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<p>The emergence of Internet-of-Things devices and expanding data processing demands have led to the popularization of edge computing. But energy efficiency challenges and security problems also remain difficult in the dynamic and distributed environment. In this paper, a novel Hybrid Election-based Ladybug Beetle Optimization (ELBO-H) is proposed to address these issues. Leveraging concepts of Election-based Optimization Algorithm (EBOA) and Ladybug Beetle Optimization (LBO), is designed to enhance the energy efficiency, robustness and security scheme for powered edge computing network. The proposed ELBO-H utilizes this algorithm in the design of security-aware edge computing architecture with minimum energy consumption among devices and edge nodes. The proposed ELBO-H can reduce the following performance metrics, energy consumption, network lifetime, throughput and delay by 83.3%, 76.4%, 53% and 62% respectively whilst guaranteeing an average attack detection rate of up to 94.28% compared to IB-SEC, G-BHO, DEEC-KSA, and CPSO for a simulation experiment on IoT edge network that is based on MATLAB with network size of 200 nodes each covering area in distance range of (200&#xa0;m×200&#xa0;m). The numerical results demonstrate that the proposed method can achieve better performance in terms of energy efficiency with satisfied security requirement. The ELBO-H method outperforms IB-SEC, G-BHO, DEEC-KSA and CPSO methods yields an average detection rate of 78.23%, 72.45%, 74.89% and 52.67%. These results reveal that the proposed approach offers a practical scheme for secure data transmission in IIoT-based edge systems which have severe energy and latency limitations.</p>

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An energy efficient authentication method for IoT-enabled edge computing using hybrid metaheuristic optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Biswa Mohan Sahoo,
  • Vaibhav Sharma,
  • Vikas Kumar Tiwari,
  • Raj Kumar Parida,
  • Mohit Kumar

摘要

The emergence of Internet-of-Things devices and expanding data processing demands have led to the popularization of edge computing. But energy efficiency challenges and security problems also remain difficult in the dynamic and distributed environment. In this paper, a novel Hybrid Election-based Ladybug Beetle Optimization (ELBO-H) is proposed to address these issues. Leveraging concepts of Election-based Optimization Algorithm (EBOA) and Ladybug Beetle Optimization (LBO), is designed to enhance the energy efficiency, robustness and security scheme for powered edge computing network. The proposed ELBO-H utilizes this algorithm in the design of security-aware edge computing architecture with minimum energy consumption among devices and edge nodes. The proposed ELBO-H can reduce the following performance metrics, energy consumption, network lifetime, throughput and delay by 83.3%, 76.4%, 53% and 62% respectively whilst guaranteeing an average attack detection rate of up to 94.28% compared to IB-SEC, G-BHO, DEEC-KSA, and CPSO for a simulation experiment on IoT edge network that is based on MATLAB with network size of 200 nodes each covering area in distance range of (200 m×200 m). The numerical results demonstrate that the proposed method can achieve better performance in terms of energy efficiency with satisfied security requirement. The ELBO-H method outperforms IB-SEC, G-BHO, DEEC-KSA and CPSO methods yields an average detection rate of 78.23%, 72.45%, 74.89% and 52.67%. These results reveal that the proposed approach offers a practical scheme for secure data transmission in IIoT-based edge systems which have severe energy and latency limitations.