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基于频繁项挖掘的空间关联性子簇形成算法
  • ISSN号：1007-5321
  • 期刊名称：《北京邮电大学学报》
  • 时间：0
  • 分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[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
  • 相关基金：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）
作者： 王茜[1], 高志鹏[1], 邱雪松[1], 王兴斌[2]
关键词： 无线传感器网络, 数据采集, 频繁项挖掘, 空间关联性, wireless sensor networks, data collection, frequent itemset mining, spatial correlation
中文摘要：

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．

英文摘要：

The cluster-based periodic data collection for wireless sensor networks, the sensor nodes de- ployed 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 fre- quent 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.

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期刊信息
  • 《北京邮电大学学报》
  • 北大核心期刊（2011版）
  • 主管单位:教育部
  • 主办单位:北京邮电大学
  • 主编：刘杰
  • 地址：北京海淀区西土城路10号195信箱
  • 邮编：100876
  • 邮箱：byxb@bupt.edu.cn
  • 电话：010-62281995 62282742
  • 国际标准刊号：ISSN：1007-5321
  • 国内统一刊号：ISSN：11-3570/TN
  • 邮发代号:2-648
  • 获奖情况:
  • 美国工程信息公司（Ei）数据库收录期刊,1999年全国优秀高等学校自然科学学报及教育部优秀...,中国期刊方阵“双效”期刊
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:7684