Clustering Algorithm Research on Mixed Received Signal Strength Sequences #br#
of Multiple Nodes on Wireless Sensor Network

doi:10.3969/j.issn.1006-2475.2016.05.008

Abstract

Abstract:

It confirms number of unknown nodes without identification by dealing with mixed received signal strength sequences achieved by mobile anchor node, the clustering

results of the mixed received signal strength directly determines the positioning accuracy of unknown nodes on the wireless sensor network. EM algorithm is sensitive to the

initial values. To overcome the drawback above, this paper proposes a three stage iterative processing method, adding front and rear channel to EM algorithm. Kmeans algorithm

is used as front processing channel to improve the selection of initial values on EM algorithm. Bayesian information criterion is employed as the rear processing channel to

improve the accuracy of the clustering. Finally, the simulation result shows that the proposed algorithm can successfully estimate the number of unknown nodes, and achieve

precise received signal strength.

Key words: , received signal strength； EM algorithm； Kmeans； Bayesian information criterion

CLC Number:

TP393

CHEN Shu, LU Ying. Clustering Algorithm Research on Mixed Received Signal Strength Sequences #br# of Multiple Nodes on Wireless Sensor Network[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.05.008.

References ［17］

［1］	Kumar Anil, Karampal. Optimized rangefree 3D node localization in wireless sensor networks using firefly algorithm［C］// 2015 IEEE International Conference on Signal
	Processing and Communication (ICSC). 2015:14-19.
［2］	戴欢. 无线传感器网络定位算法及其应用研究［D］. 无锡:江南大学, 2012.
［3］	Luo Jun. Range error correction in RSSIbased wireless sensor node localization［C］// 2014 IEEE International Conference on Mechatronics and Automation (ICMA). 2014:379-
	383.
［4］	Sichitiu M L, Ramadurai V. Localization of wireless sensor networks with a mobile beacon［C］// 2004 IEEE International Conference on Mobile Adhoc and Sensor Systems.
20	04:174-183.
［5］	Chen YeongSheng, Ting YunJu, Ke ChihHeng, et al. Efficient localization scheme with ring overlapping by utilizing mobile anchors in wireless sensor networks［C］//
20	10 4th International Conference on Multimedia and Ubiquitous Engineering (MUE). 2010:1-6.
［6］	张原. 基于高斯混合模型的无线传感器网络节点定位算法的研究［D］. 长春:吉林大学, 2010.
［7］	Mistry D, Joshi S, Agrawal N. A novel jitter separation method based on Gaussian mixture model［C］// 2015 IEEE International Conference on Pervasive Computing (ICPC).
20	15:1-4.
［8］	Devika A K, Sumithra M G, Deepika A K. A fuzzyGMM classifier for multilingual speaker identification［C］// 2014 IEEE International Conference on Communications and
	Signal Processing (ICCSP). 2014:1514-1518.
［9］	Li Yan, Li Lei. A greedy merge learning algorithm for Gaussian mixture model［C］// 2009 IEEE Workshop on Intelligent Information Technology Applications. 2009:506-509.
［10］	Rui F, Nandi A K. An enhanced splittingwhilemerging algorithm with finite mixture models［C］// 2013 IEEE International Conference on Acoustics, Speech and Signal
	Processing (ICASSP). 2013:3332-3336.
［11］	Zablotskiy S, Pitakrat T, Zablotskaya K, et al. Optimaloperators of hybrid genetic algorithm for GMM parameter estimation［C］// Proceedings of 2011 IEEE the 7th
	International Conference on Intelligent Environments. 2011:61-65.
［12］	Rajeswari K, Acharya Omkar, Sharma Mayur, et al. Improvement in kMeans clustering algorithm using data clustering［C］// 2015 IEEE International Conference on
	Computing Communication Control and Automation (ICCUBEA). 2015:367-369.
［13］	Gu Dongbing. Distributed EM algorithm for Gaussian mixtures in sensor networks［J］. IEEE Transactions on Neural Networks, 2008, 19(7):1154-1166.
［14］	刘伟峰,杨爱兰. 基于BIC准则和Gibbs采样的有限混合模型无监督学习算法［J］. 电子学报, 2011,39(z1):134-139.
［15］	Chen Pinyuen, Wu T J, Yang J. A comparative study of model selection criteria for the number of signals［J］. Iet Radar Sonar Navigation, 2008,2(3):180-188.
［16］	Wu Jingwen, Hamdan H. Model choice for binnedEM algorithms of fourteen parsimonious Gaussian mixture models by BIC and ICL criteria［C］// 2013 IEEE International
	Conference on System Science and Engineering (ICSSE). 2013:351-356.
［17］	聂云峰,舒坚,龚佳杰,等. 基于RSSI的无线传感器网络通信覆盖研究［J］. 传感技术学报, 2011,24(7):1066-1069.

