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Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN

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Abstract

The widespread use of hierarchical wireless sensor networks (HWSN) in various industries, including environmental monitoring, healthcare, and intrusion detection, has made the development of trust among sensor nodes (SNs) essential for enhancing security, dependability, and productive cooperation. However, the existing techniques for HWSN trust management are ineffective because of their poor reliability, increased communication overheads, inaccurate cluster head (CH) selection, etc. The large-scale energy-aware trust optimization (LS-EATO) algorithm is proposed for CH selection and malicious node (MN) detection to avoid these issues. A harmonic search genetic algorithm (HGA) is initially used to select the CH based on energy, trust, distance, and density. By considering trust value, this method avoids the selection of an MN node as the CH. Afterward, MNs are detected using the energy-aware intra and inter-cluster trust (EAIICT) estimation model, which depends on two modules, such as direct trust (DT) and indirect trust (IDT) at the inter-cluster level and intra-cluster level. During this process, the estimation of DT depends upon the communication trust and the data trust. Then the estimation of IDT is based on the feedback trust. This model calculates trust in distributed and centralized ways to find the MNs. Additionally, by considering residual energy (RE) in the trust computation, this estimation approach prevents choosing nodes with low energy as the trustworthy node. Furthermore, the proposed LS-EATO technique is simulated in MATLAB and compared to the existing approaches to show the effectiveness of the proposed approach. The experimental analysis represents the trustworthiness of the proposed scheme in terms of communication overhead, detection accuracy, detection rate, and energy consumption for different network sizes.

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Authors and Affiliations

  1. Department of Computer Science, Mansarovar Global University, Billkisganj, Sehore, Madhya Pradesh, 466001, India

    Rahul Das & Mona Dwivedi

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  1. Rahul Das
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  2. Mona Dwivedi
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Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RD, MD. The first draft of the manuscript was written by RD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: RD; Methodology: RD; Formal analysis and investigation: RD, MD; Writing—original draft preparation: RD; Writing—review and editing: RD; Supervision: MD.

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Correspondence to Rahul Das.

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Das, R., Dwivedi, M. Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN. J Reliable Intell Environ 10, 55–71 (2024). https://doi.org/10.1007/s40860-022-00200-6

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  • DOI: https://doi.org/10.1007/s40860-022-00200-6

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