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<p>In recent years, wireless sensor networks are playing an increasingly pivotal role in the monitoring of energy-constrained environments. However, the limited power of nodes restricts the network’s life cycle and service quality. In this paper, we propose an energy-aware dynamic reorganization clustering protocol based on the Gini coefficient and an improved particle swarm optimization algorithm (DGIPSO). The concept of Gini coefficient is applied to address uneven clustering in this hierarchical method. An improved PSO method is proposed for optimal cluster head selection, which considers residual energy, distance, and individual Gini coefficient factors. Additionally, a dynamic reorganization mechanism based on the Gini coefficient is introduced to enhance transmission performance over the network’s lifespan. Experimental results demonstrate that the proposed DGIPSO protocol significantly outperforms LEACH, R-LEACH, PSO-C, PSO-WZ, and C3HA in network lifetime, energy efficiency, data throughput, and communication stability across various network scales. By incorporating Gini coefficients and heuristic algorithms into dynamic reorganization, this approach effectively addresses uneven energy distribution in clustering protocols, providing innovative strategies for monitoring in resource-constrained applications.</p>

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An energy-aware clustering approach based on Gini coefficient and IPSO applied for energy-constrained applications

  • 期刊论文

  • 发表日期：

    2025年4月9日

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  • Hang Wan,
  • Jiaqi Gao,
  • Michael David,
  • William Derigent,
  • Haiyan Zhao,
  • Yufang Chang

摘要

In recent years, wireless sensor networks are playing an increasingly pivotal role in the monitoring of energy-constrained environments. However, the limited power of nodes restricts the network’s life cycle and service quality. In this paper, we propose an energy-aware dynamic reorganization clustering protocol based on the Gini coefficient and an improved particle swarm optimization algorithm (DGIPSO). The concept of Gini coefficient is applied to address uneven clustering in this hierarchical method. An improved PSO method is proposed for optimal cluster head selection, which considers residual energy, distance, and individual Gini coefficient factors. Additionally, a dynamic reorganization mechanism based on the Gini coefficient is introduced to enhance transmission performance over the network’s lifespan. Experimental results demonstrate that the proposed DGIPSO protocol significantly outperforms LEACH, R-LEACH, PSO-C, PSO-WZ, and C3HA in network lifetime, energy efficiency, data throughput, and communication stability across various network scales. By incorporating Gini coefficients and heuristic algorithms into dynamic reorganization, this approach effectively addresses uneven energy distribution in clustering protocols, providing innovative strategies for monitoring in resource-constrained applications.