Quantum-Secured AI-Driven Drone Logistics for Real-Time Healthcare Delivery

Hafeez, S ORCID: 0000-0003-4769-4284, Abro, GEM ORCID: 0000-0003-1874-1889 and Marimuthu, M (2026) Quantum-Secured AI-Driven Drone Logistics for Real-Time Healthcare Delivery. Arabian Journal for Science and Engineering. ISSN 2193-567X

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Abstract

This paper presents a quantum-resilient autonomy stack for medical-drone delivery that elevates communications and cryptography to first-class, stateful variables within motion planning rather than downstream constraints. A three-layer Air–Ground–Communications architecture integrates beyond-6 G (FR3/THz) links with ultra-reliable low-latency communication (URLLC) fallback and couples BB84-style quantum key distribution (QKD) with principled post-quantum cryptography (PQC) switching to ensure cryptographic continuity under mobility and adverse weather. At the core, a hybrid AI–RKF45 controller fuses a lightweight neural policy with the integrator’s local error, co-adapting control aggressiveness and solver step size for stiffness-aware manoeuvres and rapid re-optimisation under disturbances. The planner directly ingests link SNR, URLLC queueing delay, QKD quantum bit error rate (QBER), secure key rate (SKR), key-buffer levels, meteorological risk, and battery state-of-health, shaping a multi-objective cost that jointly minimises energy and control-loop end-to-end latency (sensing–compute–communication round-trip), while enforcing geofencing and Beyond Visual Line of Sight (BVLOS) constraints. In large-scale regional simulations (200 km × 200 km; up to 200 UAVs), the framework achieves a mean control-loop latency of 2.34 s (distinct from door-to-door delivery time), an 18% reduction in energy per sortie, and a 98.2% mission success rate, outperforming static planners, deep reinforcement learning (PPO/DQN), and model predictive control under matched compute budgets. The QKD↔PQC state machine applies conservative thresholds (QBER ≤ 8%, SKR ≥ 5 kbps with key-buffer hysteresis), yielding rare, millisecond-scale fallbacks that preserve latency guarantees. Complexity scales as OnνT, maintaining 2–5 s update times for fleets exceeding 200 vehicles. Reproducible artefacts and a staged pathway from hardware-in-the-loop to BVLOS trials support near-term healthcare deployment.

Item Type:	Article
Uncontrolled Keywords:	Medical drone delivery; Quantum-resilient communications; Beyond-6 G networks; Autonomous UAV navigation; Control-loop latency optimisation; 40 Engineering; 4009 Electronics, Sensors and Digital Hardware; Machine Learning and Artificial Intelligence; 7 Affordable and Clean Energy; 0905 Civil Engineering; 0912 Materials Engineering; 4004 Chemical engineering; 4005 Civil engineering; 4016 Materials engineering
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Divisions:	Computer Science and Mathematics
Publisher:	Springer
Date of acceptance:	22 January 2026
Date of first compliant Open Access:	7 July 2026
Date Deposited:	07 Jul 2026 08:58
Last Modified:	07 Jul 2026 08:58
DOI or ID number:	10.1007/s13369-026-11104-5
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28951

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