Modeling Inter-drone Interference as a Service in Skyway Networks

TL;DR

This paper investigates how inter-drone interference affects delivery efficiency in skyway networks through experiments and modeling, providing insights and a predictive model to improve multi-drone operations.

Contribution

It introduces a comprehensive experimental analysis and a formalized predictive model for inter-drone interference in skyway networks, a novel approach in drone network management.

Findings

Inter-drone interference significantly impacts power consumption and delivery times.

The developed model accurately forecasts interference effects.

Experimental results validate the model's effectiveness.

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.