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<p>Routing algorithms are effectively designed to decrease energy consumption and increase communication reliability to enhance the lifespan and performance of Wireless Sensor Networks (WSNs). Routing and clustering are important for sensor nodes to regulate energy consumption and allocate workload. However, individual nodes have limited power resources, and WSNs are dynamic and establish longer network lifetimes, which continues to be a major problem. To resolve these challenges, the Energy-Efficient Binary Sculptor Artificial Protozoa Optimization (EE-BSAPO) algorithm, a sophisticated routing protocol, is designed. The suggested protocol integrates the Binary Sculptor Artificial Protozoa Optimization method with an improved fuzzy logic-based clustering mechanism to enhance multi-hop routing and cluster formation. To enhance the cluster head selection and route discovery, based on distance, communication latency, trust value, and residual energy. Based on various simulations, EE-BSAPO significantly enhances network performance as compared to existing routing methods. In particular, its average end-to-end latency measured a very low 9.36 ms, its average throughput reached 75.24%, and its average network lifespan is 73.81%. The model is suitable for WSN because energy supplies are limited, based on its average energy efficiency of 72.32&#xa0;J. Thus, the proposed work efficiently optimizes the energy efficiency, decreases communication overhead, and expands the network lifetime in WSN.</p>

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Routing Protocol for Wireless Sensor Networks using an Energy Efficient Binary Sculptor Artificial Protozoa Optimization Algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • V. R. Sugumaran,
  • S. Palani Murugan,
  • E. Dinesh,
  • Elangovan Muniyandy

摘要

Routing algorithms are effectively designed to decrease energy consumption and increase communication reliability to enhance the lifespan and performance of Wireless Sensor Networks (WSNs). Routing and clustering are important for sensor nodes to regulate energy consumption and allocate workload. However, individual nodes have limited power resources, and WSNs are dynamic and establish longer network lifetimes, which continues to be a major problem. To resolve these challenges, the Energy-Efficient Binary Sculptor Artificial Protozoa Optimization (EE-BSAPO) algorithm, a sophisticated routing protocol, is designed. The suggested protocol integrates the Binary Sculptor Artificial Protozoa Optimization method with an improved fuzzy logic-based clustering mechanism to enhance multi-hop routing and cluster formation. To enhance the cluster head selection and route discovery, based on distance, communication latency, trust value, and residual energy. Based on various simulations, EE-BSAPO significantly enhances network performance as compared to existing routing methods. In particular, its average end-to-end latency measured a very low 9.36 ms, its average throughput reached 75.24%, and its average network lifespan is 73.81%. The model is suitable for WSN because energy supplies are limited, based on its average energy efficiency of 72.32 J. Thus, the proposed work efficiently optimizes the energy efficiency, decreases communication overhead, and expands the network lifetime in WSN.