IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i2p56-d749549.html
   My bibliography  Save this article

Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

Author

Listed:
  • Venushini Rajendran

    (Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia)

  • R Kanesaraj Ramasamy

    (Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia)

  • Wan-Noorshahida Mohd-Isa

    (Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia)

Abstract

The Internet of Things (IoT) is now expanding and becoming more popular in most industries, which leads to vast growth in cloud computing. The architecture of IoT is integrated with cloud computing through web services. Recently, Dynamic Web Service Composition (DWSC) has been implemented to fulfill the IoT and business processes. In recent years, the number of cloud services has multiplied, resulting in cloud services providing similar services with similar functionality but varying in Quality of Services (QoS), for instance, on the response time of web services; however, existing methods are insufficient in solving large-scale repository issues. Bio-inspired algorithm methods have shown better performance in solving the large-scale service composition problems, unlike deterministic algorithms, which are restricted. Thus, an improved eagle strategy algorithm method is proposed to increase the performance that directly indicates an improvement in computation time in large-scale DWSC in a cloud-based platform and on both functional and non-functional attributes of services. By proposing the improved bio-inspired method, the computation time can be improved, especially in a large-scale repository of IoT.

Suggested Citation

  • Venushini Rajendran & R Kanesaraj Ramasamy & Wan-Noorshahida Mohd-Isa, 2022. "Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach," Future Internet, MDPI, vol. 14(2), pages 1-14, February.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:2:p:56-:d:749549
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/2/56/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/2/56/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fateh Seghir & Abdellah Khababa, 2018. "A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1773-1792, December.
    2. Feng-I Chung & Chia Lun Lo, 2018. "Service-Oriented Architecture Application in Long-Term Care Institution: A Case Study on an Information System Project Based on the Whole Person Concept in Taiwan," International Journal of Computing Sciences Research, Step Academic, vol. 1(3), pages 1-21, January.
    3. S. Bharath Bhushan & Pradeep C. H. Reddy, 2018. "A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition," International Journal of Web Services Research (IJWSR), IGI Global, vol. 15(2), pages 1-20, April.
    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. Yinan Wu & Gongzhuang Peng & Hongwei Wang & Heming Zhang, 2019. "A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks," Complexity, Hindawi, vol. 2019, pages 1-20, April.
    2. Hongbin Wang & Yang Ding & Hanchuan Xu, 2024. "Particle swarm optimization service composition algorithm based on prior knowledge," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 35-53, January.

    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:gam:jftint:v:14:y:2022:i:2:p:56-:d:749549. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.