# Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

arXiv: https://arxiv.org/abs/2105.11013 · HTML: https://mlsanity.com/paper/2105.11013/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Distributed Deep Convolutional Neural Networks for the Internet-of-Things](https://mlsanity.com/paper/1908.01656/) (2019)
- [Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.05053/) (2018)
- [Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique](https://mlsanity.com/paper/1901.10832/) (2019)
- [DroNet: Efficient convolutional neural network detector for real-time UAV applications](https://mlsanity.com/paper/1807.06789/) (2018)
- [Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective](https://mlsanity.com/paper/1805.07182/) (2018)
- [Towards Collaborative Intelligence Friendly Architectures for Deep Learning](https://mlsanity.com/paper/1902.00147/) (2019)
- [Autonomous UAV Navigation Using Reinforcement Learning](https://mlsanity.com/paper/1801.05086/) (2018)
- [Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach](https://mlsanity.com/paper/1808.04617/) (2018)

## Built on by (most cited first, 8 of 8)

- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems](https://mlsanity.com/paper/2208.13032/) (2022)
- [The Effects of Partitioning Strategies on Energy Consumption in Distributed CNN Inference at The Edge](https://mlsanity.com/paper/2210.08392/) (2022)
- [Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices](https://mlsanity.com/paper/2406.14185/) (2024)
- [Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems](https://mlsanity.com/paper/2307.11499/) (2023)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board. In this paper, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely Optimal UAV-based Layer Distribution (OULD) and OULD with Mobility Prediction (OULD-MP), were run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.