## 6 Conclusions  
In this paper, a deep reinforcement learning-based resilient layer distribution (DRL-RLD) system was proposed to optimize resilient distributed CNN inference in resource-constrained UAV swarm. The proposed system minimizes the end-to-end latency by dynamically allocating CNN layers based on the position of the UAVs and their available resources, considering the U2U and U2G communication links. The system can also maintain the continuity of the network by reallocating layers when UAVs are inoperable due to internal or external factors. The performance of the proposed system was evaluated through simulations under different CNN models, as well as the number of UAVs and available resources. The simulation results proved that the proposed system can outperform the baselines in terms of end-to-end latency. It was further verified that mission continuity can be maintained through layer reassignment, even when inoperable UAVs occur. The proposed system can be a viable approach for disaster management, search and rescue, and surveillance due to its enhanced resilience and capabilities for real-time inference. In future work, the impact of various environmental factors, such as weather conditions, terrain, and obstacles (e.g., buildings or dense foliage), should be further examined to verify the  
Figure 5: Performance comparison of the proposed system under different memory constraints: (a) AlexNet; (b) YOLO; (c) VGG16. Figure 6: The performance of the proposed model in a scenario with inoperable UAVs. robustness of the proposed system model. Additionally, a dynamic environment model that accounts for the Doppler effect is necessary to better understand the impact of UAV mobility on communication link quality. Furthermore, the battery life constraints of UAVs should be integrated into future works, as real-world deployments are often limited by the power supply available to each UAV. Addressing these factors will improve the robustness, and generalizability of the proposed system in practical, real-world applications. Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and J.S. ; software, [PERSON] ([PERSON]) and S.K. ; validation, [PERSON] ([PERSON]), S.K. and J.S. ; formal analysis, [PERSON] ([PERSON]) and S.L. ; investigation, [PERSON] ([PERSON]), B.H. and [PERSON]. ([PERSON]); resources, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and Y.S. ; data curation, [PERSON] ([PERSON]), S.K. and Y.S. ; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, J.S., S.K., S.L., B.H., J.K. ([PERSON]) and Y.S. ; visualization, J.K. ([PERSON]); supervision, J.K. ([PERSON]); project administration, J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2021-0-00892-004, Research on advanced physical layer technologies of LEO(low-earth orbit) satellite communication systems for ubiquitous intelligence in space), and in part by Research Grant of Kwangwoon University in 2024. Not applicable. **Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.image-net.org/challenges/LSVRC/2014/](https://www.image-net.org/challenges/LSVRC/2014/)]([https://www.image-net.org/challenges/LSVRC/2014/](https://www.image-net.org/challenges/LSVRC/2014/)) and [[http://host.robots.ox.ac.uk/pascal/VOC/voc2007/](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/)]([http://host.robots.ox.ac.uk/pascal/VOC/voc2007/](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/)), accessed on 20 November 2024. **Conflicts of Interest**: Author [PERSON] was employed by the company SMART EVER, Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: