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<p>Wireless Sensor Networks (WSNs) consist of many geographically distributed sensor nodes that sense, record, and communicate environmental data to a central base station. However, the limited energy resources of sensor nodes lead to frequent energy depletion, thereby reducing network stability and lifetime. This energy constraint remains a critical challenge in the efficient deployment of WSNs. To overcome these issues, this study introduces a hybrid optimisation technique that integrates the Improved Butterfly Optimisation Algorithm (I-BOA) with Ant Colony Optimisation (ACO) for energy-efficient clustering and intelligent routing. The I-BOA algorithm selects the optimal cluster heads (CHs) based on residual energy, node degree, centrality, and distance metrics, ensuring balanced energy consumption across the network. Meanwhile, the ACO algorithm determines the most efficient data-forwarding paths from CHs to relay nodes and, ultimately, to the base station, by considering distance, residual energy, and node degree. The proposed I-BOA + ACO model is evaluated against conventional algorithms, including MOPSO, HSA-PSO, M-LEACH, and DEEC, under identical network conditions. Simulation results reveal that the proposed method achieves a 23.8% improvement in network lifetime, an 18.6% enhancement in throughput, and significant reductions in energy consumption, latency, and packet loss. These outcomes confirm the effectiveness of the proposed I-BOA + ACO framework in extending the operational lifetime and overall efficiency of WSNs.</p>

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Energy-efficient optimization of wireless sensor networks using the butterfly algorithm in an enhanced architecture with relay nodes

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Sanjeev Kumar,
  • Richa Agrawal

摘要

Wireless Sensor Networks (WSNs) consist of many geographically distributed sensor nodes that sense, record, and communicate environmental data to a central base station. However, the limited energy resources of sensor nodes lead to frequent energy depletion, thereby reducing network stability and lifetime. This energy constraint remains a critical challenge in the efficient deployment of WSNs. To overcome these issues, this study introduces a hybrid optimisation technique that integrates the Improved Butterfly Optimisation Algorithm (I-BOA) with Ant Colony Optimisation (ACO) for energy-efficient clustering and intelligent routing. The I-BOA algorithm selects the optimal cluster heads (CHs) based on residual energy, node degree, centrality, and distance metrics, ensuring balanced energy consumption across the network. Meanwhile, the ACO algorithm determines the most efficient data-forwarding paths from CHs to relay nodes and, ultimately, to the base station, by considering distance, residual energy, and node degree. The proposed I-BOA + ACO model is evaluated against conventional algorithms, including MOPSO, HSA-PSO, M-LEACH, and DEEC, under identical network conditions. Simulation results reveal that the proposed method achieves a 23.8% improvement in network lifetime, an 18.6% enhancement in throughput, and significant reductions in energy consumption, latency, and packet loss. These outcomes confirm the effectiveness of the proposed I-BOA + ACO framework in extending the operational lifetime and overall efficiency of WSNs.