2025

Journal

Cluster Computing

Recently, drones have emerged as an effective and rapid mean to deliver various packages as services. Exhibiting the same behavior and principles of the service paradigm, drones’ delivery tasks can be abstracted as services, also called DaaS (Drone-as-a-Service). However, drones operate in a dynamic and uncertain environment (flight regulations, weather conditions, charging resources). In addition, the drones’ limited capacity (e.g., flying range, battery capacity, payload) and the delivery constraints (e.g., cost, delivery time) make the delivery missions beyond one drone’s capacity. To cope with the above challenges, and to meet the customers’ and providers’ delivery constraints, composing drone services is a natural solution. We propose a DaaS composition approach that takes advantage of a recent and powerful technology, called knowledge graph. First, we model the drones’ environment (charging stations, skyway segments, drone services) as a heterogeneous information network. Then, we combine knowledge graph embedding with subgraph-aware proximity measure, to arrange drones and charging stations in a low-dimentional vector space. This pre-processing step helped select high-flight capacity drone services, and generate low-congestion delivery paths. Experimental results have proven the approach’s performance and the quality of DaaS compositions, compared to recent state-of-the-art approaches.

-