Bridging ACO-Based Drone Logistics and Computing Continuum for Enhanced Smart City Applications

In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response.