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<p>Wireless Body Area Networks (WBANs) combined with Software-Defined Networks (SDNs) offer a promising platform for efficient and adaptive wireless data transmission from wearable devices to central servers. This paper presents a novel method for optimizing data transmission in WBAN-SDN by addressing critical challenges of energy consumption and congestion control. The proposed approach introduces a centralized controller that performs node clustering and dynamically selects an optimal cluster head for data transmission. This selection is based on a comprehensive evaluation of parameters, including intra-cluster distance, proximity to the base station, residual energy, and congestion levels. A key innovation of this work is the integration of the Particle Swarm Optimization (PSO) algorithm to enhance cluster head selection, ensuring optimal network performance. The proposed method demonstrates significant advancements over existing techniques by improving key quality-of-service metrics. Simulation results reveal notable reductions in average energy consumption and end-to-end delay, alongside enhanced data delivery rates and extended network lifetime. These improvements highlight the effectiveness of the PSO-based strategy in balancing energy efficiency and congestion control within the WBAN-SDN framework. This work provides a valuable contribution to the development of energy-aware and congestion-resilient solutions in next-generation WBAN applications.</p>

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Energy-Aware Congestion Control in WBAN-SDN Based on a Metaheuristic Approach

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Javad Mozaffari,
  • Mohammad Abdollahi Azgomi,
  • Halimeh Madadi,
  • Mohammad Reza Ebrahimi Dishabi

摘要

Wireless Body Area Networks (WBANs) combined with Software-Defined Networks (SDNs) offer a promising platform for efficient and adaptive wireless data transmission from wearable devices to central servers. This paper presents a novel method for optimizing data transmission in WBAN-SDN by addressing critical challenges of energy consumption and congestion control. The proposed approach introduces a centralized controller that performs node clustering and dynamically selects an optimal cluster head for data transmission. This selection is based on a comprehensive evaluation of parameters, including intra-cluster distance, proximity to the base station, residual energy, and congestion levels. A key innovation of this work is the integration of the Particle Swarm Optimization (PSO) algorithm to enhance cluster head selection, ensuring optimal network performance. The proposed method demonstrates significant advancements over existing techniques by improving key quality-of-service metrics. Simulation results reveal notable reductions in average energy consumption and end-to-end delay, alongside enhanced data delivery rates and extended network lifetime. These improvements highlight the effectiveness of the PSO-based strategy in balancing energy efficiency and congestion control within the WBAN-SDN framework. This work provides a valuable contribution to the development of energy-aware and congestion-resilient solutions in next-generation WBAN applications.