End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms
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. How...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/10832 |
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| author | Jeongho Kim Joonho Seon Soohyun Kim Seongwoo Lee Jinwook Kim Byungsun Hwang Youngghyu Sun Jinyoung Kim |
| author_facet | Jeongho Kim Joonho Seon Soohyun Kim Seongwoo Lee Jinwook Kim Byungsun Hwang Youngghyu Sun Jinyoung Kim |
| author_sort | Jeongho Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c88c6f2932a4be29a47064d252e8c72 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8c88c6f2932a4be29a47064d252e8c722024-12-13T16:21:53ZengMDPI AGApplied Sciences2076-34172024-11-0114231083210.3390/app142310832End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle SwarmsJeongho Kim0Joonho Seon1Soohyun Kim2Seongwoo Lee3Jinwook Kim4Byungsun Hwang5Youngghyu Sun6Jinyoung Kim7Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaResearch and Development Department, SMART EVER, Co., Ltd., Seoul 01886, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaAn 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.https://www.mdpi.com/2076-3417/14/23/10832resource-constrained UAV swarmdistributed inferenceresilient UAV systemdeep reinforcement learningend-to-end latency optimization |
| spellingShingle | Jeongho Kim Joonho Seon Soohyun Kim Seongwoo Lee Jinwook Kim Byungsun Hwang Youngghyu Sun Jinyoung Kim End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms Applied Sciences resource-constrained UAV swarm distributed inference resilient UAV system deep reinforcement learning end-to-end latency optimization |
| title | End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms |
| title_full | End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms |
| title_fullStr | End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms |
| title_full_unstemmed | End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms |
| title_short | End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms |
| title_sort | end to end latency optimization for resilient distributed convolutional neural network inference in resource constrained unmanned aerial vehicle swarms |
| topic | resource-constrained UAV swarm distributed inference resilient UAV system deep reinforcement learning end-to-end latency optimization |
| url | https://www.mdpi.com/2076-3417/14/23/10832 |
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