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

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 ( \(|Δ θ |> 30^∘\) ). 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 \(22.4%\) (to \(12.00 × 10^5\) rounds), reduces average energy dissipation by \(28.1%\) (to 0.421 mJ/round), achieves a Packet Delivery Ratio (PDR) of \(92.4%\) , and guarantees emergency-mode latency of \(298\) 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.