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<p>Wireless Body Area Networks (WBANs) face critical challenges in achieving energy efficiency, security, and real-time reliability for continuous biomedical monitoring, as conventional protocols are hindered by energy constraints, vulnerability to cyberattacks, and high latency. To address these limitations, this paper proposes QuanBioTrust, a novel, integrated framework that fuses quantum-enhanced optimization, bio-inspired intelligence, and a dynamic zero-trust security model to create a scalable, secure, and self-sustaining WBAN architecture. QuanBioTrust leverages Quantum Particle Swarm Optimization (QPSO) with quantum entanglement to optimize cluster head (CH) selection by concurrently evaluating residual energy, sink proximity, trust score, and bio-energy harvesting potential (20–50&#xa0;µW), reducing computational complexity to ~ 500 operations per round, a 28–45% improvement over HCEL (~ 700 ops) and EGWO (~ 1000 ops). Inspired by natural systems, the framework employs flocking algorithms for mobility-adaptive clustering, firefly synchronization for efficient multi-hop fractal routing (3–5 paths), and slime mold-inspired data fusion to achieve 50–70% compression of ECG packets (200-bit → 80-bit), drastically reducing transmission energy. The framework maintains a Packet Delivery Ratio (PDR) of 97% under normal conditions (90% under attack) and an end-to-end latency of 48&#xa0;ms (185&#xa0;ms under attack), well below the 250&#xa0;ms threshold for real-time monitoring, outperforming EAFST (1000/1200&#xa0;ms) and rivaling EDC-ER (50/180&#xa0;ms). This quantum-biological fusion establishes a new paradigm for next-generation WBANs, offering a robust, energy-efficient, and resilient solution for secure, long-term, real-time healthcare monitoring.</p>

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QuanBioTrust: a quantum-enhanced bio-inspired trust framework for next-generation WBANs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月9日

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  • Soufiane Ben Othman,
  • Obaid Ali

摘要

Wireless Body Area Networks (WBANs) face critical challenges in achieving energy efficiency, security, and real-time reliability for continuous biomedical monitoring, as conventional protocols are hindered by energy constraints, vulnerability to cyberattacks, and high latency. To address these limitations, this paper proposes QuanBioTrust, a novel, integrated framework that fuses quantum-enhanced optimization, bio-inspired intelligence, and a dynamic zero-trust security model to create a scalable, secure, and self-sustaining WBAN architecture. QuanBioTrust leverages Quantum Particle Swarm Optimization (QPSO) with quantum entanglement to optimize cluster head (CH) selection by concurrently evaluating residual energy, sink proximity, trust score, and bio-energy harvesting potential (20–50 µW), reducing computational complexity to ~ 500 operations per round, a 28–45% improvement over HCEL (~ 700 ops) and EGWO (~ 1000 ops). Inspired by natural systems, the framework employs flocking algorithms for mobility-adaptive clustering, firefly synchronization for efficient multi-hop fractal routing (3–5 paths), and slime mold-inspired data fusion to achieve 50–70% compression of ECG packets (200-bit → 80-bit), drastically reducing transmission energy. The framework maintains a Packet Delivery Ratio (PDR) of 97% under normal conditions (90% under attack) and an end-to-end latency of 48 ms (185 ms under attack), well below the 250 ms threshold for real-time monitoring, outperforming EAFST (1000/1200 ms) and rivaling EDC-ER (50/180 ms). This quantum-biological fusion establishes a new paradigm for next-generation WBANs, offering a robust, energy-efficient, and resilient solution for secure, long-term, real-time healthcare monitoring.