H
Hyung-Sin Kim
Researcher at Seoul National University
Publications - 87
Citations - 1460
Hyung-Sin Kim is an academic researcher from Seoul National University. The author has contributed to research in topics: Wireless network & Routing protocol. The author has an hindex of 17, co-authored 82 publications receiving 1040 citations. Previous affiliations of Hyung-Sin Kim include Google & Samsung.
Papers
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Journal ArticleDOI
Challenging the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL): A Survey
TL;DR: This paper reviewed over 97 RPL-related academic research papers published by major academic publishers and presented a topic-oriented survey for these research efforts, finding that only 40.2% of the papers evaluate RPL through experiments using implementations on real embedded devices.
Journal ArticleDOI
Load Balancing Under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks
TL;DR: This article proposes a simple yet effective queue utilization based RPL (QU-RPL) that achieves load balancing and significantly improves the end-to-end packet delivery performance compared to the standard RPL.
Proceedings ArticleDOI
QU-RPL: Queue utilization based RPL for load balancing in large scale industrial applications
TL;DR: A simple yet effective queue utilization based RPL (QU-RPL) is proposed that significantly improves end-to-end packet delivery performance compared to the standard RPL and is very effective in lowering the queue losses and increasing the packet delivery ratio.
Proceedings ArticleDOI
ALICE: autonomous link-based cell scheduling for TSCH
TL;DR: ALICE is introduced, a novel autonomous link-based cell scheduling scheme which allocates a unique cell for each directional link (a pair of nodes and traffic direction) by closely interacting with the routing layer and using only local information, without any additional communication overhead.
Proceedings ArticleDOI
MARVEL: Enabling Mobile Augmented Reality with Low Energy and Low Latency
TL;DR: This paper proposes a system architecture which uses local inertial tracking, local optical flow, and visual tracking in the cloud synergistically, and investigates how to minimize the overhead for image computation and offloading.