Distributed CNN Inference on Resource-Constrained UAVs for Surveillance
Systems: Design and Optimization
- URL: http://arxiv.org/abs/2105.11013v1
- Date: Sun, 23 May 2021 20:19:43 GMT
- Title: Distributed CNN Inference on Resource-Constrained UAVs for Surveillance
Systems: Design and Optimization
- Authors: Mohammed Jouhari, Abdulla Al-Ali, Emna Baccour, Amr Mohamed, Aiman
Erbad, Mohsen Guizani, Mounir Hamdi
- Abstract summary: 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.
Thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications.
Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board.
- Score: 43.9909417652678
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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