Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks
Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks
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预测与否？
DOI：
10.1109/tpds.2010.174
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发表时间：
2011-06-01
影响因子：
5.3
通讯作者：
Wang, Chonggang
中科院分区：
文献类型：
--
作者：
Jiang, Hongbo;Jin, Shudong;Wang, Chonggang
对于无线传感器网络（WSNs）中的许多应用，用户可能希望从网络中连续地提取数据以供以后分析。然而，准确的数据提取是困难的，它往往是太昂贵，以获得所有的传感器读数，以及没有必要的意义上说，读数本身只代表样本的真实状态的世界。利用传感器数据之间的空间和时间相关性的聚类和预测技术为减少连续传感器数据收集的能耗提供了机会。将聚类和预测技术相结合，设计一种新的数据收集方案，以实现网络的能量有效性和稳定性。我们提出了一个能量有效的框架，在无线传感器网络中基于聚类的数据收集，通过集成自适应启用/禁用预测方案。我们的框架是基于集群的。簇头代表簇中的所有传感器节点，并从它们收集数据值。为了有效地实现预测技术在无线传感器网络中，我们提出了自适应方案来控制预测在我们的框架中使用，分析降低通信成本和限制预测成本之间的性能权衡，并设计算法，利用自适应方案的好处，使/禁用预测操作。我们的框架是一般的，足以纳入许多先进的功能，我们展示了如何睡眠/唤醒调度可以应用，这需要我们的框架方法来设计一个实用的算法，数据聚合：它避免了需要猖獗的节点到节点的聚合传播，而是它使用更快，更有效的集群到集群的传播。据我们所知，这是第一个工作，自适应启用/禁用预测方案，基于聚类的连续数据收集在传感器网络。我们提出的模型，分析和框架通过仿真和竞争技术的比较进行验证。
For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult-it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques.