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<p>Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things infrastructure, are widely applied in fields such as environmental monitoring, industrial control, and security surveillance. However, due to the limited and irreplaceable energy of sensor nodes, energy efficiency remains a core challenge in WSN research. To address this issue, a Multi-Objective Butterfly Clustering Optimization routing Algorithm (MBCO) is proposed. This algorithm innovatively combines butterfly foraging behavior with dynamic clustering, optimizing cluster head selection by simulating both the dispersive and centralized foraging behaviors of butterflies. An adaptive weight clustering mechanism based on node density and residual energy is designed to achieve network load balancing. A hybrid intra-cluster data fusion strategy is proposed, dynamically adjusting data aggregation methods according to the urgency of events. Additionally, a cross-cluster coordination mechanism is introduced to support inter-cluster load migration and resource sharing. Simulation results show that MBCO, while ensuring quality of service, significantly reduces energy consumption by 6.69 J, extends the useful life of the network by 83.05 rounds, increases packet delivery rate by 5.1%, and decreases communication delay by 67.34 ms compared to the relative average values of FDAM, EOMR-X, and EE-MO. This greatly enhances energy efficiency and provides a new paradigm for large-scale wireless sensor network deployments.</p>

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A novel multi objective energy efficient clustering optimization scheme based on heuristic intelligence for wireless sensor networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Chuchu Rao,
  • Mingqi Kan,
  • Peng Zhou,
  • Yeshen Lan

摘要

Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things infrastructure, are widely applied in fields such as environmental monitoring, industrial control, and security surveillance. However, due to the limited and irreplaceable energy of sensor nodes, energy efficiency remains a core challenge in WSN research. To address this issue, a Multi-Objective Butterfly Clustering Optimization routing Algorithm (MBCO) is proposed. This algorithm innovatively combines butterfly foraging behavior with dynamic clustering, optimizing cluster head selection by simulating both the dispersive and centralized foraging behaviors of butterflies. An adaptive weight clustering mechanism based on node density and residual energy is designed to achieve network load balancing. A hybrid intra-cluster data fusion strategy is proposed, dynamically adjusting data aggregation methods according to the urgency of events. Additionally, a cross-cluster coordination mechanism is introduced to support inter-cluster load migration and resource sharing. Simulation results show that MBCO, while ensuring quality of service, significantly reduces energy consumption by 6.69 J, extends the useful life of the network by 83.05 rounds, increases packet delivery rate by 5.1%, and decreases communication delay by 67.34 ms compared to the relative average values of FDAM, EOMR-X, and EE-MO. This greatly enhances energy efficiency and provides a new paradigm for large-scale wireless sensor network deployments.