IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01542-9.html
   My bibliography  Save this article

Swarm intelligence based centralized clustering: a novel solution

Author

Listed:
  • Cosmena Mahapatra

    (University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University
    Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University)

  • Ashish Payal

    (University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University)

  • Meenu Chopra

    (Vivekananda Institute of Professional Studies, Guru Gobind Singh Indraprastha University)

Abstract

Recent evolutions of MEMS technology, digital electronics, and wireless communication technologies have made smart environments possible; especially through the apparition or incorporation of sensors. Despite being small-sized, the sensors have paved the way for data collection in the environments where they are applied; including luminosity, gas presence, water content, humidity, pressure, and temperature. This research aims to pragmatically optimize Wireless Sensor Network Localization and Network Coverage issues using nature-inspired algorithms. The specific objective is to establish an optimal nature-inspired algorithm and comparing it with other algorithms regarding the capacity to achieve manufacturing optimization in large WSNs, especially in relation to the parameters of high scalability, data delivery rate, and low-energy consumption. Also, the study seeks to determine the extent to which swarm intelligence (SI)-based centralized clustering solutions (optimal nature-inspired algorithms), compared to other approaches, might optimize the WSN features of localization and network coverage. To determine the solutions’ performance, the study involved three scenarios. In scenario 1, the network operational time and the stability of SI-based algorithms were investigated for large WSNs that had the minimum heterogeneity. Imperative to note is that the WSNs on focus had different numbers of nodes, which included 500, 300, and 100. In scenario 2, the motivation was to investigate the SI-based WSN protocols in relation to the parameters of packet delivery, energy conception, and network lifetime for large WSNs. In scenario 3, the performance of SI-based solutions over large WSNs was compared to that which had been reported previously for other algorithms; with the target parameters of comparison being attributes such as packet delivery, energy conception, and network lifetime. From the findings, this study established that SI-based centralized clustering solutions are not only more recent but also exhibit superior performance compared to other algorithms; with the parameters of the amount of data delivered to the BS, energy consumption and scalability on the focus.

Suggested Citation

  • Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01542-9
    DOI: 10.1007/s10845-020-01542-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01542-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01542-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hao Liu & Yue Wang & Liangping Tu & Guiyan Ding & Yuhan Hu, 2019. "A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2407-2433, August.
    2. Seyed Mohsen Mousavi & Najmeh Alikar & Madjid Tavana & Debora Di Caprio, 2019. "An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1175-1194, March.
    3. Libin Han & Keyi Xing & Xiao Chen & Fuli Xiong, 2018. "A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1083-1096, June.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xuejun Zhao & Yong Qin & Changbo He & Limin Jia, 2022. "Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 185-201, January.
    2. Saneh Lata Yadav & R. L. Ujjwal, 2021. "Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2083-2098, December.
    3. Galina Samigulina & Zarina Samigulina, 2022. "Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1433-1450, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.
    2. Jiaxing Wang & Sibin Gao & Zhejun Tang & Dapeng Tan & Bin Cao & Jing Fan, 2023. "A context-aware recommendation system for improving manufacturing process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1347-1368, March.
    3. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.
    4. G. Cherif & E. Leclercq & D. Lefebvre, 2023. "Scheduling of a class of partial routing FMS in uncertain environments with beam search," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 493-514, February.
    5. Xinnian Wang & Keyi Xing & Chao-Bo Yan & Mengchu Zhou, 2019. "A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems," Complexity, Hindawi, vol. 2019, pages 1-14, June.
    6. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    7. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    9. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    10. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    11. Ling, Chunyan & Yang, Lechang & Feng, Kaixuan & Kuo, Way, 2023. "Survival signature based robust redundancy allocation under imprecise probability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    12. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    13. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    14. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    15. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    16. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    17. Ming Zhang & Qianwen Huang & Sihan Liu & Huiying Li, 2019. "Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment," Sustainability, MDPI, vol. 11(23), pages 1-27, November.
    18. Ruidi Chen & Ioannis Ch. Paschalidis, 2022. "Robust Grouped Variable Selection Using Distributionally Robust Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1042-1071, September.
    19. Darya Pyatkina & Tamara Shcherbina & Vadim Samusenkov & Irina Razinkina & Mariusz Sroka, 2021. "Modeling and Management of Power Supply Enterprises’ Cash Flows," Energies, MDPI, vol. 14(4), pages 1-17, February.
    20. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01542-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.