IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i5p500-d508105.html
   My bibliography  Save this article

Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications

Author

Listed:
  • E. Laxmi Lydia

    (Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam 530049, Andhra Pradesh, India)

  • A. Arokiaraj Jovith

    (Department of Information Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India)

  • A. Francis Saviour Devaraj

    (Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626128, Tamil Nadu, India)

  • Changho Seo

    (Department of Convergence Science, Kongju National University, Gongju 32588, Korea)

  • Gyanendra Prasad Joshi

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

Abstract

Presently, a green Internet of Things (IoT) based energy aware network plays a significant part in the sensing technology. The development of IoT has a major impact on several application areas such as healthcare, smart city, transportation, etc. The exponential rise in the sensor nodes might result in enhanced energy dissipation. So, the minimization of environmental impact in green media networks is a challenging issue for both researchers and business people. Energy efficiency and security remain crucial in the design of IoT applications. This paper presents a new green energy-efficient routing with DL based anomaly detection (GEER-DLAD) technique for IoT applications. The presented model enables IoT devices to utilize energy effectively in such a way as to increase the network span. The GEER-DLAD technique performs error lossy compression (ELC) technique to lessen the quantity of data communication over the network. In addition, the moth flame swarm optimization (MSO) algorithm is applied for the optimal selection of routes in the network. Besides, DLAD process takes place via the recurrent neural network-long short term memory (RNN-LSTM) model to detect anomalies in the IoT communication networks. A detailed experimental validation process is carried out and the results ensured the betterment of the GEER-DLAD model in terms of energy efficiency and detection performance.

Suggested Citation

  • E. Laxmi Lydia & A. Arokiaraj Jovith & A. Francis Saviour Devaraj & Changho Seo & Gyanendra Prasad Joshi, 2021. "Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications," Mathematics, MDPI, vol. 9(5), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:500-:d:508105
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/5/500/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/5/500/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Saddam Aziz & Muhammad Talib Faiz & Adegoke Muideen Adeniyi & Ka-Hong Loo & Kazi Nazmul Hasan & Linli Xu & Muhammad Irshad, 2022. "Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)," Mathematics, MDPI, vol. 10(8), pages 1-23, April.

    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:jmathe:v:9:y:2021:i:5:p:500-:d:508105. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.