Medians and Beyond：传感器网络的新聚合技术
Medians and Beyond: New Aggregation Techniques for Sensor Networks
摘要：
无线传感器网络提供了廉价的跨越和监测大地理区域的潜力。然而，传感器具有显著的功率限制（电池寿命），使得通信非常昂贵。在基于传感器的信息系统的背景下的另一个重要问题是，单独的传感器读数本质上是不可靠的。为了解决这两个方面，TinyDB和Cougar等传感器数据库系统支持网络内数据聚合，以降低通信成本并提高可靠性。然而，现有的数据聚合技术仅限于相对简单的查询类型，例如SUM、MAX、AVG和MIN/MAX。在本文中，我们提出了一个数据聚合方案，显着扩展类的查询，可以回答使用传感器网络。这些查询包括（近似）分位数，如中位数，最常见的数据值，如共识值，数据分布的直方图，以及范围查询。在我们的计划中，每个传感器聚合它从其他传感器收到的数据到一个固定的（用户指定的）大小的消息。我们提供严格的理论保证的近似质量的查询的消息大小。我们评估我们的聚合方案的性能，通过模拟，并证明其准确性，可扩展性和低资源利用率的高度可变的输入数据集。
英文摘要：
Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
