Modeling Inter-drone Interference as a Service in Skyway Networks

2026-01-05

arXiv.org

AI Summary

This study addresses the significant degradation in delivery efficiency and system performance caused by inter-drone interference in multi-UAV aerial road networks. Through an indoor experimental platform, the authors systematically compare the operational performance of single and multiple drones following predefined paths, analyzing interference behaviors both during flight and at charging stations. For the first time, inter-drone interference is modeled as a quantifiable and predictable service. By integrating controlled experiments, multi-scenario testing, and node- and segment-level data analysis, a predictive model is developed based on key metrics such as power consumption and delivery time. The resulting interference dataset validates the model’s accuracy in forecasting multi-UAV interference effects, thereby enhancing scheduling efficiency and overall performance evaluation in aerial road networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times, are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences.

Problem

• inter-drone interference
• skyway networks
• delivery efficiency
• multi-drone systems
• network performance

Innovation

• inter-drone interference
• skyway networks
• predictive modeling
• multi-drone systems
• delivery efficiency