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.

We study the vehicle routing problem for a system where there is some uncertainty regarding both the cost of travel and the refueling opportunities. Travel cost stands for the energy spent by the vehicle to move between locations. Refueling opportunities are offered at known locations where the vehicle can harvest or re-gain some of the lost energy. The objective is to visit a set of predefined locations without exhausting the energy of the vehicle. We describe the problem in a formal way, and propose a heuristic algorithm for taking routing decisions at runtime. We evaluate the algorithm for a grid topology as a function of the number of locations to be visited and the autonomy degree of the vehicle, showing that the proposed algorithm achieves good results as long as the energy margins are not very tight. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.