Optimizing Joint Speed and Altitude Schedule for UAV Data Collection in Low-Altitude Airspace

Low-altitude airspace in major cities across the world is increasingly congested with unmanned aerial vehicles (UAVs) and other aircraft. Emerging technologies, innovative business models, and supportive government policies are driving the growth of the low-altitude economy, where UAVs play a crucial role. Given the limited on-board energy of UAVs, this paper investigates the Joint UAV Speed and Altitude Scheduling (JUSAS) problem for data collection from sensors deployed along power transmission lines, bridges, highways, railways, water/gas/oil pipelines, or rivers/coasts. Distinct from existing work, the paper focuses on jointly optimizing UAV speed and altitude scheduling while determining the wireless sensor collection order. It accounts for the altitude-specific sensor transmission range model and the complexities of overlapping range relationships. We first propose the Slowest Segment First (SSF) policy to obtain an optimal UAV speed scheduling for fixed-altitude scenarios. Building upon this, we then reformulate JUSAS as a shortest-path-type problem using our novel flight scheduling graph, solved efficiently through the SSF-based Ant Colony Optimization (SSF-ACO) algorithm. To handle practical scenarios without prior sensor information along the path, we develop SSF-ACO-Online for real-time scheduling. Extensive simulations demonstrate that SSF-ACO significantly outperforms four other algorithms (i.e., SSF-Only, SSF-GA, SSF-PSO, and SSF-SA) in energy efficiency, and reduces 13.11% energy consumption on average. SSF-ACO-Online achieves comparable performance with energy consumption 1.24% higher than offline counterpart in average.