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Spatiotemporal heterogeneity in demand distribution poses a significant challenge for deployment and coverage control in unmanned aerial vehicle (UAV) tasking. Recent disasters such as the January 2025 Los Angeles wildfires have amplified both the urgency and the scale of such UAV operations. Traditional methods typically assume uniform or static demand, overlooking spatial and temporal variations, ultimately leading to suboptimal deployment of UAVs. To address this shortcoming, this paper introduces Spatiotemporal Coverage for Optimal Unmanned Tasking (SCOUT), a method that begins by initially identifying high-demand areas and subsequently refines UAV locations through an iterative, gradient-based update. The resulting deployment and coverage control minimizes a weighted cost function that integrates spatial distances and demand density, thereby enhancing both resource accessibility and equity for the target areas. Evaluation results show that SCOUT consistently outperforms 3D K-means and weighted Voronoi methods. Implementing a continuous deployment task further underscores the strong potential of the method for dynamic decision support in complex and rapidly changing environments.
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This content will become publicly available on November 5, 2026
Robust UAV Trajectory Design for Non-Uniform Coverage
Non-uniform coverage is a central challenge in UAV-assisted wireless networks, yet most existing methods either assume uniform demand or depend on precise user locations. This letter proposes a distribution-centric trajectory design that uses only statistical demand maps to generate ergodic UAV paths, guaranteeing that time-averaged presence converges to the prescribed spatial distribution. The scheme modulates UAV speed via a time-warping function for smooth transitions between high- and low-density regions, uses continuous angular adjustments, and provides rigorous convergence and complexity analyses. Simulations demonstrate that our method improves coverage fairness and distribution matching by 4.7× and 5.8×, respectively, relative to a deep-reinforcement-learning baseline, while also achieving 1.1× and 1.5× gains over an optimaltransport approach. It reduces outage probability by up to 65 % compared with uniform coverage. Experiments with the Telecom Italia Milano dataset show that the resulting coverage closely matches real urban demand patterns under wind and sensor disturbances.
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- PAR ID:
- 10655509
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Communications Letters
- Volume:
- 30
- ISSN:
- 1089-7798
- Page Range / eLocation ID:
- 188 to 192
- Subject(s) / Keyword(s):
- UAV trajectory design, non-uniform coverage, stochastic processes.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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