• The KIPS Transactions:PartC
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• v.15C no.4
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• pp.273-280
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• 2008

In WSN(Wireless Sensor Network) many routing algorithms such as LEACH, PEGASIS and PEDEP consisting of sensor nodes with limited energy have been proposed to extend WSN lifetime. Under the assumption of perfect fusion, these algorithms used convergecast that periodically collects sensed data from all sensor nodes to a base station. But because these schemes studied less energy consumption for a convergecast as well as fairly energy consumption altogether, the minimum energy consumption for a convergecast was not focused enough nor how topology influences to energy consumption. This paper deals with routing topology and energy consumption for a single convergecast in the following ways. We chose major WSN topology as MSC(Minimum Spanning Chain)s, MSTs, PEGASIS chains and proposed LECSEN chains. We solved the MSC length by Linear Programming(LP) and propose the LECSEN chain to compete with MST and MSC. As a result of simulation by Monte Carlo method for calculation of the topology length and standard deviation of link length, we learned that LECSEN is competitive with MST in terms of total energy consumption and shows the best with the view of even energy consumption at the sensor nodes. Thus, we concluded LECSEN is a very useful routing topology in WSN.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.