mlsanityData to 7 Oct 2026

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

arXiv 2105.11013 ↗First posted 23 May 2021cs.DC cs.LG cs.SYcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 23 May 2021.

Builds on

14 of 47 references.

References · 14

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2 more
  1. 2018Autonomous UAV Navigation Using Reinforcement Learningcited by 6
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Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 3; 2024: 1.

8 papers.

  1. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 23
  2. 2023Vision-based Learning for Drones: A Surveycited by 9
  3. 2022Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarmscited by 5
  4. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  5. 2022RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systemscited by 4
  6. 2022The Effects of Partitioning Strategies on Energy Consumption in Distributed CNN Inference at The Edgecited by 1
  7. 2024Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devicescited by 1
  8. 2023Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systemscited by 0