Kai Han

Work place: Zhongyuan University of Technology, Zhengzhou, P.R.China

E-mail: hankai@gmail.com

Website:

Research Interests: Computer systems and computational processes, Computer Networks

Biography

Kai Han is presently an associate professor with the School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China. He received his B.S. and Ph.D. degrees in Computer Science from University of Science and Technology of China, Hefei, China, in 1997 and 2004, respectively. He was a postdoctoral fellow from 2005 to 2008 at the Confederative Advanced Research Center, Suzhou, China, which is co-sponsored by City University of Hong Kong and University of Science and Technology of China. Dr. Han's research interests are in the general areas of computer networks and telecommunication systems, with focuses on wireless mesh networks, wireless ad hoc networks, and cognitive radio networks.

Author Articles
Reducing Multicast Redundancy and Latency in Multi-Interface Multi-Channel Wireless Mesh Networks

By Kai Han Yang Liu

DOI: https://doi.org/10.5815/ijieeb.2009.01.06, Pub. Date: 8 Oct. 2009

In wireless mesh networks, each node can be equipped with multiple network interface cards tuned to different channels. In this paper, we study the problem of collision-free multicast in multi-interface multi-channel wireless mesh networks. The concept of interface redundancy is proposed as a new criterion for the multicast/broadcast redundancy in wireless mesh networks, and we prove that building a multicast/broadcast tree with the minimum interface redundancy is NP-hard. We also prove that the minimum-latency multicasting problem in multi-channel wireless mesh networks is NP-hard. We present two heuristic-based algorithms which jointly reduce the interface redundancy and the multicast latency. Since broadcast can be considered as a special case of multicast, an approximate algorithm for low-redundancy broadcast tree construction is also proposed, which has a constant approximation ratio. Finally, the simulation results prove the effectiveness of our approach.

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