IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7021-d1256743.html
   My bibliography  Save this article

Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks

Author

Listed:
  • James Deva Koresh Hezekiah

    (Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India)

  • Karnam Chandrakumar Ramya

    (Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore 641008, Tamil Nadu, India)

  • Mercy Paul Selvan

    (Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, India)

  • Vishnu Murthy Kumarasamy

    (Department of Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore 641042, Tamil Nadu, India)

  • Dipak Kumar Sah

    (Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Malathi Devendran

    (Department of Electronics and Communication Engineering, Kongu Engineering College, Erode 638060, Tamil Nadu, India)

  • Sivakumar Sabapathy Arumugam

    (Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore 641048, Tamil Nadu, India)

  • Rajagopal Maheswar

    (Department of Electronics and Communication Engineering, Centre for IoT and AI (CITI), KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India)

Abstract

Wireless Sensor Networks (WSN) play a major role in various applications, yet maintaining energy efficiency remains a critical challenge due to their limited energy availability. Network lifetime is one of the primary parameters for analyzing the performance of a WSN. This proposed work aims to improve the network lifetime of a WSN by enhancing its energy utilization through the Enhanced Monkey Search Algorithm (E-MSA). The E-MSA provides an optimum solution for this issue by finding a better routing decision by analyzing the available energy on the nodes and the distance between the source and destination. Additionally, a Class Topper Optimization (CTO) algorithm is also included in the work for determining an efficient node to be the cluster head and lead cluster head. In this technique, the data packets are collected by the lead cluster head from the other cluster heads for sending the information in a sequential manner to the base station for reducing data loss. A simulation model is implemented in the NS2 platform with 700 nodes in a 300 × 300 square meter area with 0.5 J of energy to each node for finding the efficiency of the proposed E-MSA with CTO algorithm over the traditional On-Demand Distance Vector (ODV) and Destination-Sequenced Distance Vector (DSDV) approaches. The experimental outcome indicates that the proposed work can reach a maximum lifetime of 1579 s which is comparatively better than the ODV and DSDV approaches by 212 and 358 s, respectively. Similarly, a packet delivery ratio of 79% is achieved with a throughput of 0.85 Mbps along with a delay of 0.48 s for the operation of all 700 nodes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7021-:d:1256743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7021/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Rajnish Kler & Roshan Gangurde & Samariddin Elmirzaev & Md Shamim Hossain & Nhut V. T. Vo & Tien V. T. Nguyen & P. Naveen Kumar & Peiman Ghasemi, 2022. "Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-8, October.
    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. Rameez Shaikh & Alex Stojcevski & Mehdi Seyedmahmoudian & Jaideep Chandran, 2024. "A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm," Sustainability, MDPI, vol. 16(11), pages 1-27, May.
    2. Ghazi M. Magableh, 2023. "Evaluating Wheat Suppliers Using Fuzzy MCDM Technique," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    3. Nan Zhao & Chun Feng, 2023. "Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    4. Grzegorz Nawalany & Miroslav Zitnak & Małgorzata Michalik & Jana Lendelova & Paweł Sokołowski, 2024. "Impact of the Location and Energy Carriers Used on Greenhouse Gas Emissions from a Building," Energies, MDPI, vol. 17(19), pages 1-26, September.
    5. Sina Davoudi & Peter Stasinopoulos & Nirajan Shiwakoti, 2024. "Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry," Sustainability, MDPI, vol. 16(16), pages 1-67, August.
    6. Lihong Xu & Jiawei You & Hongliang Yuan, 2023. "Real-Time Parametric Path Planning Algorithm for Agricultural Machinery Kinematics Model Based on Particle Swarm Optimization," Agriculture, MDPI, vol. 13(10), pages 1-17, October.
    7. Shaojian Qu & Xinqi Li & Chang Liu & Xufeng Tang & Zhisheng Peng & Ying Ji, 2023. "Two-Stage Robust Programming Modeling for Continuous Berth Allocation with Uncertain Vessel Arrival Time," Sustainability, MDPI, vol. 15(13), pages 1-30, July.

    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:jeners:v:16:y:2023:i:20:p:7021-:d:1256743. 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.