Kun-chan Lan
National Cheng Kung University
112 Papers
740 Citations
Kun-chan Lan is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 22, co-authored 105 publications. Previous affiliations of Kun-chan Lan include China Medical University (Taiwan) & University of Southern California.
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Papers
A Survey of Opportunistic Networks
Chung-Ming Huang,Kun-chan Lan,Chang-Zhou Tsai +2 more
- 25 Mar 2008
TL;DR: Some research challenges in an opportunistic network are discussed, where the intermediate nodes may take custody of data during the blackout and forward it when the connectivity resumes.
Measuring and Improving the Performance of Network Mobility Management in IPv6 Networks
TL;DR: This paper developed a network mobility testbed and implemented the network mobility (NEMO) basic support protocol and identified problems in the architecture which affect the handoff and routing performance, and extended a previously proposed route optimization (RO) scheme, OptiNets.
Realistic mobility models for Vehicular Ad hoc Network (VANET) simulations
Kun-chan Lan,Chien-Ming Chou +1 more
- 01 Oct 2008
TL;DR: This work introduces a tool MOVE, a tool that allows users to rapidly generate realistic mobility models for VANET simulations and evaluates the effects of details of mobility models in three case studies of VANet simulations to show that selecting sufficient level of details in the simulation is critical forVANET protocol design.
On the correlation of Internet flow characteristics
Kun-chan Lan,John Heidemann +1 more
- 01 Jan 2003
TL;DR: This work systematically characterize prior definitions for the properties of such heavy-hitter traffic and shows that there are strong correlations between some combinations of size, rate and burstiness.
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An intelligent driver location system for smart parking
Kun-chan Lan,Wen-Yuah Shih +1 more
TL;DR: This work designs a phone-based system to track a driver's trajectory to detect when they are about to leave their parking spot, applying a waist-mounted PDR method that can measure the driver's moving distance with a high accuracy.
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