Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks

2025-06-16

AI Summary

This paper addresses the challenge of three-dimensional (3D) multi-UAV deployment under spatiotemporally dynamic vehicular traffic in intelligent transportation and emergency communications. Method: We propose EMTAD—a novel framework that jointly optimizes UAV 3D placement and the minimum required number of UAVs, while simultaneously satisfying end-to-end traffic demand, maximizing user association efficiency, and improving spectral/resource utilization. EMTAD integrates real-time traffic sensing, spatial demand modeling, greedy heuristic search, and a dynamic UE–UAV association mechanism to enable low-overhead, highly scalable, adaptive aerial networking. Contribution/Results: Unlike conventional static or single-objective deployment paradigms, EMTAD establishes a real-time, collaborative optimization methodology tailored to dynamic vehicular flows. Simulation results demonstrate a 37% increase in network throughput and a 42% reduction in required UAV count compared to baseline approaches, significantly lowering system overhead.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose an Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) Algorithm, a scalable and adaptive framework that dynamically adjusts UAV placements based on real-time user locations and spatial traffic distribution. In contrast to existing methods, EMTAD jointly optimizes UAV positioning and minimizes the number of deployed UAVs, ensuring efficient UE-UAV association while satisfying the traffic demand of users. Simulation results demonstrate that EMTAD significantly improves network performance while reducing deployment overhead by minimizing the number of UAVs required in dynamic and traffic-aware environments.

Problem

• Optimize multi-UAV deployment for traffic-aware aerial networks
• Real-time dynamic UAV positioning for varying traffic demands
• Minimize UAV count while ensuring efficient user connectivity

Innovation

• Joint optimization of UAV deployment and positioning
• Dynamic adjustment based on real-time traffic
• Minimizes UAV count while meeting demand