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<p>Data aggregation has emerged as a key technique to reduce energy consumption by minimizing redundant data transmissions and optimizing the overall communication load in Wireless Sensor Networks (WSNs). This paper presents an energy-based data aggregation strategy in WSNs, in which the Cluster Head (CH) changes in each cycle based on the residual energy in the nodes. This method adapts data transmission based on the correlation of sensor information in each transmission to enrich the lifespan of the network. By incorporating energy-conscious algorithms and managing the transmission of packets based on the data, the energy expenditure for both communication and computation is significantly reduced. The model takes into account various energy dissipation factors, such as electronic circuitry, free space, and multipath fading, ensuring a comprehensive analysis of energy within the network. The simulation findings demonstrate that the proposed method outperforms existing protocols, achieving approximately double the network lifetime, a 54% reduction in transmission delay, up to 38% lower average energy consumption, and a 76% increase in residual energy, contributing to enhanced network efficiency and longevity. The method involves an average of 1711 operations per round, reflecting its algorithmic sophistication.</p>

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Adaptive Aggregation Scheme in a WSN Based on Residual Energy and Data Correlation

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Annie Paul,
  • S. Emalda Roslin

摘要

Data aggregation has emerged as a key technique to reduce energy consumption by minimizing redundant data transmissions and optimizing the overall communication load in Wireless Sensor Networks (WSNs). This paper presents an energy-based data aggregation strategy in WSNs, in which the Cluster Head (CH) changes in each cycle based on the residual energy in the nodes. This method adapts data transmission based on the correlation of sensor information in each transmission to enrich the lifespan of the network. By incorporating energy-conscious algorithms and managing the transmission of packets based on the data, the energy expenditure for both communication and computation is significantly reduced. The model takes into account various energy dissipation factors, such as electronic circuitry, free space, and multipath fading, ensuring a comprehensive analysis of energy within the network. The simulation findings demonstrate that the proposed method outperforms existing protocols, achieving approximately double the network lifetime, a 54% reduction in transmission delay, up to 38% lower average energy consumption, and a 76% increase in residual energy, contributing to enhanced network efficiency and longevity. The method involves an average of 1711 operations per round, reflecting its algorithmic sophistication.