W
Woo-Sung Jung
Researcher at Electronics and Telecommunications Research Institute
Publications - 45
Citations - 515
Woo-Sung Jung is an academic researcher from Electronics and Telecommunications Research Institute. The author has contributed to research in topics: Network packet & Wireless sensor network. The author has an hindex of 11, co-authored 42 publications receiving 363 citations. Previous affiliations of Woo-Sung Jung include Ajou University.
Papers
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Journal ArticleDOI
QGeo: Q-Learning-Based Geographic Ad Hoc Routing Protocol for Unmanned Robotic Networks
TL;DR: A novel protocol that uses Q-learning-based geographic routing (QGeo) to improve the network performance of unmanned robotic networks and finds that QGeo has a higher packet delivery ratio and a lower network overhead than existing methods.
Journal ArticleDOI
Efficient clustering-based data aggregation techniques for wireless sensor networks
TL;DR: Two hybrid clustering based data aggregation mechanisms are proposed that can increase the data aggregation efficiency as well as improve energy efficiency and other important issues compared to previous works.
Proceedings ArticleDOI
A Hybrid Approach for Clustering-Based Data Aggregation in Wireless Sensor Networks
TL;DR: This work proposes a hybrid clustering based data aggregation scheme that can adaptively choose a suitable clustering technique depending on the status of the network, increasing the data aggregation efficiency as well as energy consumption and successful data transmission ratio.
Journal ArticleDOI
Lightweight driver monitoring system based on multi-task mobilenets
TL;DR: A lightweight driver monitoring system using a resource sharing device in a vehicle (e.g., a driver’s mobile phone) and based on Multi-Task Mobilenets (MT-Mobilenets), which consists of the Mobilenet’ base and multi-task classifier.
Patent
Hybrid clustering based data aggregation method for multi-target tracking in wireless sensor network
TL;DR: In this article, the multi-target tracking may be efficiently performed in a heterogeneous sensor network by combining clustering methods and adaptively varying the clustering method for reducing the amount of data to be transmitted.