On the Energy Consumption of UAV Edge Computing in Non-Terrestrial Networks

TL;DR

This paper models the energy consumption of UAVs, HAPs, and satellites in non-terrestrial networks, demonstrating that edge computing via data offloading can enhance UAV battery life and reduce delays.

Contribution

It introduces a comprehensive energy consumption model for UAV edge computing in NTNs and evaluates the benefits of data offloading for system performance.

Findings

Edge computing improves UAV autonomy in many configurations.

Data offloading reduces end-to-end delay.

Supporting NTNs enhances UAV operational efficiency.

Abstract

During the last few years, the use of Unmanned Aerial Vehicles (UAVs) equipped with sensors and cameras has emerged as a cutting-edge technology to provide services such as surveillance, infrastructure inspections, and target acquisition. However, this approach requires UAVs to process data onboard, mainly for person/object detection and recognition, which may pose significant energy constraints as UAVs are battery-powered. A possible solution can be the support of Non-Terrestrial Networks (NTNs) for edge computing. In particular, UAVs can partially offload data (e.g., video acquisitions from onboard sensors) to more powerful upstream High Altitude Platforms (HAPs) or satellites acting as edge computing servers to increase the battery autonomy compared to local processing, even though at the expense of some data transmission delays. Accordingly, in this study we model the energy consumption of UAVs, HAPs, and satellites considering the energy for data processing, offloading, and hovering. Then, we investigate whether data offloading can improve the system performance. Simulations demonstrate that edge computing can improve both UAV autonomy and end-to-end delay compared to onboard processing in many configurations.