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

TL;DR

This paper presents a novel distributed deep neural network inference approach for resource-limited UAVs, optimizing latency and adapting to UAV mobility, to enhance real-time surveillance capabilities without relying on server-based processing.

Contribution

It introduces a new optimization-based methodology for distributing CNN inference across UAVs, incorporating mobility prediction to improve performance in dynamic environments.

Findings

Optimization outperforms heuristic approaches in latency reduction.

Mobility prediction enhances system adaptability and efficiency.

Proposed methods are validated through high-performance computing simulations.

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.