IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v80y2022i1d10.1007_s11235-022-00883-5.html
   My bibliography  Save this article

Hybrid algorithm optimization for coverage problem in wireless sensor networks

Author

Listed:
  • Han-Dong Jia

    (Shandong University of Science and Technology)

  • Shu-Chuan Chu

    (Shandong University of Science and Technology)

  • Pei Hu

    (Shandong University of Science and Technology
    Nanyang Institute of Technology)

  • LingPing Kong

    (VŠB-Technical University of Ostrava)

  • XiaoPeng Wang

    (Shandong University of Science and Technology)

  • Václav Snášel

    (VŠB-Technical University of Ostrava)

  • Tong-Bang Jiang

    (Dalian Maritime University)

  • Jeng-Shyang Pan

    (Shandong University of Science and Technology
    Chaoyang University of Technology)

Abstract

With the continuous development of evolutionary computing, many excellent algorithms have emerged, which are applied in all walks of life to solve various practical problems. In this paper, two hybrid fish, bird and insect algorithms based on different architectures are proposed to solve the optimal coverage problem in wireless sensor networks. The algorithm combines the characteristics of three algorithms, namely, particle swarm optimization algorithm, Phasmatodea population evolution algorithm and fish migration optimization algorithm. The new algorithm has the advantages of the three algorithms. In order to prove the effectiveness of the algorithm, we first test it on 28 benchmark functions. The results show that the two hybrid fish, bird and insect algorithms with different architectures have significant advantages. Then we apply the proposed algorithm to solve the coverage problem of wireless sensor networks through experimental simulation. The experimental results show the advantages of our proposed algorithm and prove that our proposed hybrid fish, bird and insect algorithm is suitable for solving the coverage problem of wireless sensor networks.

Suggested Citation

  • Han-Dong Jia & Shu-Chuan Chu & Pei Hu & LingPing Kong & XiaoPeng Wang & Václav Snášel & Tong-Bang Jiang & Jeng-Shyang Pan, 2022. "Hybrid algorithm optimization for coverage problem in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(1), pages 105-121, May.
    • Handle: RePEc:spr:telsys:v:80:y:2022:i:1:d:10.1007_s11235-022-00883-5
    DOI: 10.1007/s11235-022-00883-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-022-00883-5
    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/s11235-022-00883-5?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. Akhilesh Panchal & Rajat Kumar Singh, 2021. "EHCR-FCM: Energy Efficient Hierarchical Clustering and Routing using Fuzzy C-Means for Wireless Sensor Networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(2), pages 251-263, February.
    2. Jeng-Shyang Pan & Zhenyu Meng & Shu-Chuan Chu & Hua-Rong Xu, 2017. "Monkey King Evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(3), pages 351-364, July.
    3. Turki Ali Alghamdi, 2020. "Energy efficient protocol in wireless sensor network: optimized cluster head selection model," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(3), pages 331-345, July.
    4. Kavita Jaiswal & Veena Anand, 2021. "A QoS aware optimal node deployment in wireless sensor network using Grey wolf optimization approach for IoT applications," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(4), pages 559-576, December.
    Full references (including those not matched with items on IDEAS)

    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. Jinhai Song & Zhiyong Zhang & Kejing Zhao & Qinhai Xue & Brij B. Gupta, 2023. "A Novel CNN-LSTM Fusion-Based Intrusion Detection Method for Industrial Internet," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 17(1), pages 1-18, January.
    2. Hilary I. Okagbue & Muminu O. Adamu & Timothy A. Anake & Ashiribo S. Wusu, 2019. "Nature inspired quantile estimates of the Nakagami distribution," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 517-541, December.
    3. Kumar Prateek & Nitish Kumar Ojha & Fahiem Altaf & Soumyadev Maity, 2023. "Quantum secured 6G technology-based applications in Internet of Everything," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 315-344, February.
    4. Chandra Naik & Pushparaj D. Shetty, 2022. "FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(4), pages 559-571, April.
    5. Akhilesh Panchal & Rajat Kumar Singh, 2021. "EOCGS: energy efficient optimum number of cluster head and grid head selection in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(1), pages 1-13, September.
    6. Shaha Al-Otaibi & Venkatesan Cherappa & Thamaraimanalan Thangarajan & Ramalingam Shanmugam & Prithiviraj Ananth & Sivaramakrishnan Arulswamy, 2023. "Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks," Sustainability, MDPI, vol. 15(7), pages 1-16, March.

    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:telsys:v:80:y:2022:i:1:d:10.1007_s11235-022-00883-5. 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.