# A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems

arXiv: https://arxiv.org/abs/2301.06363 · HTML: https://mlsanity.com/paper/2301.06363/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [SwarmControl: An Automated Distributed Control Framework for Self-Optimizing Drone Networks](https://mlsanity.com/paper/2005.09781/) (2020)
- [SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing](https://mlsanity.com/paper/2105.15105/) (2021)
- [Multi-robot persistent surveillance with connectivity constraints](https://mlsanity.com/paper/1909.07703/) (2019)
- [On New Approaches of Maximum Weighted Target Coverage and Sensor Connectivity: Hardness and Approximation](https://mlsanity.com/paper/1811.00487/) (2018)
- [Dynamic Pricing and Capacity Allocation of UAV-provided Mobile Services](https://mlsanity.com/paper/1812.02910/) (2018)
- [Interference Avoidance Position Planning in Dual-hop and Multi-hop UAV Relay Networks](https://mlsanity.com/paper/1907.01930/) (2019)
- [Distributed Collaborative 3D-Deployment of UAV Base Stations for On-Demand Coverage](https://mlsanity.com/paper/2002.05339/) (2020)

## Built on by (most cited first, 2 of 2)

- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)

## Abstract (arXiv)

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 A2-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 (A2-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 A2-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A2-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 A2-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.