  To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the
execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV
networks, the successful transmission of these tasks to the edge is severely
challenged due to severe bandwidth constraints. For this reason, we propose a
novel A$^2$-UAV framework to optimize the number of correctly executed tasks at
the edge. In stark contrast with existing art, we take an application-aware
approach and formulate a novel pplication-Aware Task Planning Problem
(A$^2$-TPP) that takes into account (i) the relationship between deep neural
network (DNN) accuracy and image compression for the classes of interest based
on the available dataset, (ii) the target positions, (iii) the current
energy/position of the UAVs to optimize routing, data pre-processing and target
assignment for each UAV. We demonstrate A$^2$-TPP is NP-Hard and propose a
polynomial-time algorithm to solve it efficiently. We extensively evaluate
A$^2$-UAV through real-world experiments with a testbed composed by four DJI
Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with
four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and
MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet
dataset. Results show that A$^2$-UAV attains on average around 38% more
accomplished tasks than the state-of-the-art, with 400% more accomplished tasks
when the number of targets increases significantly. To allow full
reproducibility, we pledge to share datasets and code with the research
community.

Networking and Internet Architecture;Computer Vision and Pattern Recognition;Machine Learning