Approximate Data Collection in Sensor Networks using Probabilistic Models
Approximate Data Collection in Sensor Networks using Probabilistic Models
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DOI：
10.1109/icde.2006.21
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发表时间：
2006-04
期刊：
影响因子：
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通讯作者：
D. Chu;A. Deshpande;J. Hellerstein;W. Hong
中科院分区：
文献类型：
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作者：
D. Chu;A. Deshpande;J. Hellerstein;W. Hong
无线传感器网络被证明是有用的，在各种设置。这些网络的核心挑战是最大限度地降低能耗。先前的数据库研究已经提出通过将数据减少运算符（如聚合和选择）推入网络来实现这一点。这种方法已经被证明不受传感器网络技术的早期采用者的欢迎，他们通常想要提取传感器读数的完整“转储”，即，运行“SELECT *”查询。不幸的是，因为这些查询没有数据减少，他们消耗大量的能量在当前的传感器网络查询处理器。在本文中，我们攻击的“选择”问题的传感器网络。我们提出了一个强大的近似技术称为肯，使用复制的动态概率模型，以尽量减少从传感器节点到网络的PC基站的通信。除了数据收集，我们表明，肯是非常适合异常和事件检测应用程序。这项工作的一个关键挑战是智能地利用传感器节点之间的空间相关性，而不施加不适当的传感器到传感器的通信负担，以保持模型。使用两个真实世界的传感器网络部署的痕迹，我们证明了相对简单的模型可以提供显着的通信（因此能源）节省，而不会过度牺牲结果的质量或频率。即使是在我们的简单模型中选择最佳也是NPhard，但我们的实验表明，贪婪启发式算法的性能几乎与穷举算法一样好。
Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete "dumps" of the sensor readings, i.e., to run "SELECT *" queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors. In this paper we attack the "SELECT " problem for sensor networks. We propose a robust approximate technique called Ken that uses replicated dynamic probabilistic models to minimize communication from sensor nodes to the network’s PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications. A key challenge in this work is to intelligently exploit spatial correlations across sensor nodes without imposing undue sensor-to-sensor communication burdens to maintain the models. Using traces from two real-world sensor network deployments, we demonstrate that relatively simple models can provide significant communication (and hence energy) savings without undue sacrifice in result quality or frequency. Choosing optimally among even our simple models is NPhard, but our experiments show that a greedy heuristic performs nearly as well as an exhaustive algorithm.