Article: Entropy dragonfly optimisation-based cluster head selection and deep learning clone node detection for wireless sensor network Journal: International Journal of Information and Computer Security (IJICS) 2025 Vol.26 No.4 pp.394 - 421 Abstract: In a network with no fixed infrastructure, a wireless sensor network comprises mobile nodes that communicate with one another using wireless networks. Node clone attacks can exploit WSN. Attackers take control of one sensor node, create numerous copies with the same identity (ID), and spread these copies throughout the network. Clones appear authentic since they have all the credentials of a real member. The clustering of WSN nodes, a fundamental process, aims to achieve load balancing and prolonged network lifetime. This study created the energy efficient sleep awake aware protocol, which improves energy efficiency and chooses the appropriate CH based on node energy. The volume of data and distance between nodes and the base station determine WSN energy efficiency. The dragonfly optimisation technique boosts network performance. Deep learning clone node detection has been introduced to identify WSN clones. Clone identification is essential for preventing cloning assaults. A cheap identity verification approach can find clones locally and globally. Final validation of the suggested approach is done extensively with network simulator 2. (NS2). After the performance analysis, the scheme's effectiveness is assessed by comparing planned and present methods. Inderscience Publishers - linking academia, business and industry through research

Title: Entropy dragonfly optimisation-based cluster head selection and deep learning clone node detection for wireless sensor network

Authors: K. Jane Nithya; K. Shyamala

Addresses: Department of Computer Science, Ethiraj College for Women, Chennai, Tamil Nadu, India; Affiliated to: University of Madras, India ' Department of Computer Science, Dr. Ambedkar Government Arts College (Autonomous), Chennai, Tamil Nadu, India; Affiliated to: University of Madras, India

Abstract: In a network with no fixed infrastructure, a wireless sensor network comprises mobile nodes that communicate with one another using wireless networks. Node clone attacks can exploit WSN. Attackers take control of one sensor node, create numerous copies with the same identity (ID), and spread these copies throughout the network. Clones appear authentic since they have all the credentials of a real member. The clustering of WSN nodes, a fundamental process, aims to achieve load balancing and prolonged network lifetime. This study created the energy efficient sleep awake aware protocol, which improves energy efficiency and chooses the appropriate CH based on node energy. The volume of data and distance between nodes and the base station determine WSN energy efficiency. The dragonfly optimisation technique boosts network performance. Deep learning clone node detection has been introduced to identify WSN clones. Clone identification is essential for preventing cloning assaults. A cheap identity verification approach can find clones locally and globally. Final validation of the suggested approach is done extensively with network simulator 2. (NS2). After the performance analysis, the scheme's effectiveness is assessed by comparing planned and present methods.

Keywords: clone attack; energy efficient sleep awake aware; EESAA; entropy dragonfly optimisation; EDFO; deep learning clone node detection; DLCND; wireless sensor network; WSN; quality of service.

DOI: 10.1504/IJICS.2025.146526

International Journal of Information and Computer Security, 2025 Vol.26 No.4, pp.394 - 421

Received: 18 Feb 2024
Accepted: 05 Jul 2024
Published online: 02 Jun 2025 *

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Title: Entropy dragonfly optimisation-based cluster head selection and deep learning clone node detection for wireless sensor network

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