<p>Wireless Body Area Networks (WBANs) face the persistent challenge of simultaneously achieving energy efficiency, high reliability, and ultra-low latency under dynamic human mobility and time-varying on-body channel conditions. To address this multi-objective routing problem, this paper proposes the <i>Opposition-Based Free Search Krill Herd</i> (OB-FSKH) algorithm, a novel hybrid metaheuristic framework that significantly enhances the standard Krill Herd (KH) paradigm through the synergistic integration of Quasi-Oppositional Learning (QOL), Free Search (FS)-based perturbation, and Dynamic Opposition-Based Learning (OBL). OB-FSKH models each krill individual as a candidate multi-hop path and evolves the population using biologically inspired motion operators, induced, foraging, and diffusion, guided by a composite cost function. This function enables true multi-objective optimization by jointly minimizing four conflicting Quality-of-Service (QoS) metrics: energy consumption, end-to-end delay, packet error rate, and posture-dependent path loss, with the latter marking the first explicit incorporation of real-time postural dynamics into the KH fitness landscape for adaptive route selection. A closed-loop re-optimization mechanism ensures resilience to topology changes triggered by significant body movements (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(|\Delta \theta |> 30^\circ\)</EquationSource></InlineEquation>). Rigorous evaluation using a custom high-fidelity dataset from human subject trials and IEEE 802.15.6-compliant channel models under realistic interference (up to 20 dB SNR degradation) demonstrates OB-FSKH’s superior performance: it extends network lifetime by <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\mathbf {22.4\%}\)</EquationSource></InlineEquation> (to <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(12.00 \times 10^5\)</EquationSource></InlineEquation> rounds), reduces average energy dissipation by <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(\mathbf {28.1\%}\)</EquationSource></InlineEquation> (to 0.421 mJ/round), achieves a Packet Delivery Ratio (PDR) of <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(\mathbf {92.4\%}\)</EquationSource></InlineEquation>, and guarantees emergency-mode latency of <InlineEquation ID="IEq6"><EquationSource Format="TEX">\(\textbf{298}\)</EquationSource></InlineEquation> ms. These statistically validated results, benchmarked against six state-of-the-art protocols including SEAR, DHH-EFO, and RPESA, establish OB-FSKH as a robust, intelligent, and clinically viable solution for next-generation telemedicine systems.</p>

Opposition-Based Free Search Krill Herd for Multi-Objective Energy-Aware and Reliable WBAN Routing under Dynamic Human Mobility

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2026年8月4日

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Ben Othman Soufiane,
Osman Hassan

摘要

Wireless Body Area Networks (WBANs) face the persistent challenge of simultaneously achieving energy efficiency, high reliability, and ultra-low latency under dynamic human mobility and time-varying on-body channel conditions. To address this multi-objective routing problem, this paper proposes the Opposition-Based Free Search Krill Herd (OB-FSKH) algorithm, a novel hybrid metaheuristic framework that significantly enhances the standard Krill Herd (KH) paradigm through the synergistic integration of Quasi-Oppositional Learning (QOL), Free Search (FS)-based perturbation, and Dynamic Opposition-Based Learning (OBL). OB-FSKH models each krill individual as a candidate multi-hop path and evolves the population using biologically inspired motion operators, induced, foraging, and diffusion, guided by a composite cost function. This function enables true multi-objective optimization by jointly minimizing four conflicting Quality-of-Service (QoS) metrics: energy consumption, end-to-end delay, packet error rate, and posture-dependent path loss, with the latter marking the first explicit incorporation of real-time postural dynamics into the KH fitness landscape for adaptive route selection. A closed-loop re-optimization mechanism ensures resilience to topology changes triggered by significant body movements (\(|\Delta \theta |> 30^\circ\)). Rigorous evaluation using a custom high-fidelity dataset from human subject trials and IEEE 802.15.6-compliant channel models under realistic interference (up to 20 dB SNR degradation) demonstrates OB-FSKH’s superior performance: it extends network lifetime by \(\mathbf {22.4\%}\) (to \(12.00 \times 10^5\) rounds), reduces average energy dissipation by \(\mathbf {28.1\%}\) (to 0.421 mJ/round), achieves a Packet Delivery Ratio (PDR) of \(\mathbf {92.4\%}\), and guarantees emergency-mode latency of \(\textbf{298}\) ms. These statistically validated results, benchmarked against six state-of-the-art protocols including SEAR, DHH-EFO, and RPESA, establish OB-FSKH as a robust, intelligent, and clinically viable solution for next-generation telemedicine systems.