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<p>A Wireless Sensor Network (WSN) refers to the network of spatially dispersed sensors that records and monitors physical conditions of an environment and forwards the obtained data to Base Station (BS). These networks are widely employed in applications like smart cities, environmental monitoring, and industrial automation. However, minimizing delay in energy-efficient clustering and routing is challenging because energy constraints often require sensors to enter low-power states, which causes delays in communication. Therefore, effective energy management and selection of optimal multi-hop routing paths are essential to minimize delays and prevent network congestion. This research proposes Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO) to optimize energy efficiency and reduce delay via effective clustering and routing in WSN. Logistic mapping and symmetric adaptive division population are the di strategies, which are used for population initialization and balancing exploration, which enhance diversity and effectively search the solution space. Distance between Cluster Head (CH) and BS, intra-cluster distance, node degree, average delay during transmission, and residual energy are the multi-objectives, which are utilized as fitness functions for CH and route path selection. MO-DSGGO achieves less delay of 0.176 ms and reduced energy consumption of 7.2&#xa0;J for scenario 2 with 100 nodes and network size of <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\:1200\times\:1200\)</EquationSource></InlineEquation> mts and obtains throughput of 95% in scenario 7 with 250 nodes and <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\:100\times\:100{\:m}^{2}\)</EquationSource></InlineEquation> in MATLAB R2020b. These improvements represent clear superiority over existing methods like Energy Optimization Routing by applying an improved Artificial Bee Colony (EOR-iABC) and Energy Optimization Approach Medium access control Routing Cross-Layer (EOAMRCL). Also, the proposed method obtains better convergence analysis that represents rapid and more stable performance. MO-DSGGO achieves longer node lifetime of 97.272% with Packet Delivery Ratio (PDR) of 95.38%, and throughput of 14,297 BPS compared to Energy Efficient Lifetime-aware Cluster-based Routing (EELCR) that demonstrates more reliable and efficient network utilization.</p>

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Multi-objective optimization approach for energy efficient clustering and routing in wireless sensor networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • E. Madhankumar,
  • K. Selvaraj,
  • Dae-Ki Kang

摘要

A Wireless Sensor Network (WSN) refers to the network of spatially dispersed sensors that records and monitors physical conditions of an environment and forwards the obtained data to Base Station (BS). These networks are widely employed in applications like smart cities, environmental monitoring, and industrial automation. However, minimizing delay in energy-efficient clustering and routing is challenging because energy constraints often require sensors to enter low-power states, which causes delays in communication. Therefore, effective energy management and selection of optimal multi-hop routing paths are essential to minimize delays and prevent network congestion. This research proposes Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO) to optimize energy efficiency and reduce delay via effective clustering and routing in WSN. Logistic mapping and symmetric adaptive division population are the di strategies, which are used for population initialization and balancing exploration, which enhance diversity and effectively search the solution space. Distance between Cluster Head (CH) and BS, intra-cluster distance, node degree, average delay during transmission, and residual energy are the multi-objectives, which are utilized as fitness functions for CH and route path selection. MO-DSGGO achieves less delay of 0.176 ms and reduced energy consumption of 7.2 J for scenario 2 with 100 nodes and network size of \(\:1200\times\:1200\) mts and obtains throughput of 95% in scenario 7 with 250 nodes and \(\:100\times\:100{\:m}^{2}\) in MATLAB R2020b. These improvements represent clear superiority over existing methods like Energy Optimization Routing by applying an improved Artificial Bee Colony (EOR-iABC) and Energy Optimization Approach Medium access control Routing Cross-Layer (EOAMRCL). Also, the proposed method obtains better convergence analysis that represents rapid and more stable performance. MO-DSGGO achieves longer node lifetime of 97.272% with Packet Delivery Ratio (PDR) of 95.38%, and throughput of 14,297 BPS compared to Energy Efficient Lifetime-aware Cluster-based Routing (EELCR) that demonstrates more reliable and efficient network utilization.