WSN particle swarm optimization routing algorithm based on nuclide monitoring of uranium tailings pond

doi:10.16265/j.cnki.issn1003-3033.2023.10.1772

Abstract

Abstract:

A energy-balanced clustering algorithm for WSN based on PSO (EBPSO) for uranium tailings storage nuclide monitoring has been proposed to extend the network lifespan of existing WSN. Firstly, in the cluster head(CH) election stage, the particle swarm optimization algorithm was used to select the most reasonable CH. Taking into account parameters such as sensor node energy, distance between CH and distance between CH and base stations, the most reasonable CH nodes were selected and applied to uranium tailings pond nuclide monitoring, thus forming a wireless sensor network. Secondly, at the data transmission stage, a technique for selecting forwarding nodes was proposed. Then, to reduce energy consumption, an energy threshold re-clustering strategy was applied. Finally, the performance of clustering routing algorithm based on PSO algorithm optimized fuzzy C-means (POFCA), low energy adaptive clustering hierarchy(LEACH), and energy-efficient unequal clustering(EEUC) method were compared. The simulation test results demonstrate that compared with POFCA, LEACH, and EEUC, the network lifetime of EBPSO algorithm is increased by 1.7%, 24.7% and 9.2% respectively. It successfully extends the network lifespan and is ideal for the application situation of uranium tailings pond nuclide monitoring.

Key words: uranium tailings pond, radionuclide monitoring, wireless sensor network(WSN), particle swarm optimization(PSO), routing algorithm

ZHOU Zixiang, YU Xiuwu, PENG Wei, LIU Yong. WSN particle swarm optimization routing algorithm based on nuclide monitoring of uranium tailings pond[J]. China Safety Science Journal, 2023, 33(10): 207-213.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Tab.1

Experimental parameter setting

参数名称	节点数目	BS位置	网络规模	最大轮数r_max	数据信息/bit	控制信息/bit	a
取值	100	(50,50)	100 m×100 m	2 000	4 000	200	0.5
参数名称	节点初始能量E₀/J	$ε f s$ /pJ·(bit^-1·m^-4)	$ε m p$ /pJ·(bit^-1·m^-4)	E_t/(nJ·bit^-1)	d₀/m	C₁	C₂
取值	0.6	10	0.0013	50	87	2	2

Tab.1

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

References 14

[1]	余修武, 谢晓永, 梁北孔, 等. 基于粒子群优化和巷道分区的深井WSN定位算法[J]. 中国安全科学学报, 2019, 29(2):166-171.
	YU Xiuwu, XIE Xiaoyong, LIANG Beikong, et al. A deep mine WSN localization algorithm based on both particle swarm optimization and tunnel partition[J]. China Safety Science Journal, 2019, 29(2):166-171.
[2]	RAO P C S, JANA P K, BANKA H. A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks[J]. Wireless Networks, 2017, 23: 2005-2020. doi: 10.1007/s11276-016-1270-7
[3]	HEINZELMAN W R, CHANDRAKASAN A, BALAKRISHNAN H. Energy-efficient communication protocol for wireless microsensor networks[C]. Proceedings of the 33^rd Annual Hawaii International Conference on System Sciences,IEEE, 2000:DOI:10.1109/HICSS.2000.926982.
[4]	YOUNIS O, FAHMY S. HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks[J]. IEEE Transactions on Mobile Computing, 2004, 3(4): 366-379. doi: 10.1109/TMC.2004.41
[5]	LINDSEY S, RAGHAVENDRA C S. PEGASIS: power-efficient gathering in sensor information systems[C]. Proceedings, IEEE Aerospace Conference, 2002:DOI:10.1109/AERO.2002.1035242.
[6]	LI Chengfa, YE Mao, CHEN Guihai, et al. An energy-efficient unequal clustering mechanism for wireless sensor networks[C]. IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005: DOI:10.1109/MAHSS.2005.1542849.
[7]	TARHANI M, KAVIAN Y S, SIAVOSHI S. SEECH: scalable energy efficient clustering hierarchy protocol in wireless sensor networks[J]. IEEE Sensors Journal, 2014, 14(11): 3944-3954. doi: 10.1109/JSEN.2014.2358567
[8]	孙爱晶, 李世昌, 张艺才. 基于 PSO 优化模糊 C 均值的 WSN 分簇路由算法[J]. 通信学报, 2021, 42(3): 91-99. doi: 10.11959/j.issn.1000-436x.2021053
	SUN Aijing, LI Shichang, ZHANG Yicai. WSN clustering routing algorithm based on PSO optimized fuzzy C-means[J]. Journal of Communications, 2021, 42 (3): 91-99.
[9]	武小年, 张楚芸, 张润莲, 等. WSN中基于改进粒子群优化算法的分簇路由协议[J]. 通信学报, 2019, 40(12):114-123. doi: 10.11959/j.issn.1000-436x.2019241
	WU Xiaonian, ZHANG Chuyun, ZHANG Runlian, et al. Clustering routing protocol based on improved PSO algorithm in WSN[J]. Journal on Communications, 2019, 40(12):114-123. doi: 10.11959/j.issn.1000-436x.2019241
[10]	朱开磊, 孙爱晶. 基于布谷鸟优化 K 均值的 WSN 分簇路由算法[J]. 物联网学报, 2022, 6(1): 73-81. doi: 10.11959/j.issn.2096-3750.2022.00257
	ZHU Kailei, SUN Aijing. WSN clustering routing algorithm based on Cuckoo Search algorithm optimized K-means[J]. Chinese Journal on Internet of Things, 2022, 6 (1): 73-81.
[11]	黄磊, 苗放, 王梦雪. 区域尾矿库安全监测预警系统设计与构建[J]. 中国安全科学学报, 2013, 23(12): 146-152.
	HUANG Lei, MIAO Fang, WANG Mengxue. Designing and setting up a monitoring and early warning system for tailings ponds in a region[J]. China Safety Science Journal, 2013, 23 (12): 146-152.
[12]	KENNEDY J, EBERHART R. Particle swarm optimization[C]. Proceedings of ICNN' 95-International Conference on Neural Networks, IEEE, 1995:DOI:10.1109/ICNN.1995.488968.
[13]	余修武, 李佩, 刘永, 等. 一种基于动态竞争半径的非均匀分簇路由算法[J]. 传感技术学报, 2021, 34(3):400-406.
	YU Xiuwu, LI Pei, LIU Yong, et al. A non-uniform clustering routing algorithm based on dynamic competitive radius[J]. Chinese Journal of Sensors and Actuators, 2021, 34(3):400-406.
[14]	PACHLOR R, SHRIMANKAR D. LAR-CH: a cluster-head rotation approach for sensor networks[J]. IEEE Sensors Journal, 2018, 18(23): 9821-9828. doi: 10.1109/JSEN.2018.2872065