{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:41:51Z","timestamp":1768686111664,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT","doi-asserted-by":"publisher","award":["2020R1A4A1017775"],"award-info":[{"award-number":["2020R1A4A1017775"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge\u2013cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge\u2013cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP\/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge\u2013cloud deployment was 1.91\u00d7 more than the cloud deployment and 8.5\u00d7 more than the edge deployment.<\/jats:p>","DOI":"10.3390\/s22218371","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T08:15:12Z","timestamp":1667376912000},"page":"8371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Optimizing Face Recognition Inference with a Collaborative Edge\u2013Cloud Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Paul P.","family":"Oroceo","sequence":"first","affiliation":[{"name":"Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]},{"given":"Jeong-In","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]},{"given":"Ej Miguel Francisco","family":"Caliwag","sequence":"additional","affiliation":[{"name":"Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0599-289X","authenticated-orcid":false,"given":"Sang-Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2533-3496","authenticated-orcid":false,"given":"Wansu","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"751","DOI":"10.29207\/resti.v4i4.2146","article-title":"Digital Imaging Light Energy Saving Lamp Based On A Single Board Computer","volume":"4","author":"Bangsawan","year":"2020","journal-title":"J. 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