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<p>Recently, Wireless Sensor Networks (WSNs) have garnered considerable attention owing to their potential applications across various fields. However, WSNs face several challenges, including energy-aware clustering, congestion&#xa0;manage,&#xa0;energy&#xa0;control, and&#xa0;data&#xa0;aggregation. Congestion, in particular, can harm packet transport rate, latency, energy expenditure, and Quality of Service. This work focuses on addressing these challenges through a combination of optimization techniques. The proposed approach utilizes the K-means algorithm to cluster the sensor nodes, enabling efficient data organization and transmission. An active firefly optimization technique is employed to mitigate congestion and optimize the data transmission rate, which dynamically adjusts the transmission rate based on negative acknowledgements. The fire hawk optimization technique optimizes cluster heads, ensuring effective coordination and resource allocation within the clusters. Additionally, the throughput and routing optimization are enhanced using the ant colony optimization algorithm, maximizing network performance. The proposed approach's performance is assessed using the results obtained from the K-means algorithm, active firefly optimization, fire hawk optimization algorithm, and firefly optimization algorithm. By employing these optimization methods, the projected approach aims to improve the overall efficiency, reliability, and lifetime of the WSN. The experimental results will validate the approach's effectiveness and provide insights into its practical implementation.</p>

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Congestion control and enhancing efficiency in wireless sensor networks through optimization techniques

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Savita Sandeep Jadhav,
  • Sangeeta Jadhav

摘要

Recently, Wireless Sensor Networks (WSNs) have garnered considerable attention owing to their potential applications across various fields. However, WSNs face several challenges, including energy-aware clustering, congestion manage, energy control, and data aggregation. Congestion, in particular, can harm packet transport rate, latency, energy expenditure, and Quality of Service. This work focuses on addressing these challenges through a combination of optimization techniques. The proposed approach utilizes the K-means algorithm to cluster the sensor nodes, enabling efficient data organization and transmission. An active firefly optimization technique is employed to mitigate congestion and optimize the data transmission rate, which dynamically adjusts the transmission rate based on negative acknowledgements. The fire hawk optimization technique optimizes cluster heads, ensuring effective coordination and resource allocation within the clusters. Additionally, the throughput and routing optimization are enhanced using the ant colony optimization algorithm, maximizing network performance. The proposed approach's performance is assessed using the results obtained from the K-means algorithm, active firefly optimization, fire hawk optimization algorithm, and firefly optimization algorithm. By employing these optimization methods, the projected approach aims to improve the overall efficiency, reliability, and lifetime of the WSN. The experimental results will validate the approach's effectiveness and provide insights into its practical implementation.