Data Correlation-Based Clustering in Sensor Networks
Data Correlation-Based Clustering in Sensor Networks
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DOI：
10.1109/csa.2008.21
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Myungho Yeo;Mi Sook Lee;S. Lee;Jaesoo Yoo
中科院分区：
文献类型：
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作者：
Myungho Yeo;Mi Sook Lee;S. Lee;Jaesoo Yoo
许多类型的传感器数据在空间和时间上都表现出很强的相关性。时间和空间抑制都提供了减少传感器数据收集的能量成本的机会。然而，现有的聚类算法仅仅根据传感器节点的分布或网络拓扑结构来组织簇，而不考虑传感器数据之间的相关性，因此很难利用空间或时间上的机会。本文提出了一种基于传感器数据相关性的聚类算法。我们修改的广告阶段和TDMA调度方案，组织集群的相邻传感器节点具有相似的读数。此外，我们提出了一个时空抑制方案，我们的聚类算法。为了显示我们的聚类算法的优越性，我们比较它与现有的抑制算法的传感器网络的生命周期和数据的大小已收集在基站。实验结果表明，该算法可以减少40%的数据量，延长20%~ 30%的网络生存时间。
Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppression provides opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not correlation of sensor data. In this paper, we propose a novel clustering algorithm based on correlation of sensor data. We modify the advertisement phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data was reduced by 40%, and the whole network lifetime was prolonged by 20 ~ 30%.