Abstract
WSN consists of a large number of micro sensor nodes with limited resources. The limited battery resources of these nodes have become an important bottleneck to the development of WSN. In order to improve the energy efficiency and prolong the network life cycle, we propose a clustering method GDGA based on improved genetic algorithm. In this method, we divide the sensor area into two parts: the first part is that the distance from the node to BS is less than the transmission threshold of the node. For the nodes in this area, we do not cluster but directly transmit the data to BS. The part beyond the threshold of BS is divided into the second region. The nodes in this region will be clustered using the improved genetic algorithm according to the characteristics of node distribution. Simulation results show that compared with other four protocols, GDGA has the highest energy efficiency, lower average energy consumption of cluster head and longer life cycle of the whole network.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 61771410, No. 61876089), by the Postgraduate Innovation Fund Project by Southwest University of Science and Technology (No. 19ycx0106), by the Artificial Intelligence Key Laboratory of Sichuan Province (No. 2017RYY05, No. 2018RYJ03), by the Zigong City Key Science and Technology Plan Project (2019YYJC16), by and by the Horizontal Project (No. HX2017134, No. HX2018264, No. E10203788, HX2019250).
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Song, Y., Liu, Z., Xiao, H. (2020). A Clustering Algorithm for Wireless Sensor Networks Using Geographic Distribution Information and Genetic Algorithms. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_14
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DOI: https://doi.org/10.1007/978-3-030-57881-7_14
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