Novel optimization algorithm for cable RFID positioning technology

doi:10.16265/j.cnki.issn1003-3033.2023.S2.0024

Abstract

Abstract:

To solve the RFID positioning problem in the intelligent management of cables in the open-pit coal mine, a method to optimize the RFID positioning accuracy based on the IWOA was proposed. RFID technology was used to locate the electronic tag and read the information, and RSSI value was utilized to calculate the measurement distance between the reader and the tag. Then the distance error was optimized through the improved whale algorithm, and the positioning accuracy of the system was reflected by the position coordinate error value selected by iterative search. The results show that the average positioning error of the system optimized by the IWOA is 0.024 1 m. Compared with the traditional LANDMARC, differential evolution (DE)-RFID, and whale optimization algorithm (WOA)-RFID positioning methods, the average error is smaller, and the positioning accuracy is increased to 97.8%, which can more accurately locate the electronic tag.

Key words: radio frequency identification (RFID), positioning technology, optimization algorithm, improved whale optimization algorithm (IWOA), received signal strength indication (RSSI), reference tags, adaptability

CLC Number:

X936

JIAO Xiaoliang, GUO Anbin, ZHANG Jianyu, SU Hongjun. Novel optimization algorithm for cable RFID positioning technology[J]. China Safety Science Journal, 2023, 33(S2): 182-187.

Figures/Tables 7

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References 17

[1]	朱峥. 全球电力电缆管理模式发展及展望[J]. 自动化应用, 2023, 64(2): 129-131.
	ZHU Zheng. Research and prospect of global power cable management mod[J]. Automation Application, 2023, 64(2): 129-131.
[2]	苏红俊, 闫凌, 许洪. 基于B/S架构的电缆信息管理系统[J]. 露天采矿技术, 2022, 37(6): 100-104.
	SU Hongjun, YAN Ling, XU Hong. Cable information management system based on B/S architecture[J]. Opencast Mining Technology, 2022, 37(6): 100-104.
[3]	董文斌, 张建宇, 徐楚雯. RFID技术在矿用电缆信息管理中的应用[J]. 露天采矿技术, 2022, 37(1): 42-45.
	DONG Wenbin, ZHANG Jianyu, XU Chuwen. Application of RFID technology in mining cable information management[J]. Opencast Mining Technology, 2022, 37(1): 42-45.
[4]	刘佳玲. 基于RFID的定位系统[J]. 电子技术与软件工程, 2018, 24(16): 29.
[5]	LADDA M, BEKRRIS K E, RUDYS A P, et al. On the feasibility of using wireless ether net for indoor localization[J]. IEEE Transactions on Robotics & Automation, 2004, 20(3):555-559.
[6]	张园园, 孙麟, 刘明. 梯形模糊事故树预测算法的改进及应用[J]. 中国安全科学学报, 2020, 30(11):156-161. doi: 10.16265/j.cnki.issn 1003-3033.2020.11.023
	ZHANG Yuanyuan, SUN Lin, LIU Ming. Improvement and application of trapezoidal fuzzy fault tree prediction algorithm[J]. China Safety Science Journal, 2020, 30(11):156-161. doi: 10.16265/j.cnki.issn 1003-3033.2020.11.023
[7]	BOUET M, PUJOLLE G. RFID in health systems: applications, challenges, and perspectives[J]. Annals of Telecommunications Annales Des Télécommunications, 2010, 65(9):497-503.
[8]	刘博深, 甄佳奇. 一种基于RSSI的改进VIRE算法[J]. 黑龙江大学工程学报, 2018, 9(2):70-74.
	LIU Boshen, ZHEN Jiaqi. An improved VIRE algorithm for indoor positioning based on RSSI[J]. Journal of Engineering of Heilongjiang University, 2018, 9(2):70-74.
[9]	徐杨杰, 王艳, 严大虎, 等. 基于Newton插值与混合灰狼优化SVR的RFID定位算法[J]. 系统仿真学报, 2017, 29(9): 1 921-1 929.
	XU Yangjie, WANG Yan, YAN Dahu, et al. Indoor positioning algorithm for rfid based on newton interpolation and support vector regression optimized by hybridizing grey wolf optimization[J]. Journal of System Simulation, 2017, 29(9): 1 921-1 929.
[10]	孔红山, 郁滨. 一种基于BP神经网络的VIRE改进算法研究[J]. 系统仿真学报, 2018, 30(9):3586-3 595.
	KONG Hongshan, YU Bin. An improved algorithm of VIRE based on BP neural network[J]. Journal of System Simulation, 2018, 30(9): 3 586-3 595.
[11]	林兴贵, 周诚, 宋鋆. 盾构隧道水平运输不安全行为的物联网预报警研究[J]. 中国安全科学学报, 2014, 24(11):109-115.
	LIN Xinggui, ZHOU Cheng, SONG Jun. Iot-based early-warning system of unsafe behaviors for horizontal-transportation in shield tunnel[J]. China Safety Science Journal, 2014, 24(11):109-115.
[12]	BILODEAU J S, BOUZOUANE A, BOUCHARD B, et al. An experimental comparative study of RSSI-based positioning algorithms for passive RFID localization in smart environments[J]. Journal of Ambient Intelligence & Humanized Computing, 2017, 9(5):1327-1 343.
[13]	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95:51-67. doi: 10.1016/j.advengsoft.2016.01.008
[14]	孙晶. 基于深度学习和位置指纹的RFID室内定位算法研究[D]. 长春: 吉林大学, 2018.
	SUN Jing. Study on RFID indoor positioning algorithms based on deep learning and location fingerprint[D]. Changchun: Jilin University, 2018.
[15]	李丽, 郑嘉利, 王哲, 等. 基于异步优势动作评价的RFID室内定位算法[J]. 计算机科学, 2020, 47(2):233-238. doi: 10.11896/jsjkx.190100070
	LI Li, ZHENG Jiali, WANG Zhe, et al. RFID indoor positioning algorithm based on asynchronous advantage actor-critic[J]. Computer Science, 2020, 47(2):233-238. doi: 10.11896/jsjkx.190100070
[16]	宋宁佳, 崔英花. 基于GA-GRNN的RFID室内定位算法[J]. 计算机工程, 2019, 45(11):298-302,308. doi: 10.19678/j.issn.1000-3428.0052838
	SONG Ningjia, CUI Yinghua. RFID indoor positioning algorithm based on GA-GRNN[J]. Computer Engineering, 2019, 45(11):298-302,308. doi: 10.19678/j.issn.1000-3428.0052838
[17]	付小辉. 基于载波相位的RFID定位关键技术研究[D]. 重庆: 重庆邮电大学, 2020.
	FU Xiaohui. Research on the key technology of RFID positioning based on carrier phase[D] Chongqing: Chongqing University of Posts and Telecommunications, 2020.

相关参数名称	参数取值
鲸鱼个体总数SearchAgents_no	36
迭代次数Max_iteration	50
鲸鱼搜索区域	10 m×10 m

定位算法	MSE	RMSE	MAE
LANDMARC 算法	1.115 8	1.197 1	3.446 4
WOA算法	0.325 3	0.337 0	2.735 2
DE算法	0.046 3	0.050 6	2.626 5
IWOA算法	0.001 3	0.001 7	2.431 6