A Cognitive, Quantum-Inspired Learning Framework for Energy-Efficient and Reliable Routing in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are critical enablers of real-time, non-invasive healthcare monitoring, enabling continuous acquisition and wireless transmission of vital physiological signals such as electrocardiogram (ECG), heart rate, and motion data. However, their practical deployment faces significant challenges due to stringent energy constraints, dynamic on-body channel variations induced by mobility, and multi-class Quality-of-Service (QoS) requirements, particularly ultra-low latency ( $$\le 18\,\text {ms}$$ ) for emergency alerts and high reliability ( $$\ge 99.5\%$$ PDR) for diagnostic data. Conventional routing protocols often rely on static topologies or reactive mechanisms that fail under the coupled spatio-temporal variability of physiological dynamics and wireless channels. To address these limitations, we propose QuantumEERP-Net, a novel, intelligent, and self-optimizing routing framework that synergistically integrates Multi-Agent Dueling Double Deep Q-Networks (D3QN), a Quantum-Inspired Dual Prediction Model (Q-DPM), and Context-Aware Adaptive Routing into a fully decentralized architecture. Each sensor node operates as an autonomous agent, learning optimal forwarding policies through continuous interaction with a dynamic environment modeled using log-normal shadowing ( $$\sigma = 6.500\,\text {dB}$$ ) and Markovian mobility states. The Q-DPM, implemented via a variational quantum circuit with parameter-shift gradient estimation, jointly predicts future sensor values (e.g., ECG at $$125~\textrm{Hz}$$ ) and residual energy levels with mean error $$< 0.008\,\text {J}$$ , enabling proactive suppression of redundant transmissions and substantial energy savings. A real-time context-aware engine fuses IMU and ECG inputs to detect mobility patterns (static/walking/running) and critical events (HR $$> 180~\textrm{bpm}$$ , falls) with accuracy $$\ge 0.991$$ , triggering low-latency routing overrides for emergency traffic. Extensive simulations over 20 000 cycles using a comprehensive hybrid dataset of 450,000 labeled samples (150,000 per class), with a held-out test set of 150,000 samples (50,000 per class), demonstrate that QuantumEERP-Net achieves a First Node Dead (FND) of 10,250 rounds (an 83.0% improvement) and extends Last Node Dead (LND) by 84.3% compared to EAFST. The framework attains a Packet Delivery Ratio (PDR) of 1.000 for Emergency data, reduces emergency end-to-end delay to 13.800 ms (with an average delay of 2.000 ms), and maintains a security compliance of 0.995. Furthermore, hardware benchmarking on an nRF52832 SoC validates that the D3QN and Q-DPM inference consumes only $$\approx 3.600\,\upmu \text {J}$$ per cycle, ensuring minimal computational overhead. These gains result from the tight integration of link-quality-aware forwarding, NSGA-II-based multi-objective optimization, and predictive intelligence. By unifying quantum-inspired computation, deep reinforcement learning, and physiological context awareness, QuantumEERP-Net establishes a new paradigm for secure, energy-efficient, and adaptive communication in next-generation WBANs, offering a scalable solution for mission-critical telemedicine and remote patient monitoring systems.