2025

Journal

Computing

As the adoption of unmanned aerial vehicles by both consumers and companies is growing rapidly, the use of drones is nowadays leading the way various types of packages (e.g., food, medication, supplies) are delivered. Drone services have increased companies’ benefits and saved money on their shipping costs, which resulted in a reduced delivery time and cost for consumers. However, the delivery tasks either achieved by single or swarming drones are facing several challenges, which are mainly related to the modeling of drones’ skyway network, the uncertainty of flight conditions and available resources, the consumers’ trust and quality of experience, the privacy concerns caused by shared user information (e.g., GPS, camera). Among these issues, we focus on the modeling and resource availability issues. To cope with complex delivery requests, this paper takes advantage of the service computing paradigm to propose a service composition approach, in which multiple drone services can participate in the delivery plan. We first propose a graph-based modeling of the skyway network and flights history. This latter, is fed into a proposed frequent subgraph mining algorithm, and is processed to extract relevant patterns from the previously generated delivery paths, based on consumers’ positive feedback. We also adopt Long Short-Term Memory to propose a model that forecasts the overload of charging stations, with the goal of tuning the mined (i.e., frequently traversed) paths with the low-congestion stations. The prediction and mining results are, finally, exploited, in the selection of the appropriate drone formation that will achieve the delivery mission. Experimental studies based on real-world datasets have confirmed the efficiency of our approach, compared to three graph-based approaches.

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