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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation
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An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation

机译：利用时空相关性的无线传感器网络节能数据收集框架

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摘要

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.
机译：有限的能量供应是无线传感器网络的主要限制之一。一种可行的策略是积极降低传感器的空间采样率，即降低现场中测量点的密度。通过适当的调度，我们希望保留数据收集的高保真度。在本文中，我们提出了一种数据收集方法，该方法基于对传感器报告的监视数据的仔细分析。通过探索感测数据的空间相关性，我们将传感器节点动态划分为群集，以使同一群集中的传感器具有相似的监视时间序列。他们将来可以分担数据收集的工作量，因为将来的读数可能类似。此外，在短时间内，传感器可能报告相似的读数。从同一传感器报告的数据中的这种相关性称为时间相关性，可以对其进行探索以进一步节省能量。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群集，如何根据环境变化动态维护群集，如何在群集中安排传感器，如何探索时间相关性以及如何以高保真度还原接收器中的数据。我们进行了广泛的实证研究，以使用真实的测试平台系统和大规模的综合数据集来测试我们的方法。

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