Han Choe

Seoul National University, Seoul, Seoul, South Korea

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Publications (2)0.43 Total impact

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    ABSTRACT: Over the last several years, various clustering algorithms for wireless sensor networks have been proposed to prolong network lifetime. Most clustering algorithms provide an equal cluster size using node’s ID, degree and etc. However, many of these algorithms heuristically determine the cluster size, even though the cluster size significantly affects the energy consumption of the entire network. In this paper, we present a theoretical model and propose a simple clustering algorithm called Location-based Unequal Clustering Algorithm (LUCA), where each cluster has a different cluster size based on its location information which is the distance between a cluster head and a sink. In LUCA, in order to minimize the energy consumption of entire network, a cluster has a larger cluster size as increasing distance from the sink. Simulation results show that LUCA achieves better performance than conventional equal clustering algorithm for energy efficiency. KeywordsEnergy efficiency–Location information–Unequal clustering–Wireless sensor networks
    Wireless Personal Communications 02/2011; 56(4):715-731. · 0.43 Impact Factor
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    ABSTRACT: To enhance the performance of overlay multicast networks, the overlay multicast tree should be optimized. This optimization prob-lem is a minimum diameter, degree-limited spanning tree (MDDLST) problem which is known to be NP-Hard. We present a new scheme to optimize an overlay multicast tree dynamically. Our algorithm can adapt the tree structure to the dynamic membership and network situation. And each member reduces an overlay tree diameter using directional maximum overlay costs and an edge change. By this distributed oper-ation, the tree diameter is converged to the sub-optimal. From graph theoretical analysis and simulation results, we show that the proposed scheme can reduce a tree diameter with low computation overhead.
    01/2004;