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arXiv · 2508.01671

Energy-Predictive Planning for Optimizing Drone Service Delivery

Guanting Ren·Babar Shahzaad·Balsam Alkouz·Abdallah Lakhdari·Athman Bouguettaya
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Abstract

We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stochastic arrival times of other drones operating in the same skyway network. Leveraging these predictions, we develop a heuristic optimization approach for composite drone services. This approach identifies the most time-efficient and energy-efficient skyway path and recharging schedule for each drone in the network. We conduct extensive experiments using a real-world drone flight dataset to evaluate the performance of the proposed framework.

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Guanting Ren, Babar Shahzaad, Balsam Alkouz, Abdallah Lakhdari, Athman Bouguettaya. 2025-08-03. Energy-Predictive Planning for Optimizing Drone Service Delivery. https://arxiv.org/abs/2508.01671

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