IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i4p1362-d1585815.html
   My bibliography  Save this article

Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment

Author

Listed:
  • Haitham Assiri

    (Department of Computer Science, College of Engineering and Computer Sciences, Jazan University, Jazan 45142, Saudi Arabia)

Abstract

As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives.

Suggested Citation

  • Haitham Assiri, 2025. "Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment," Sustainability, MDPI, vol. 17(4), pages 1-24, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1362-:d:1585815
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/4/1362/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/4/1362/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Iyad Katib & Mahmoud Ragab, 2023. "Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment," Mathematics, MDPI, vol. 11(8), pages 1-16, 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. Kavitha Ramaswami Jothi & Balamurugan Vaithiyanathan, 2024. "Developing a Hybrid Approach with Whale Optimization and Deep Convolutional Neural Networks for Enhancing Security in Smart Home Environments’ Sustainability Through IoT Devices," Sustainability, MDPI, vol. 16(24), pages 1-29, December.
    2. Fatmah Y. Assiri & Mahmoud Ragab, 2023. "Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    3. Rayed AlGhamdi, 2023. "Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    4. Walid I. Khedr & Ameer E. Gouda & Ehab R. Mohamed, 2023. "P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture," Mathematics, MDPI, vol. 11(16), pages 1-36, August.

    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:jsusta:v:17:y:2025:i:4:p:1362-:d:1585815. 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.