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Node positioning based on IPSO-IDE in WSNs

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Abstract

Due to the unreasonable connectivity of wireless sensor networks, there are errors in calculating the distance between the location-unaware nodes and the location-aware nodes. Therefore, a novel method combining improved particle swarm optimization and improved differential evolution (IPSO-IDE) is proposed. Firstly, the learning factor of the particle swarm optimization method is modified, and the disturbance term is added to prevent premature, thus strengthening its exploration ability. Then, the adaptive mutation and crossover mechanism are applied to the differential evolution method so that each chromosome can mutate and cross adaptively with individual fitness and average fitness, to improve its exploitation ability. Finally, the improved particle swarm optimization (IPSO) and differential evolution method (IDE) are run in parallel in the node positioning model. After fixed number of iterations, the optimal individual information of the two methods is shared to obtain the IPSO-IDE method. Therefore, IPSO-IDE has the advantages of both. The results show that the IPSO-IDE algorithm not only has some advantages in time complexity, but also improves the positioning accuracy by 27.2%, 8.7%, 4.5% and 5.7% respectively, compared with particle swarm optimization with a variable neighborhood algorithm, modified particle swarm optimization algorithm, hybrid particle swarm optimization and differential evolution algorithm and artificial bee colony algorithm.

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References

  1. Srirangarajan S, Tewfik AH, Luo Z (2008) Distributed sensor network positioning using SOCP relaxation. IEEE Trans Wirel Commun 7(12):4886–4895

    Article  Google Scholar 

  2. Biswas P, Liang T, Toh K, Ye Y, Wang T (2006) Semidefinite programming approaches for sensor network positioning with noisy distance measurements. IEEE Trans Autom Sci Eng 3(4):360–371

    Article  Google Scholar 

  3. Patwari N, Ash JN, Kyperountas S, Hero AO, Moses RL, Correal NS (2005) Locating the nodes: cooperative positioning in wireless sensor networks. IEEE Signal Process Mag 22(4):54–69

    Article  ADS  Google Scholar 

  4. Vicente D, Tomic S, Beko M, et al. (2017) “Performance analysis of a distributed method for target positioning in wireless sensor networks using hybrid measurements in a connection failure scenario’’ in 2017 International Young Engineers Forum (YEF-ECE). IEEE, 2017

  5. Chowdhury Tjs et al (2016) Advances on positioning techniques for wireless sensor networks: a survey. Comput Netw 110:284–305

    Article  Google Scholar 

  6. Zhongdong Hu, Kang Z, Zhendong W (2018) Positioning method of wireless sensor network nodes based on saddle terrain. Chin J Sens Actuators 22(4):1753–1757

    Google Scholar 

  7. Nayyar A, Singh R (2017) Ant colony optimization (ACO) based routing protocols for wireless sensor networks (WSNs): a survey. Int J Adv Comput Sci Appl 8:2

    Google Scholar 

  8. Zheng JG, Wu CD, Chu H, et al. (2010) Positioning method based on RSSI and distance geometry constrain for wireless sensor network. In 2010 International Conference on Electrical and Control Engineering. pp. 2836–2839 2010

  9. Lei-bing YAN, Yin LU, Ye-rong ZHANG (2018) Unified orthogonal cubature kalman tracking method based on TOA/TDOA. Acta Electron Sin 48(8):1989–1996

    Google Scholar 

  10. Niculescu D and Badri Nath (2003) “Ad hoc positioning system (APS) using AOA,” in Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies, 3:1734–1743, 2003

  11. Yang J, Cai Y, Tang D et al (2018) A novel centralized range-free static node positioning method with memetic method and lévy flight. Sensors 19(14):3242–3271

    Article  ADS  Google Scholar 

  12. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):29–41

    Article  CAS  Google Scholar 

  13. Eberhart R and Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. pp. 39–43, 1995

  14. Yang X and Suash Deb (2009) "Cuckoo Search via Lévy flights," in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). pp. 210–214, 2009

  15. Zhihua Cui et al (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber physical systems. J Parallel Distrib Comput 103:42–52

    Article  Google Scholar 

  16. Gumaida BF, Luo J (2019) A hybrid particle swarm optimization with a variable neighborhood search for the positioning enhancement in wireless sensor networks. Appl Intell 49(10):3539–3557

    Article  Google Scholar 

  17. Zhou F, Chen S (2018) “DV-Hop Node localization Method Based on Improved Particle Swarm Optimization.” in International Conference in Communications. Springer, Singapore, 2018

  18. Zhang L, Ji W, Zhang Y (2014) Node localization method for wireless sensor networks based on hybrid optimization of differential evolution and particle swarm algorithm. Open Autom Control Syst J 6(1):621–628

    Article  Google Scholar 

  19. Kanwar V, Kumar A (2021) DV-Hop localization methods for displaced sensor nodes in wireless sensor network using PSO. Wirel Netw 27(1):91–102

    Article  Google Scholar 

  20. Chai QW et al (2020) A parallel WOA with two communication strategies applied in DV-Hop localization method. EURASIP J Wirel Commun Netw 2020(1):1–10

    Article  MathSciNet  Google Scholar 

  21. Rajakumar R et al (2017) GWO-LPWSNs: Grey wolf optimization algorithm for node localization problem in wireless sensor networks. J Comput Netw Commun 2017:1–10

    Article  Google Scholar 

  22. Arora S, Singh S (2017) Node localization in wireless sensor networks using butterfly optimization algorithm. Arab J Sci Eng 42(8):3325–3335

  23. Qingguo Z, Jinghua W, Wei Z (2016) Wireless sensor network node positioning based on differential evolution. Comput Eng 39(11):78–82

  24. Junya Y, Yuhua Q, Huafeng Li, Shangcai Ma (2017) Node positioning based on multipath distance and neural network in WSNs. Comput Sci 44(8):71–75

    Google Scholar 

  25. Annepu V, Rajesh A (2020) Implementation of an efficient artificial bee colony method for node localization in unmanned aerial vehicle assisted wireless sensor networks. Wirel Pers Commun 114(3):2663–2680

    Article  Google Scholar 

  26. Ciuprina G, Ioan D, Munteanu I (2002) Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans Magn 38(2):1037–1040

    Article  ADS  Google Scholar 

  27. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  28. Yi W et al (2015) A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl Intell 42(4):642–660

    Article  Google Scholar 

  29. Ho-Huu V, Vo-Duy T, Luu-Van T et al (2016) Optimal design of truss structures with frequency constraints using improved differential evolution method based on an adaptive mutation scheme. Autom Constr 68:81–94

    Article  Google Scholar 

  30. Duan, Qiqi, et al. (2017) "Visualizing the search dynamics in a high-dimensional space for a particle swarm optimizer." Asia-Pacific Conference on Simulated Evolution and Learning. Springer, Cham. 994–1002

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Authors and Affiliations

  1. The School of Mathematics and Computer Science, Xinyang Vocational Technical College, Xinyang, 464000, China

    Zheng Lv

  2. The School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou, 310023, China

    Fangfang Qiang

  3. The School of Electronic and Information, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yu zhan

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  1. Zheng Lv
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  2. Fangfang Qiang
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  3. Yu zhan
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Correspondence to Fangfang Qiang.

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Lv, Z., Qiang, F. & zhan, Y. Node positioning based on IPSO-IDE in WSNs. Evol. Intel. 17, 483–492 (2024). https://doi.org/10.1007/s12065-022-00782-3

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