Energy Heterogeneity-Based Multi-Chain Algorithm for WSN

doi:10.3969/j.issn.1000-565X.2015.09.012

Journal of South China University of Technology (Natural Science Edition) ›› 2015, Vol. 43 ›› Issue (9): 74-80.doi: 10.3969/j.issn.1000-565X.2015.09.012

• Computer Science & Technology • Previous Articles Next Articles

Energy Heterogeneity-Based Multi-Chain Algorithm for WSN

Wang Wei-xing^1,2 Huang Hong¹ Sun Dao-zong^1,2 Li Liang-bin¹ Hu Zi-ang¹

1． College of Electronic Engineering，South China Agricultural University，Guangzhou 510642，Guangdong，China;2． Key Laboratory of Key Technology on Agricultural Machinery and Equipment of the Ministry of Education，South China Agricultural University，Guangzhou 510642，Guangdong，China

Received:2015-01-23 Revised:2015-04-08 Online:2015-09-25 Published:2015-09-07
Contact: 王卫星(1963-)，男，教授，博士生导师，主要从事无线传感器网络、电子信息技术在农业中的应用研究． E-mail:weixing@scau.edu.cn
About author:王卫星(1963-)，男，教授，博士生导师，主要从事无线传感器网络、电子信息技术在农业中的应用研究．
Supported by:
Supported by the National Spark Project(2013GA780046)，the Science and Technology Planning Project of
Guangdong Province(2013B020314014)and the Natural Science Foundation of Guangdong Province(2014A030313451)

Abstract

Abstract: In order to balance the energy consumption of WSN effectively and improve the network lifetime，an
energy heterogeneity-based multi-chain algorithm (EHMCA) is proposed． In the algorithm，a two-stage heterogeneous network is adopted first，and the network area is divided into a number of equal subregions． Then，the optimal domain radius of clusters is determined according to the spacing between layers，the threshold value of the cluster head is also determined based on the energy of the advanced nodes and the average energy of the whole network，and the multi-hop routing inter-cluster parallel transmission mechanism is employed between layers． Therefore，in the whole network area，the cluster head transmits data to the base station by means of the multi-hop inter-layer multi-link parallel transmission mode and the single hop transmission mode of the optimal cluster． Simulation results show that the network performance of the EHMCA is better than those of the other three kinds of protocols，which
proves that the EHMCA is effective and practical．

Key words: wireless sensor networks, hierarchical network, energy heterogeneity, clustering, multi-chain

Wang Wei-xing Huang Hong Sun Dao-zong Li Liang-bin Hu Zi-ang . Energy Heterogeneity-Based Multi-Chain Algorithm for WSN[J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(9): 74-80.

[1]	LIU Yijun, WANG Jiada, ZHONG Shijie, et al.. Fast Multi-View Clustering Based on Uniform Label Matrix [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(9): 110-119.
[2]	WANG Xuewu, FANG Junyu, GAO Jin, et al. Multi-Objective Optimization Based on Improved Distribution of Solutions [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 137-148.
[3]	LIN Peiqun, GONG Minping, ZHOU Chuhao. User Portrait Method of Freeway Freight Car for Risk Identification of Freight Transportation [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 1-9.
[4]	CHEN Tingzhao, CHEN Yanyan, WANG Zili, et al. Methods of Determining the Range of Non-motorized Travel Influence Area Under the Concept of “Metro Transit Micro-center” [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 56-65.
[5]	LIU Xiaolan, SHI Zongyu, YE Zehui, et al. Anchor Graph Based Low-Rank Incomplete Multi-View Subspace Clustering [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 60-70.
[6]	CHEN Jiqing, SHU Xiaoxiong, LAN Fengchong, et al. Construction of Autonomous Vehicles Test Scenarios with Typical Dangerous Accident Characteristics [J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(5): 1-8.
[7]	LIANG Jingzhang, HUANG Xingshu, WU Lijuan, et al. Clustering Method of Power Load Profiles Based on KPCA and Improved K-means [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(6): 143-150.
[8]	LI Zhi, CHEN Yehang, FENG Bao, et al. Cerebral Infarction Image Segmentation Based on Active Contour Model [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(5): 102-111,124.
[9]	ZHENG Sifan, WANG Weixing, HE Zhanhua, et al. Research on Swing Amplitude Detection of Automobile Wiper with Two Granularity Optical Flow Manifold Learning#br# [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(1): 123-132.
[10]	LIU Xiaolan PAN Gan YI Miao LI Zhipeng. Distributed Low-rank Tensor Subspace Clustering Algorithm [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(8): 77-83,95.
[11]	DAI Xiang HUANG Xifeng TANG Rui JIANG Mengting CHEN Xingshu WANG Haizhou LUO Liang. Subtopic Detection Algorithm Based on Hierarchical Clustering [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(8): 84-95.
[12]	WU Wenjing JING Peng JIA Hongfei ZHANG Minghang. Low Carbon Travel Intention Data Mining for Residents Based on K-means Clustering and Random Forest Algorithm [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(7): 105-111.
[13]	CHEN Weiya PAN Xin FANG Xiaoping. Short-term Prediction of Passenger Flow on Bus Routes Based on K-means Clustering Combination Models [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(4): 83-89,113.
[14]	JI Xia ZHANG Tao ZHU Jianlei LIU Shicheng LI Xuejun. Improved DPC Clustering Algorithm with Neighbor Density Distribution Optimized Sample Assignment [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(2): 98-105.
[15]	LIU Xuemei HUANG Guanda HUANG Tianlai. Application of Sequence Alignment-Free Comparison-Based SeqDistK to Microbial Flora Clustering [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(11): 71-77.

Energy Heterogeneity-Based Multi-Chain Algorithm for WSN

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments