基于频繁项挖掘的空间关联性子簇形成算法

doi:10.13190/j.jbupt.2015.s1.005

北京邮电大学学报 ›› 2015, Vol. 38 ›› Issue (s1): 20-23.doi: 10.13190/j.jbupt.2015.s1.005

基于频繁项挖掘的空间关联性子簇形成算法

王茜¹, 高志鹏¹, 邱雪松¹, 王兴斌²

1. 北京邮电大学网络与交换技术国家重点实验室, 北京 100086;
2. 总参信息化部驻成都地区军事代表室, 成都 610041

收稿日期:2014-07-20 出版日期:2015-06-28 发布日期:2015-06-28
作者简介:王茜(1991—), 女, 博士生, E-mail: wangqian1991@bupt.edu.cn; 高志鹏(1980—), 男, 副教授, 博士生导师.
基金资助:
国家自然科学基金项目(61272515,61372108,61121061);北京高等学校青年英才计划项目(YETP0474);教育部博士点基金项目(20110005110011)

Frequent Itemset Mining-Based Spatial Subclustering Algorithm

WANG Qian¹, GAO Zhi-peng¹, QIU Xue-song¹, WANG Xing-bin²

1. State Key Laboratory of Network and Switching Technology, Beijing Universityof Posts and Telecommunications, Beijing 100086, China;
2. Military Representative Office in Chengdu of Information Department of PLA General Staff, Sichuan Chengdu 610041, China

Received:2014-07-20 Online:2015-06-28 Published:2015-06-28

摘要/Abstract

摘要：

部署在无线传感器网络监测区域的传感器节点周期性地进行感知数据的采集和传输,传感器节点采集数据之间存在的空间关联性会增加采集数据的冗余度和网络能耗. 为了延长无线传感器网络的生命周期,提出了一种基于频繁项挖掘的空间关联性子簇形成算法. 仿真实验结果表明,该算法与已有算法相比,降低了网络能耗,延长了网络的生命周期,保证了采集数据的质量.

关键词: 无线传感器网络, 数据采集, 频繁项挖掘, 空间关联性

Abstract:

The cluster-based periodic data collection for wireless sensor networks, the sensor nodes deployed in monitoring region periodic transmit data to sink nodes were considered. The spatial correlation in collected data increases the redundancy of data and network energy consumption. An algorithm was proposed to prolong the lifetime of wireless sensor network and ensure the fidelity of collected data. A frequent itemset mining-based spatial subclustering algorithm was proposed. Analysis and experiment show that the improved algorithm can achieve more energy savings, extend the wireless sensor networks lifetime and guarantee the fidelity of collected data.

Key words: wireless sensor networks, data collection, frequent itemset mining, spatial correlation

中图分类号:

TP393

王茜, 高志鹏, 邱雪松, 王兴斌. 基于频繁项挖掘的空间关联性子簇形成算法[J]. 北京邮电大学学报, 2015, 38(s1): 20-23.

WANG Qian, GAO Zhi-peng, QIU Xue-song, WANG Xing-bin. Frequent Itemset Mining-Based Spatial Subclustering Algorithm[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(s1): 20-23.

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基于频繁项挖掘的空间关联性子簇形成算法

Frequent Itemset Mining-Based Spatial Subclustering Algorithm

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