进的遗传算法显著提高了收敛速度,所需迭代次数减少至20次,适应度值优化52.18%;在节点部署和覆盖研究中,优化后的节点数量为42个,覆盖率达97.28%,节点休眠率为76.19%,有效提升了火电厂粉尘环境监测系统的节能效果。

关键词: 改进遗传算法, 电厂粉尘, 环境监测, 节点覆盖控制, 无线传感器网络(WSN), 精英保留策略

Abstract:

In order to effectively reduce the risk of blind zones and lack of control in dust environment monitoring, optimize the node coverage control of the dust environment monitoring system in thermal power plants, and prolong the lifetime of WSN, an energy-saving optimization method based on improved genetic algorithms was proposed. Firstly, based on node coverage, total energy consumption of node deployment and total energy consumption of node communication and transmission, the network coverage quality objective function was constructed. Then, aiming at the problems of the local optimization and coding duplication existing in traditional genetic algorithms, the chromosome combination scheme of integer coding, the adaptive adjustment method of crossover and mutation probability and the elite retention strategy were proposed. Finally, the simulation comparison and analysis were performed to determine the optimized node number and distribution scheme. The results show that the improved genetic algorithm significantly improves the convergence speed. The number of iterations required is reduced to 20, and the fitness value is optimized by 52.18%. In the node deployment and coverage study, the optimized number of nodes is 42, the coverage rate is 97.28%, and the node dormancy rate is 76.19%, which effectively improves the energy-saving effect of the dust environmental monitoring system in the thermal power plant.

Key words: improved genetic algorithm, power plant dust, environment monitoring, node coverage control, wireless sensor network (WSN), elite retention strategy

中图分类号:

X924.4安全控制技术

王博, 商宇航, 姚立超, 蒋永清. 基于改进遗传算法的电厂粉尘监测节点覆盖控制研究[J]. 中国安全科学学报, 2024, 34(9): 121-130.

WANG Bo, SHANG Yuhang, YAO Lichao, JIANG Yongqing. Research on power plant dust monitoring node coverage control based on improved genetic algorithm[J]. China Safety Science Journal, 2024, 34(9): 121-130.

图/表 14

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表1

图11

图12

图13

参考文献 21

[1]	MEHMOOD G, KHAN M S, WAHEED A, et al. An efficient and secure session key management scheme in wireless sensor network[J]. Complexity, 2021: DOI:10.1155/2021/6577492.
[2]	贾青青, 贾纹纹, 张秦, 等. 基于NB-IoT的典型气体污染物实时监测系统设计[J]. 传感器与微系统, 2022, 41(9):102-104,108.
	JIA Qingqing, JIA Wenwen, ZHANG Qin, et al. Real-time monitoring system design for typical air pollutants based on NB-IoT[J]. Transducer and Microsystem Technologies, 2022, 41(9): 102-104, 108.
[3]	王明华, 黄畅, 王彦, 等. 无线传感器网络节点重部署研究进展[J]. 计算机应用研究, 2023, 40(4):978-986.
	WANG Minghua, HUANG Chang, WANG Yan, et al. Research progress of nodes redeployment in wireless sensor network[J]. Application Research of Computers, 2023, 40(4): 978-986.
[4]	MINH T N, KEITH A T. Optimizing number of clusters for energy saving purpose in wireless sensor networks[C]. International Conference on Engineering Research and Applications, 2018: 455-461.
[5]	李守玉, 何庆, 陈俊. 改进平衡优化器算法的WSN覆盖优化[J]. 计算机应用研究, 2022, 39(4):1168-1172,1 189.
	LI Shouyu, HE Qing, CHEN Jun. Improved equilibrium optimizer algorithm for WSN coverage optimization[J]. Application Research of Computers, 2022, 39(4): 1168-1172, 1 189.
[6]	郭超, 杨宇轩, 胡荣磊, 等. 基于粒子群算法的WSN覆盖优化[J]. 计算机应用研究, 2020, 37(4):1170-1173,1 182.
	GUO Chao, YANG Yuxuan, HU Ronglei, et al. WSN coverage optimization based on particle swarm optimization[J]. Application Research of Computers, 2020, 37(4): 1170-1173, 1 182.
[7]	KRISHNANAND K N, DEBASISH G. Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations[J]. Robotics and Autonomous Systems, 2008, 56(7): 549-569.
[8]	NEEDELL D, VERSHYNIN R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2009, 9(3): 318-334.
[9]	NEEDELL D, VERSHYNIN R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Processing, 2010, 4(2): 310-316.
[10]	余修武, 彭威, 余员琴, 等. 基于SSO的铀尾矿库无线传感器网络定位算法[J]. 中国安全科学学报, 2023, 33(4):84-90. doi: 10.16265/j.cnki.issn1003-3033.2023.04.0264
	YU Xiuwu, PENG Wei, YU Yuanqin, et al. Localization algorithm for uranium tailings reservoir based on SSO in wireless sensor network[J]. China Safety Science Journal, 2023, 33(4): 84-90. doi: 10.16265/j.cnki.issn1003-3033.2023.04.0264
[11]	郭瑞, 徐广璐. 基于信息融合与GA-SVM的煤矿瓦斯浓度多传感器预测模型研究[J]. 中国安全科学学报, 2013, 23(9):33-38.
	GUO Rui, XU Guanglu. Research on coal mine gas concentration multi-sensor prediction model based on information fusion and GA-SVM[J]. China Safety Science Journal, 2013, 23(9): 33-38.
[12]	李静, 莫思敏. 基于改进遗传算法的深度神经网络优化研究[J]. 计算机工程与科学, 2021, 43(8):1503-1511.
	LI Jing, MO Simin. Optimizing deep neural networks using a modified genetic algorithm[J]. Computer Engineering & Science, 2021, 43(8): 1503-1511.
[13]	ZHOU Xiaoping, HUANG Xiaocheng, ZHAO Xiaofeng. Optimization of the critical slip surface of three-dimensional slope by using an improved genetic algorithm[J]. International Journal of Geomechanics, 2020, 20(8): DOI:org/10.1061/(ASCE)GM.1943-5622.0001747.
[14]	余成成, 徐巍, 钟宇超, 等. 基于多通信半径和改进遗传算法的DV-Hop定位[J]. 仪表技术与传感器, 2023(2):99-103,120.
	YU Chengcheng, XU Wei, ZHONG Yuchao, et al. DV-Hop localization based on multiple communication radius and improved genetic algorithm[J]. Instrument Technique and Sensor, 2023(2): 99-103, 120.
[15]	李汝宁, 冯兴, 姚仰平, 等. 基于改进遗传算法的机场场道施工方案多目标优化[J/OL]. 北京航空航天大学学报:1-11[2024-08-30].https://doi.org/10.13700/j.bh.1001-5965.2022.0893.
	LI Runing, FENG Xing, YAO Yangping, et al. Multi objective optimization of airport runway construction scheme based on improved genetic algorithm[J/OL]. Journal of Beijing University of Aeronautics and Astronautics, 1-11[2024-08-30].https://doi.org/10.13700/j.bh.1001-5965.2022.0893.
[16]	WEI Huixian, LIU Jia. Computer mathematical modeling based on the improved genetic algorithm and mobile computing[J]. Wireless Communications & Mobile Computing, 2022, 2021: DOI:org/10.1155/2021/1584435.
[17]	向征, 袁博轩, 刘玥琳. 基于多目标融合及改进遗传算法的终端区进场协同排序[J]. 科学技术与工程, 2022, 22(29):13104-13 113.
	XIANG Zheng, YUAN Boxuan, LIU Yuelin. Collaborative sequencing of arrival flights in terminal area based on multi-objective fusion and improved genetic algorithm[J]. Science Technology and Engineering, 2022, 22(29): 13 104-13 113.
[18]	周天清, 胡海琴, 曾新亮. NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理[J]. 电子与信息学报, 2022, 44(9):3014-3023.
	ZHOU Tianqing, HU Haiqin, ZENG Xinliang. Cooperative computation offloading and resource management based on improved genetic algorithm in NOMA-MEC systems[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3014-3023.
[19]	HAMZA M, MUHAMMAD B, ABUBAKAR S A. An improved genetic algorithm based power system stabilizer for power system stabilization[C]. 2019 IEEE AFRICON, 2019: 1-5.
[20]	SHAHROKH V, MOHAMMADREZA E, SEYED E D. Integration of geographic and hierarchical routing protocols for energy saving in wireless sensor networks with mobile sink[J]. Wireless Networks, 2019, 25(5): 2953-2961.
[21]	ALEXANDER T. Energy saving routing metric for aggregate low rate wireless sensor networks[J]. Wireless Networks, 2019, 26(3): 2037-2050.

序号	仿真参数	取值
1	节点数量	100
2	分布面积/ m²	100×100
3	基站位置	(50, 175)
4	初始能量/ J	0.005
5	光电子器件能量损耗/(pJ·bit^-1)	4
6	能量系数/ (pJ·bit^-1·m^-2)	4
7	扫描角度/(°)	60
8	接收数据能量损耗/(pJ·bit^-1)	60
9	数据融合能耗/ (nJ·bit^-1)	5
10	包长/bit	4 000
11	控制包长/bit	200