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Choong Seon Hong
Researcher at Kyung Hee University
Publications - 1088
Citations - 18047
Choong Seon Hong is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 47, co-authored 1015 publications receiving 12065 citations. Previous affiliations of Choong Seon Hong include University of Maryland University College & National Computerization Agency.
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Proceedings ArticleDOI
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
TL;DR: This work formulates a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs and obtains the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDl learning time, accuracy level, and UE energy cost.
Journal ArticleDOI
Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience
TL;DR: In this article, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality of experience (QoE) of wireless devices in a cloud radio access network is studied.
Proceedings ArticleDOI
Security in wireless sensor networks: issues and challenges
TL;DR: The security threats are identified, proposed security mechanisms are reviewed and the holistic view of security for ensuring layered and robust security in wireless sensor networks is discussed.
Journal ArticleDOI
Human Behavior Analysis by Means of Multimodal Context Mining.
Oresti Banos,Claudia Villalonga,Jaehun Bang,Taeho Hur,Donguk Kang,Sangbeom Park,Thien Huynh-The,Vui Le-Ba,Muhammad Bilal Amin,Muhammad Asif Razzaq,Wahajat Ali Khan,Choong Seon Hong,Sungyoung Lee +12 more
TL;DR: This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion and extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner.
Journal ArticleDOI
Energy Efficient Federated Learning Over Wireless Communication Networks
TL;DR: An iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived and can reduce up to 59.5% energy consumption compared to the conventional FL method.