Random mating mayfly algorithm for RFID network planning

doi:10.19682/j.cnki.1005-8885.2022.0010

The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (5): 40-50.doi: 10.19682/j.cnki.1005-8885.2022.0010

Special Issue: Special Topic on Artificial Intelligence of Things

Previous Articles Next Articles

Random mating mayfly algorithm for RFID network planning

Xie Xiaode, Zheng Jiali , Lin Zihan, He Siyi, Feng Minyu

Received:2021-05-06 Revised:2021-12-03 Online:2022-10-31 Published:2022-10-28
Contact: Jia-Li ZHENG E-mail:zjl@gxu.edu.cn
Supported by:
National Natural Science Foundation of China;Natural Science Foundation of Guangxi Province, China

Abstract

Abstract:

In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.

Key words: radio frequency identification (RFID)| RFID network planning (RNP)| reader deployment| mayfly algorithm (MA)| random mating

CLC Number:

TP301.6

Xie Xiaode Zheng Jiali Lin Zihan He Siyi Feng Minyu. Random mating mayfly algorithm for RFID network planning[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(5): 40-50.

References

[1] CHEN Y H, HORNG S J, RUN R S, et al. A novel anti-collision algorithm in RFID systems for identifying passive tags. IEEE Transactions on Industrial Informatics, 2010, 6(1): 105-121.

[2] KROHN A, ZIMMER T, BEIGL M, et al. Collaborative sensing in a retail store using synchronous distributed jam signaling. Proceedings of the 3rd International Conference on Pervasive Computing, 2005, May 8-13, Munich, Germany. LNISA 3468. Berlin, Germany: Springer, 2005: 237-254.

[3] ZAHRAN E G, ARAFA A A, SALEH H I, et al. A self learned invasive weed-mixed biogeography based optimization algorithm for RFID network planning. Wireless Networks, 2020, 26(3): 1-19.

[4] KE W C, LIU B H, TSAI M J. Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete. IEEE Transactions on Computers, 2007, 56(5): 710-715.

[5] GUAN Q, LIU Y, YANG Y P, et al. Genetic approach for network planning in the RFID systems. Proceedings of the 2006 International Conference on Intelligent Systems Design & Applications ( ISDA’06 ). 2006, Oct 16, Jinan, China. Piscataway, NJ, USA: IEEE, 2006: 567-572.

[6] KENNEDY J, EBERHART R. Particle swarm optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks (ICNN’95): Vol 4, 1995, Nov 27-Dec 1, Perth, Australia. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.

[7] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69(3): 46-61.

[8] ZERVOUDAKIS K, TSAFARAKIS S. A mayfly optimization algorithm. Computers & Industrial Engineering, 2020, 145: Article 106559.

[9] CHEN H N, ZHU Y L, HU K Y, et al. RFID network planning using a multi-swarm optimizer. Journal of Network and Computer Applications, 2011, 34(3): 888-901.

[10] GONG Y J, SHEN M, ZHANG J, et al. Optimizing RFID network planning by using a particle swarm optimization algorithm with redundant reader elimination. IEEE Transactions on Industrial Informatics, 2012, 8(4): 900-912.

[11] JABALLAH A, MEDDEB A. A new variant of Cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem. Wireless Networks, 2019, 25 (4): 1585-1604.

[12] ZHAO C X, WU C Z, CHAI J, et al. Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Applied Soft Computing, 2017, 55: 549-564.

[13] ZHANG T, LIU J. An efficient and fast kinematics-based algorithm for RFID network. Computer Networks, 2017, 121: 13-24. [14] YUAN C C, CHEN H N, SHEN J, et al. Indicator-based multi-objective adaptive bacterial foraging algorithm for RFID network planning. Cluster Computing, 2019, 22(5): 12649-12657.

[15] HASNAN K, TALIB N H, NAWAWI A. Analysis of gradient-based Cuckoo search for the large scale optimal RFID network planning. Journal of Physics: Conference Series, 2019, 1150(1): Article 012008.

[16] MA L B, WANG X W, HUANG M, et al. Two-level master-slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(5): 861-880.

[17] JABALLAH A, MEDDEB A. A new algorithm based CSP framework for RFID network planning. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2): 2905-2914.

[18] STRUMBERGER I, TUBA E, BACANIN N, et al. Modified monarch butterfly optimization algorithm for RFID network planning. Proceedings of the 6th International Conference on Multimedia Computing and Systems ( ICMCS’18 ), 2018, May 10-12, Rabat, Morocco. Piscataway, NJ, USA: IEEE, 2018: 1-6.

[19] TUBA M, BACANIN N. Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. Proceedings of the 2015 IEEE Congress on Evolutionary Computation ( CEC’15), 2015, May 25-28, Sendai, Japan. Piscataway, NJ, USA: IEEE, 2015: 499-506.

[20] MA L B, HU K Y, ZHU Y L, et al. Cooperative artificial bee colony algorithm for multi-objective RFID network planning. Journal of Network and Computer Applications, 2014, 42: 143-62.

[21] ZAHRAN E G, ARAFA A A, SALEH H I, et al. Biogeography based optimization algorithm for efficient RFID reader deployment. Proceedings of the 13th International Conference on Computer Engineering and Systems ( ICCES’18 ), 2018, Dec 18-19, Cairo, Egypt. Piscataway, NJ, USA: IEEE, 2018: 454-459.

[22] SURIYA A, PORTER J D. Genetic algorithm based approach for RFID network planning. Proceedings of the 2014 IEEE Region 10 Conference ( TENCON’14 ), 2014, Oct 22-25, Bangkok, Thailand. Piscataway, NJ, USA: IEEE, 2014: 1-5.

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

Random mating mayfly algorithm for RFID network planning

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 5

Recommended Articles

Metrics

Comments

[1]	Niu Yaohui, Li Xiuping, Zhao Wenyu. Review of reader antennas for UHF RFID systems [J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(5): 72-92.
[2]	Lin Zihan, Zheng Jiali, Xie Xiaode, Feng Minyu, He Siyi. RFID network planning based on improved brain storm optimization algorithm [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(5): 30-39.
[3]	Quan Yixuan, Zheng Jiali, Xie Xiaode, Lin Zihan, Luo Wencong. Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(6): 91-102.
[4]	. Research on robust multi-period inventory inaccuracy based on RFID technology [J]. Acta Metallurgica Sinica(English letters), 2015, 22(5): 32-40.
[5]	. Electromagnetic effects of nearby NaCl solution on RFID tags based on dynamic measurement system [J]. Acta Metallurgica Sinica(English letters), 2015, 22(5): 49-55.