Aimed at the problems of limited energy and short network lifetime in wireless sensor network,BBOK-GA based on biogeographic algorithm optimization K-means was proposed.In the clustering stage, biogeographic algorithm optimization K-means was firstly used to prevent K-means from falling into the local optimum. According to the energy factor and distance factor, a new fitness function was designed to select optimal cluster heads and complete the clustering. And genetic algorithm was used to search the optimal routing path towards base station for cluster heads. The simulation results indicate that BBOK-GA reduces the network energy consumption,increases the network throughput and extends the network life time compared to LEACH, LEACH-C, and K-GA.
PENG Cheng
,
TAN Chong
,
LIU Hong
,
ZHENG Min
. Clustering routing algorithm for WSN based on BBO optimized K-means[J]. Journal of University of Chinese Academy of Sciences, 2024
, 41(3)
: 357
-364
.
DOI: 10.7523/j.ucas.2022.065
[1] 钱志鸿,王义君.面向物联网的无线传感器网络综述[J].电子与信息学报,2013,35(1):215-227. DOI:10.3724/SP.J.1146.2012.00876.
[2] 郭志鹏,李娟,赵友刚,等.物联网中的无线传感器网络技术综述[J].计算机与应用化学, 2019, 36(1):72-83. DOI:10.16866/j.com.app.chem201901009.
[3] Handy M J, Haase M, Timmermann D. Low energy adaptive clustering hierarchy with deterministic cluster-head selection[C]//4th International Workshop on Mobile and Wireless Communications Network. September 9-11, 2002, Stockholm, Sweden. IEEE, 2002: 368-372. DOI:10.1109/MWCN.2002.1045790.
[4] Tripathi M, Gaur M S, Laxmi V, et al. Energy efficient LEACH-C protocol for wireless sensor network[C]//Third International Conference on Computational Intelligence and Information Technology (CIIT 2013). October 18-19, 2013, Mumbai. IET, 2013: 402-405.DOI:10.1049/cp.2013. 2620.
[5] Janson S, Luczak T, Rucinski A. Random graphs[M]. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. DOI:10.1002/9781118032718.
[6] 董发志, 丁洪伟, 杨志军, 等. 基于遗传算法和模糊C均值聚类的WSN分簇路由算法[J]. 计算机应用, 2019, 39(8): 2359-2365. DOI:10.11772/j.issn.1001-9081. 2019010134.
[7] 武小年, 张楚芸, 张润莲, 等. WSN中基于改进粒子群优化算法的分簇路由协议[J]. 通信学报, 2019, 40(12): 114-123. DOI:10.11959/j.issn.1000? 436x.2019241.
[8] 凌春, 孙文胜. 基于改进蚁群算法的无线传感器网络路由[J]. 计算机工程与设计, 2019, 40(3): 627-631, 637. DOI:10.16208/j.issn1000-7024.2019.03.006.
[9] 胡春安, 叶健. 基于鲸鱼算法的无线传感器网络分簇路由算法[J]. 计算机工程与设计, 2019, 40(11): 3067-3072. DOI:10.16208/j.issn1000-7024.2019.11.002.
[10] Dan S. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702-713. DOI:10.1109/TEVC.2008.919004.
[11] Guo W A, Chen M, Wang L, et al. A survey of biogeography-based optimization[J]. Neural Computing and Applications, 2017, 28(8): 1909-1926. DOI:10.1007/s00521-016-2179-x.
[12] Jain A K. Data clustering: 50 years beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8): 651-666. DOI:10.1016/j.patrec.2009.09.011.
[13] Holland J H. Genetic algorithms[J]. Scientific American, 1992,267(1): 66-73. DOI: 10.1038/scientificamerican 0792-66.
[14] Yuan X H, Elhoseny M, El-Minir H K, et al. A genetic algorithm-based, dynamic clustering method towards improved WSN longevity[J]. Journal of Network and Systems Management, 2017, 25(1): 21-46. DOI:10.1007/s10922-016-9379-7.
[15] Bhushan S, Pal R, Antoshchuk S G. Energy efficient clustering protocol for heterogeneous wireless sensor network: a hybrid approach using GA and K-means[C]//2018 IEEE Second International Conference on Data Stream Mining & Processing. August 21-25, 2018, Lviv, Ukraine. IEEE, 2018: 381-385. DOI:10.1109/DSMP.2018.8478538.