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On the Energy Consumption of Rotary Wing and Fixed Wing UAVs in Flying Networks

Pedro Ribeiro, André Coelho, Rui Campos

TL;DR

This work extends the MUAVE energy simulator to eMUAVE, enabling energy-consumption evaluation for both rotary-wing and fixed-wing UAVs deployed as Flying Access Points in Flying Networks. It integrates state-of-the-art energy models and the SUPPLY trajectory algorithm to compare propulsion energy across UAV types under reference and random networking scenarios. Key contributions include the open-source eMUAVE tool and a comprehensive energy-focused evaluation showing rotary-wing UAVs generally outperform fixed-wing UAVs in energy efficiency, with fixed-wing viability depending on trajectory geometry and radius. The findings inform UAV selection and trajectory design for energy-aware flying networks, with implications for disaster response and event coverage where NTNs are deployed.

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly used to enable wireless communications. Due to their characteristics, such as the ability to hover and carry cargo, UAVs can serve as communications nodes, including Wi-Fi Access Points and Cellular Base Stations. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). Additionally, we developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate the UAV energy consumption, specifically when using the SUPPLY algorithm. However, MUAVE was initially designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that allows the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. Our energy consumption evaluation using eMUAVE considers reference and random networking scenarios. The results show that fixed-wing UAVs can be employed in the majority of networking scenarios. However, rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following the trajectories defined by SUPPLY.

On the Energy Consumption of Rotary Wing and Fixed Wing UAVs in Flying Networks

TL;DR

This work extends the MUAVE energy simulator to eMUAVE, enabling energy-consumption evaluation for both rotary-wing and fixed-wing UAVs deployed as Flying Access Points in Flying Networks. It integrates state-of-the-art energy models and the SUPPLY trajectory algorithm to compare propulsion energy across UAV types under reference and random networking scenarios. Key contributions include the open-source eMUAVE tool and a comprehensive energy-focused evaluation showing rotary-wing UAVs generally outperform fixed-wing UAVs in energy efficiency, with fixed-wing viability depending on trajectory geometry and radius. The findings inform UAV selection and trajectory design for energy-aware flying networks, with implications for disaster response and event coverage where NTNs are deployed.

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly used to enable wireless communications. Due to their characteristics, such as the ability to hover and carry cargo, UAVs can serve as communications nodes, including Wi-Fi Access Points and Cellular Base Stations. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). Additionally, we developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate the UAV energy consumption, specifically when using the SUPPLY algorithm. However, MUAVE was initially designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that allows the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. Our energy consumption evaluation using eMUAVE considers reference and random networking scenarios. The results show that fixed-wing UAVs can be employed in the majority of networking scenarios. However, rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following the trajectories defined by SUPPLY.

Paper Structure

This paper contains 11 sections, 5 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. UAV Types
  4. UAV Energy Consumption Models
  5. Sustainable multi-UAV Performance-aware Placement Algorithm
  6. Enhanced Multi-UAV Energy Consumption Simulator
  7. UAV Energy Consumption Evaluation
  8. Energy Evaluation Under Reference Networking Scenarios
  9. Energy Evaluation for Random Networking Scenarios
  10. Discussion
  11. Conclusion

Figures (5)

  • Figure 1: Intersection area perimeter and SUPPLY trajectories.
  • Figure 2: Comparison of energy consumption per hour for a reference scenario with 2 GUs
  • Figure 3: Comparison of energy consumption per hour for a reference scenario with 5 GUs.
  • Figure 4: Comparison of energy consumption per hour for a reference scenario with 10 GUs.
  • Figure 5: Percentage increase in energy consumption per hour of fixed-wing UAVs compared to rotary-wing UAVs, for each distinct number of GUs – 2, 5, and 10.