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<p>Wireless Body Area Networks (WBANs) encounter critical challenges optimizing energy efficiency and Quality of Service (QoS) amid dynamic conditions, such as posture-driven channel fluctuations, variable traffic patterns, and strict application demands. Current methods frequently optimize energy, MAC scheduling, or routing in isolation, which restricts their ability to maintain consistent end-to-end QoS and maximize network longevity. To address these constraints, this paper introduces a hybrid optimization strategy employing a Modified Bat Algorithm (MBAT) and a Firefly-driven global search. The strategy enables adaptive cross-layer optimization by dynamically calibrating transmission parameters based on QoS metrics—packet delivery ratio, latency, and throughput. A multi-objective fitness function combining path loss, throughput, and communication range is designed to balance energy consumption and QoS. The Firefly algorithm explores global solutions to pinpoint optimal search regions, whereas MBAT accelerates local convergence and solution enhancement. Simulation results show that the MBAT approach consistently surpasses baseline techniques—TRAP, UPA, TPC, and LSEPC—by achieving lower energy consumption and packet loss, while also improving the packet delivery ratio and throughput across both static and dynamic WBAN settings. These outcomes indicate that the framework delivers a scalable and efficient solution for QoS-aware energy optimization in WBANs, making it well-suited for real-time healthcare monitoring.</p>

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A QoS aware energy enhancement meta-heuristic approach for optimal data transfer in wireless body area networks

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • R. Pradeep,
  • G. Kavithaa

摘要

Wireless Body Area Networks (WBANs) encounter critical challenges optimizing energy efficiency and Quality of Service (QoS) amid dynamic conditions, such as posture-driven channel fluctuations, variable traffic patterns, and strict application demands. Current methods frequently optimize energy, MAC scheduling, or routing in isolation, which restricts their ability to maintain consistent end-to-end QoS and maximize network longevity. To address these constraints, this paper introduces a hybrid optimization strategy employing a Modified Bat Algorithm (MBAT) and a Firefly-driven global search. The strategy enables adaptive cross-layer optimization by dynamically calibrating transmission parameters based on QoS metrics—packet delivery ratio, latency, and throughput. A multi-objective fitness function combining path loss, throughput, and communication range is designed to balance energy consumption and QoS. The Firefly algorithm explores global solutions to pinpoint optimal search regions, whereas MBAT accelerates local convergence and solution enhancement. Simulation results show that the MBAT approach consistently surpasses baseline techniques—TRAP, UPA, TPC, and LSEPC—by achieving lower energy consumption and packet loss, while also improving the packet delivery ratio and throughput across both static and dynamic WBAN settings. These outcomes indicate that the framework delivers a scalable and efficient solution for QoS-aware energy optimization in WBANs, making it well-suited for real-time healthcare monitoring.