On dynamic data-driven selection of sensor streams
On dynamic data-driven selection of sensor streams
复制标题
关于传感器流的动态数据驱动选择
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Philip S. Yu
中科院分区：
文献类型：
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作者：
C. Aggarwal;Yan Xie;Philip S. Yu
传感器节点具有有限的本地存储、计算能力和电池寿命，因此期望在数据收集期间最小化来自这些节点的存储、处理和通信。收集到的大量数据进一步扩大了这一问题。在真实的应用中，传感器流通常彼此高度相关或者可能具有其他种类的函数依赖性。例如，在给定地理接近度中的一组声音传感器可以拾取几乎相同的信号集合。显然，由于不同传感器之间存在相当大的功能依赖性，因此传感器收集的数据存在巨大的冗余。这些冗余也可能随着数据随时间的推移而变化。在本文中，我们讨论了真实的时间算法，以减少在传感器网络中收集的数据量。广义的思想是在真实的时间内有效地确定传感器流之间的函数依赖关系，并且仅从最小的传感器集合主动地收集数据。其余的传感器以低采样率被动地收集数据，以检测基础数据中的任何变化趋势。我们提出了真实的时间算法，以尽量减少在减少收集的数据的功耗，并显示所得到的数据保留几乎相同的信息量在一个低得多的成本。
Sensor nodes have limited local storage, computational power, and battery life, as a result of which it is desirable to minimize the storage, processing and communication from these nodes during data collection. The problem is further magnified by the large volumes of data collected. In real applications, sensor streams are often highly correlated with one another or may have other kinds of functional dependencies. For example, a group of sound sensors in a given geographical proximity may pick almost the same set of signals. Clearly, since there are considerable functional dependencies between different sensors, there are huge redundancies in the data collected by sensors. These redundancies may also change as the data evolve over time. In this paper, we discuss real time algorithms for reducing the volume of the data collected in sensor networks. The broad idea is to determine the functional dependencies between sensor streams efficiently in real time, and actively collect the data only from a minimal set of sensors. The remaining sensors collect the data passively at low sampling rates in order to detect any changing trends in the underlying data. We present real time algorithms in order to minimize the power consumption in reducing the data collected and show that the resulting data retains almost the same amount of information at a much lower cost.