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An energy-adaptive clustering method based on Taguchi-based-GWO optimizer for wireless sensor networks with a mobile sink

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Abstract

In wireless sensor networks (WSNs), balancing energy consumption is always a critical problem. Multi-hop data transmission may cause the nodes around the static sink to exhaust energy prematurely. It can be avoided by introducing the mobile sink (MS) in WSNs. The MS only needs to visit some specific nodes, such as cluster heads (CHs) in the clustering network, to collect data. In this paper, we propose an energy-adaptive clustering method based on Taguchi-based-GWO optimizer (EACM-TGWO). Different from the existing clustering protocols, we develop the optimal number of CHs according to the characteristic of energy consumption in MS-based WSNs. Afterwards, the fitness function is established by combining the residual energy and the average distance within clusters to select CHs. Meanwhile, an adaptive weight factor is introduced to dynamically tune the influence degree of the two factors on the clustering results. Furthermore, a novel meta-heuristic algorithm Taguchi-based grey wolf optimizer (TGWO) is used to search for the optimal CHs set. Compared with grey wolf optimizer (GWO) and some of its improved versions, TGWO is more excellent tested in CEC2017. We also make an elaborate comparison of EACM-TGWO with LEACH-C, EACM-GWO, MSECA, and GAOC. The simulation results indicate that EACM-TGWO is more powerful in terms of balancing energy consumption and saving network energy.

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References

  1. Di Francesco M, Das SK, Anastasi G (2011) Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans. Sensor Netw. (TOSN) 8(1):1–31. https://doi.org/10.1145/1993042.1993049

    Article  Google Scholar 

  2. Li J, Li G-C, Chu S-C, Gao M, Pan J-S (2022) Modified parallel tunicate swarm algorithm and application in 3d WSNS coverage optimization. J Int Technol 23(2):227–244

    Google Scholar 

  3. Khan AW, Abdullah AH, Anisi MH, Bangash JI (2014) A comprehensive study of data collection schemes using mobile sinks in wireless sensor networks. Sensors 14(2):2510–2548. https://doi.org/10.3390/s140202510

    Article  Google Scholar 

  4. Li J, Han Q, Wang W (2022) Characteristics analysis and suppression strategy of energy hole in wireless sensor networks. Ad Hoc Netw 135:102938. https://doi.org/10.1016/j.adhoc.2022.102938

    Article  Google Scholar 

  5. Zhu C, Han G, Zhang H (2017) A honeycomb structure based data gathering scheme with a mobile sink for wireless sensor networks. Peer-to-Peer Netw Appl 10(3):484–499. https://doi.org/10.1007/s12083-016-0496-6

    Article  Google Scholar 

  6. Mehto A, Tapaswi S, Pattanaik K (2020) A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink. Wirel Netw 26(4):2639–2663. https://doi.org/10.1007/s11276-019-02022-6

    Article  Google Scholar 

  7. Mehto A, Tapaswi S, Pattanaik K (2021) Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink. Computing 103(4):707–733. https://doi.org/10.1007/s00607-021-00917-x

    Article  MathSciNet  MATH  Google Scholar 

  8. Han Y, Li G, Xu R, Su J, Li J, Wen G (2020) Clustering the wireless sensor networks: a meta-heuristic approach. IEEE Access 8:214551–214564. https://doi.org/10.1109/ACCESS.2020.3041118

    Article  Google Scholar 

  9. Khan MI, Gansterer WN, Haring G (2013) Static vs mobile sink: the influence of basic parameters on energy efficiency in wireless sensor networks. Comput Commun 36(9):965–978. https://doi.org/10.1016/j.comcom.2012.10.010

    Article  Google Scholar 

  10. Li J, Gao M, Pan J-S, Chu S-C (2021) A parallel compact cat swarm optimization and its application in dv-hop node localization for wireless sensor network. Wirel Netw 27(3):2081–2101. https://doi.org/10.1007/s11276-021-02563-9

    Article  Google Scholar 

  11. Krishnan M, Yun S, Jung YM (2019) Dynamic clustering approach with ACO-based mobile sink for data collection in WSNS. Wirel Netw 25(8):4859–4871. https://doi.org/10.1007/s11276-018-1762-8

    Article  Google Scholar 

  12. Lipare A, Edla DR, Kuppili V (2019) Energy efficient load balancing approach for avoiding energy hole problem in WSN using grey wolf optimizer with novel fitness function. Appl Soft Comput 84:105706. https://doi.org/10.1016/j.asoc.2019.105706

    Article  Google Scholar 

  13. Verma S, Sood N, Sharma AK (2019) Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Appl Soft Comput 85:105788. https://doi.org/10.1016/j.asoc.2019.105788

    Article  Google Scholar 

  14. Chauhan V, Soni S (2020) Mobile sink-based energy efficient cluster head selection strategy for wireless sensor networks. J Ambient Intell Humaniz Comput 11(11):4453–4466. https://doi.org/10.1007/s12652-019-01509-6

    Article  Google Scholar 

  15. Pitchaimanickam B, Murugaboopathi G (2020) A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput Appl 32(12):7709–7723. https://doi.org/10.1007/s00521-019-04441-0

    Article  Google Scholar 

  16. Gupta GP, Saha B (2020) Load balanced clustering scheme using hybrid metaheuristic technique for mobile sink based wireless sensor networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01909-z

    Article  Google Scholar 

  17. Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw 106:102237. https://doi.org/10.1016/j.adhoc.2020.102237

    Article  Google Scholar 

  18. Wei Q, Bai K, Zhou L, Hu Z, Jin Y, Li J (2021) A cluster-based energy optimization algorithm in wireless sensor networks with mobile sink. Sensors 21(7):2523. https://doi.org/10.3390/s21072523

    Article  Google Scholar 

  19. Kathiroli P, Selvadurai K (2021) Energy efficient cluster head selection using improved sparrow search algorithm in wireless sensor networks. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.08.031

    Article  Google Scholar 

  20. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. Proc 33rd Ann Hawaii Int Conf Syst Sci. https://doi.org/10.1109/HICSS.2000.926982

    Article  Google Scholar 

  21. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670. https://doi.org/10.1109/TWC.2002.804190

    Article  Google Scholar 

  22. Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. Pacific Rim Int Conf Artif Intell. https://doi.org/10.1007/978-3-540-36668-3_94

    Article  Google Scholar 

  23. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57. https://doi.org/10.1007/s11721-007-0002-0

    Article  Google Scholar 

  24. Pan J-S, Tsai P-W, Liao Y-B (2010) Fish migration optimization based on the fishy biology. In: 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp 783–786. https://doi.org/10.1109/ICGEC.2010.198

  25. Pan J-S, Hu P, Chu S-C (2021) Binary fish migration optimization for solving unit commitment. Energy 226:120329. https://doi.org/10.1016/j.energy.2021.120329

    Article  Google Scholar 

  26. Song P-C, Chu S-C, Pan J-S, Yang H (2020) Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine. In: 2020 2nd International Conference on Industrial Artificial Intelligence (IAI), pp 1–5. https://doi.org/10.1109/IAI50351.2020.9262236

  27. Pan J-S, Song P-C, Chu S-C, Peng Y-J (2020) Improved compact cuckoo search algorithm applied to location of drone logistics hub. Mathematics 8(3):333. https://doi.org/10.3390/math8030333

    Article  Google Scholar 

  28. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

  29. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  30. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435. https://doi.org/10.1007/s00521-017-3272-5

    Article  Google Scholar 

  31. Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2016/7950348

    Article  Google Scholar 

  32. Long W, Jiao J, Liang X, Tang M (2018) An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng Appl Artif Intell 68:63–80. https://doi.org/10.1016/j.engappai.2017.10.024

    Article  Google Scholar 

  33. Wang YJ, Ma CL (2018) Opposition-based learning differential ion motion algorithm. J Inf Hiding Multimed Signal Process 9(4):987–996

    MathSciNet  Google Scholar 

  34. Yu X, Xu W, Li C (2021) Opposition-based learning grey wolf optimizer for global optimization. Knowl-Based Syst 226:107139. https://doi.org/10.1016/j.knosys.2021.107139

    Article  Google Scholar 

  35. Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with lévy flight for optimization tasks. Appl Soft Comput 60:115–134. https://doi.org/10.1016/j.asoc.2017.06.044

    Article  Google Scholar 

  36. Tsai P-W, Pan J-S, Chen S-M, Liao B-Y (2012) Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst Appl 39(7):6309–6319. https://doi.org/10.1016/j.eswa.2011.11.117

    Article  Google Scholar 

  37. Wang H, Geng Q, Qiao Z (2014) Parameter tuning of particle swarm optimization by using taguchi method and its application to motor design. In: 2014 4th IEEE International Conference on Information Science and Technology, pp 722–726. https://doi.org/10.1109/ICIST.2014.6920579

  38. Zhan Z-H, Zhang J, Li Y, Shi Y-H (2010) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847. https://doi.org/10.1109/TEVC.2010.2052054

    Article  Google Scholar 

  39. Gao W-F, Liu S-Y, Huang L-L (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024. https://doi.org/10.1109/TSMCB.2012.2222373

    Article  Google Scholar 

  40. Li X, Wang J, Yin M (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247. https://doi.org/10.1007/s00521-013-1354-6

    Article  Google Scholar 

  41. Hu J, Chen H, Heidari AA, Wang M, Zhang X, Chen Y, Pan Z (2021) Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection. Knowl-Based Syst 213:106684. https://doi.org/10.1016/j.knosys.2020.106684

    Article  Google Scholar 

  42. Li J, Li Y-X, Tian S-S, Zou J (2019) Dynamic cuckoo search algorithm based on Taguchi opposition-based search. Int J Bio-Insp Comput 13(1):59–69. https://doi.org/10.1504/IJBIC.2019.097728

    Article  Google Scholar 

  43. Tsai J-T, Liu T-K, Chou J-H (2004) Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans Evol Comput 8(4):365–377. https://doi.org/10.1109/TEVC.2004.826895

    Article  Google Scholar 

  44. Feng Z-K, Liu S, Niu W-J, Li B-J, Wang W-C, Luo B, Miao S-M (2020) A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation. Knowl-Based Syst 208:106461. https://doi.org/10.1016/j.knosys.2020.106461

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, Northeast Electric Power University, Jilin, China

    ZhiSheng Wang, JianPo Li & Jeng-Shyang Pan

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu & Jeng-Shyang Pan

  3. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan

    Jeng-Shyang Pan

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Wang, Z., Chu, SC., Li, J. et al. An energy-adaptive clustering method based on Taguchi-based-GWO optimizer for wireless sensor networks with a mobile sink. Computing 105, 1769–1793 (2023). https://doi.org/10.1007/s00607-023-01168-8

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