Reducing the Mission Time of Drone Applications through Location-Aware Edge Computing

Abstract

In data-driven applications, which go beyond simple data collection, drones may need to process sensor measurements at certain locations, during the mission. However, the onboard computing platforms typically have strong resource limitations, which may lead to significant delays and long mission times. To address this problem, we explore the potential of offloading heavyweight computations from the drone to nearby edge computing infrastructure. We discuss a concrete implementation for a service-oriented application software stack, which takes offloading decisions based on the expected service invocation time and the locations of the servers expected to be available in the mission area. We evaluate our implementation using an experimental setup that combines a hardware-in-the-loop and software-in-the-loop configuration. Our results show that the proposed approach can reduce the total mission time significantly, by up to 48% vs local-only processing, and by 10% vs more naive opportunistic offloading, depending on the mission scenario.