An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation
An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation
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DOI：
10.1109/tpds.2007.1046
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发表时间：
2007-07-01
影响因子：
5.3
通讯作者：
Pei, Jian
中科院分区：
文献类型：
--
作者：
Liu, Chong;Wu, Kui;Pei, Jian
有限的能量供应是无线传感器网络发展的主要制约因素之一。一种可行的策略是积极降低传感器的空间采样率，即场地中测点的密度。通过适当的调度，我们希望保持数据收集的高保真度。在本文中，我们提出了一种基于对传感器报告的监测数据进行仔细分析的数据收集方法。通过探索传感数据的空间相关性，将传感器节点动态划分成簇，使同一簇中的传感器具有相似的监测时间序列。他们可以在未来分担数据收集的工作量，因为他们未来的读数可能是相似的。此外，在短时间内，传感器可能报告类似的读数。同一传感器上报的数据之间的这种相关性称为时间相关性，我们可以对这种相关性进行探索，从而进一步节约能源。我们开发了一个通用框架来解决几个重要的技术挑战，包括如何将传感器划分为集群，如何动态维护集群以响应环境变化，如何在集群中调度传感器，如何探索时间相关性，以及如何高保真地恢复汇中的数据。我们进行了广泛的实证研究，使用真实的测试平台系统和大规模的合成数据集来测试我们的方法。
Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, that is, the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short-time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic data set.