Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

作     者：Liang Xue Ying Liu Zhi-Qun Gu Zhi-Hua Li Xin-Ping Guan 

作者机构：School of Information and Electrical Engineering Hebei University of Engineering State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications System Control and Information Processing Key Laboratory of Ministry of Education Shanghai Jiao Tong University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2017年第14卷第6期

页      面：637-649页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080202[工学-机械电子工程] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 081001[工学-通信与信息系统] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China(Nos.61304131 and 61402147) Grant of China Scholarship Council(No.201608130174) Natural Science Foundation of Hebei Province(Nos.F2016402054 and F2014402075) the Scientific Research Plan Projects of Hebei Education Department(Nos.BJ2014019,ZD2015087 and QN2015046) the Research Program of Talent Cultivation Project in Hebei Province(No.A2016002023) 

主　　题：Heterogeneous wireless sensor networks clustering technique in cluster data routes integral framework network lifetimes 

摘      要：A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network *** classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.