Evolutionary genetic algorithm for efficient clustering of wireless sensor networks

Authors:
Hyun-Sik Seo;Se-Jin Oh;Chae-Woo Lee
Affiliations:
School of Electrical and Computer Engineering, Ajou University, Suwon, Korea;School of Electrical and Computer Engineering, Ajou University, Suwon, Korea;School of Electrical and Computer Engineering, Ajou University, Suwon, Korea
Venue:
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Year:
2009

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Abstract

Sensor nodes forming a sensor network usually have limited energy capacity so it is important to minimize sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Much attention has been given to the clustering technique as an efficient way of reducing the energy consumption of a sensor node. Energy saving results can vary greatly depending on the number and size of clusters and the distance among the sensor nodes. In this paper, we aim to find an optimal cluster formation by applying a genetic algorithm in which the chromosome contains the information about the relative position of the nodes. The Location-aware two-dimensional GA (LA2D-GA) proposed in this paper can performs more efficient gene evolution than one-dimensional GA (1D-GA) by giving unique location information to each node. The effectiveness of our algorithm is shown by simulation.