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Applied Sciences
Volume 14
Issue 23
10.3390/app142310832
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms

by
Jeongho Kim
1,
Joonho Seon
1,
Soohyun Kim
1,
Seongwoo Lee
1,
Jinwook Kim
1,
Byungsun Hwang
1,
Youngghyu Sun
2 and
Jinyoung Kim
1,*
1
Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
2
Research and Development Department, SMART EVER, Co., Ltd., Seoul 01886, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(23), 10832; https://doi.org/10.3390/app142310832
Submission received: 25 October 2024 / Revised: 16 November 2024 / Accepted: 21 November 2024 / Published: 22 November 2024
(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)
Versions Notes

Abstract

An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs.
Keywords: resource-constrained UAV swarm; distributed inference; resilient UAV system; deep reinforcement learning; end-to-end latency optimization. resource-constrained UAV swarm; distributed inference; resilient UAV system; deep reinforcement learning; end-to-end latency optimization.

Share and Cite

MDPI and ACS Style

Kim, J.; Seon, J.; Kim, S.; Lee, S.; Kim, J.; Hwang, B.; Sun, Y.; Kim, J. End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Appl. Sci. 2024, 14, 10832. https://doi.org/10.3390/app142310832

AMA Style

Kim J, Seon J, Kim S, Lee S, Kim J, Hwang B, Sun Y, Kim J. End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Applied Sciences. 2024; 14(23):10832. https://doi.org/10.3390/app142310832

Chicago/Turabian Style

Kim, Jeongho, Joonho Seon, Soohyun Kim, Seongwoo Lee, Jinwook Kim, Byungsun Hwang, Youngghyu Sun, and Jinyoung Kim. 2024. "End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms" Applied Sciences 14, no. 23: 10832. https://doi.org/10.3390/app142310832

APA Style

Kim, J., Seon, J., Kim, S., Lee, S., Kim, J., Hwang, B., Sun, Y., & Kim, J. (2024). End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Applied Sciences, 14(23), 10832. https://doi.org/10.3390/app142310832

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