IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i12p365-d995737.html
   My bibliography  Save this article

Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

Author

Listed:
  • Ashraf A. Taha

    (Department of Networks and Distributed Systems, Informatics Research Institute, City of Scientific Research and Technological Applications, SRTA-CITY, Alexandria 21934, Egypt)

  • Hagar O. Abouroumia

    (Computer and Communication Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Shimaa A. Mohamed

    (Department of Networks and Distributed Systems, Informatics Research Institute, City of Scientific Research and Technological Applications, SRTA-CITY, Alexandria 21934, Egypt)

  • Lamiaa A. Amar

    (Department of Networks and Distributed Systems, Informatics Research Institute, City of Scientific Research and Technological Applications, SRTA-CITY, Alexandria 21934, Egypt)

Abstract

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption.

Suggested Citation

  • Ashraf A. Taha & Hagar O. Abouroumia & Shimaa A. Mohamed & Lamiaa A. Amar, 2022. "Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm," Future Internet, MDPI, vol. 14(12), pages 1-17, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:365-:d:995737
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/12/365/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/12/365/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shayesteh Tabatabaei, 2022. "Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-23, March.
    2. Efe Francis Orumwense & Khaled Abo-Al-Ez, 2022. "On Increasing the Energy Efficiency of Wireless Rechargeable Sensor Networks for Cyber-Physical Systems," Energies, MDPI, vol. 15(3), pages 1-18, February.
    3. Jiquan Wang & Zhiwen Cheng & Okan K. Ersoy & Panli Zhang & Weiting Dai & Zhigui Dong, 2018. "Improvement Analysis and Application of Real-Coded Genetic Algorithm for Solving Constrained Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-16, June.
    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. James Deva Koresh Hezekiah & Karnam Chandrakumar Ramya & Mercy Paul Selvan & Vishnu Murthy Kumarasamy & Dipak Kumar Sah & Malathi Devendran & Sivakumar Sabapathy Arumugam & Rajagopal Maheswar, 2023. "Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks," Energies, MDPI, vol. 16(20), pages 1-19, October.

    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. Guangjiao Chen & Guifen Chen, 2022. "A Method of Relay Node Selection for UAV Cluster Networks Based on Distance and Energy Constraints," Sustainability, MDPI, vol. 14(23), pages 1-14, December.

    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:12:p:365-:d:995737. 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.