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<p>This study presents the Drone Relay Delivery System (DRDS), an intelligent framework developed to strengthen healthcare logistics through autonomous aerial delivery. The proposed architecture combines Mobile Edge Computing (MEC) and the Internet of Things (IoT) to make it possible to route energy efficiently, make decisions in real time, and distribute medical supplies in a way that is reliable in various settings. The DRDS incorporates a dual-optimization strategy that balances pre-planned efficiency with adaptive responsiveness. The Energy-Aware Static Delivery Service Provision (EDSP) algorithm creates baseline routes that use the least amount of energy and cost the least to run when conditions are stable. In contrast, the Energy-Aware Dynamic Computing Service Provision (ECSP) algorithm recalibrates routes in real-time to address disruptions, such as adverse weather, airspace restrictions, or emergency requests. The framework was evaluated using the Bouman and Delhi hospital-inspired datasets to assess performance under static and dynamic scenarios. Comparing it to NSGA-II, Adaptive Ant Colony Optimization, and Reinforcement Learning showed significant improvements in energy use, quickness, and reliability of deliveries. By combining MEC-based edge intelligence with IoT sensing and multi-objective optimization, the DRDS creates a flexible and long-lasting base for future healthcare delivery systems.</p>

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Integrating mobile edge computing in drone delivery: a holistic service provision strategy for drone-based healthcare logistics

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Tejinder Singh Lakhwani,
  • Yerasani Sinjana,
  • Anuj Pal Kapoor

摘要

This study presents the Drone Relay Delivery System (DRDS), an intelligent framework developed to strengthen healthcare logistics through autonomous aerial delivery. The proposed architecture combines Mobile Edge Computing (MEC) and the Internet of Things (IoT) to make it possible to route energy efficiently, make decisions in real time, and distribute medical supplies in a way that is reliable in various settings. The DRDS incorporates a dual-optimization strategy that balances pre-planned efficiency with adaptive responsiveness. The Energy-Aware Static Delivery Service Provision (EDSP) algorithm creates baseline routes that use the least amount of energy and cost the least to run when conditions are stable. In contrast, the Energy-Aware Dynamic Computing Service Provision (ECSP) algorithm recalibrates routes in real-time to address disruptions, such as adverse weather, airspace restrictions, or emergency requests. The framework was evaluated using the Bouman and Delhi hospital-inspired datasets to assess performance under static and dynamic scenarios. Comparing it to NSGA-II, Adaptive Ant Colony Optimization, and Reinforcement Learning showed significant improvements in energy use, quickness, and reliability of deliveries. By combining MEC-based edge intelligence with IoT sensing and multi-objective optimization, the DRDS creates a flexible and long-lasting base for future healthcare delivery systems.