[1]	FU Hong-lin, ZHANG Tai-hong, YANG Ya-ting, Aizimaiti Aiwanier, MA Bo. Scenes Text Modification Network for Uyghur Based on Generative Adversarial Network [J]. Computer and Modernization, 2024, 0(01): 41-46.
[2]	LI Xiang, ZHUANG Yi. Critical Fault Point Identification Method for Routing Algorithms Using XGBoost [J]. Computer and Modernization, 2023, 0(11): 75-81.
[3]	LI Shi-yue, MENG Jia-na, YU Yu-hai, LI Xue-ying, XU Ying-ao. Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement [J]. Computer and Modernization, 2023, 0(10): 1-8.
[4]	LIU Rui-xue, LI Wen, LIU Fang, DU Shou-guo. Prediction of Time Series with Missing Value Based on Tensor Autoregressive Completion [J]. Computer and Modernization, 2023, 0(09): 51-58.
[5]	TANG Yu-lin, HUANG Deng-shan, CHEN Shu-lu, CHEN Peng-ming. SFIM Image Fusion Method Combining IHS and Adaptive Filtering [J]. Computer and Modernization, 2023, 0(09): 70-76.
[6]	MAO Ming-yang, XU Sheng-chao. Continuous Attribute Discretization Algorithm of Rough Sets for BP Neural Networks Based on Particle Swarm Optimization#br# [J]. Computer and Modernization, 2023, 0(09): 115-119.
[7]	LIU Xian-zhuo, DENG Wei-si, XIE En-yan. Adaptive Protection System for Distribution Network Considering Grid Connection of Distributed Generation [J]. Computer and Modernization, 2023, 0(09): 120-126.
[8]	WANG Jia-chen, ZHANG Hong-xin, LIU Qing-hua, . CT Image Generation of Pneumonia Based on Generative Adversarial Network [J]. Computer and Modernization, 2023, 0(07): 20-24.
[9]	ZHANG Jun, SU Wen-hao. Optimization Method of Hadoop File Archiving Based on LZO [J]. Computer and Modernization, 2023, 0(06): 1-6.
[10]	SU Jin-ku, GUI Zhi-ming. Prediction of Short-term Taxi Flow Based on Spatio-temporal Characteristics [J]. Computer and Modernization, 2023, 0(05): 32-38.
[11]	PENG Sui, XU Liang, ZHANG Zhi-qiang, LOU Yuan-yuan, YU Hao, QIN Xiao-hui. Probabilistic Voltage Stability Evaluation Algorithm Based on Mixed Vine Copula and ILHS [J]. Computer and Modernization, 2022, 0(12): 6-12.
[12]	WANG Ying-ying, ZHUANG Yi, SUN Yi-fan. Reliability Evaluation Model of BP Neural Network Based on Particle Swarm Optimization [J]. Computer and Modernization, 2022, 0(12): 42-49.
[13]	WEN Zi-xin, LI Shao-ying, WANG Bin-cheng, LIU Bo, . Face Clustering Method Based on Nearest Neighborhood Aggregation [J]. Computer and Modernization, 2022, 0(12): 81-87.
[14]	GAO Feng, ZHUANG Yi, LIU Xiao. A Trusted Transmission Scheme of SDN Based on Path Tracking Feedback [J]. Computer and Modernization, 2022, 0(12): 102-110.
[15]	MIAO Zi-jing, MEI Xin. Chinese Event Detection Based on Multi-lexicon Feature Augmentation [J]. Computer and Modernization, 2022, 0(09): 13-18.

Clustering Algorithm Research on Mixed Received Signal Strength Sequences #br# of Multiple Nodes on Wireless Sensor Network

Knowledge

Abstract

Cite this article

share this article

References ［17］

Related Articles 15

Recommended Articles

Metrics

Comments