H
Hee Yong Youn
Researcher at Sungkyunkwan University
Publications - 359
Citations - 3123
Hee Yong Youn is an academic researcher from Sungkyunkwan University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 22, co-authored 358 publications receiving 2794 citations.
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Journal ArticleDOI
Energy Efficient Routing Protocols for Mobile Ad Hoc Networks
Chansu Yu,Ben Lee,Hee Yong Youn +2 more
TL;DR: This article surveys and classifies the energy-aware routing protocols proposed for MANETs and finds that each protocol has definite advantages/disadvantages and is well suited for certain situations.
Proceedings ArticleDOI
Non-blocking, localized routing algorithm for balanced energy consumption in mobile ad hoc networks
TL;DR: A new routing algorithm, called local energy-aware routing (LEAR), is presented, which achieves a trade-off between balanced energy consumption and shortest routing delay, and at the same time avoids the blocking and route cache problems.
Proceedings ArticleDOI
A Novel Cluster Head Selection Method based on K-Means Algorithm for Energy Efficient Wireless Sensor Network
TL;DR: An efficient cluster head selection method using K-means algorithm to maximize the energy efficiency of wireless sensor network is proposed based on the concept of finding the cluster head minimizing the sum of Euclidean distances between the head and member nodes.
Proceedings Article
Proceedings of the 10th international conference on Ubiquitous computing
TL;DR: The set of accepted papers constitutes the largest number of papers accepted to a UbiComp conference to date, representing an overall acceptance rate of 19%.
Journal ArticleDOI
Performance analysis of finite buffered multistage interconnection networks
Youngsong Mun,Hee Yong Youn +1 more
TL;DR: The authors present a model which can accurately evaluate the performance of single-buffered and multibuffered MINs (multistage interconnection networks) with 2*2 switching elements (SESs) and reveal that the proposed models are consistently much more accurate irrespective of the size of the network, buffer, and traffic condition.