Deterministic Data Reduction in Sensor Networks

Hüseyin Akcan, Hervé Brönnimann
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引用次数: 12

Abstract

The processing capabilities of wireless sensor nodes enable to aggregate redundant data to limit total data flow over the network. The main property of a good aggregation algorithm is to extract the most representative data by using minimum resources. From this point of view, sampling is a promising aggregation method, that acts as surrogate for the whole data, and once extracted can be used to answer multiple kinds of queries (such as AVG, MEDIAN, SUM, COUNT, etc.), at no extra cost. Additionally, sampling also preserves the correlation info within multi-dimensional data, which is quite valuable for further data mining. In this paper, we propose a novel, distributed, weighted sampling algorithm to sample sensor network data and compare to an existing random sampling algorithm, which to the best of our knowledge is the only algorithm to work in this kind of setting
传感器网络中的确定性数据约简
无线传感器节点的处理能力能够聚合冗余数据,以限制网络上的总数据流。一个好的聚合算法的主要特性是用最少的资源提取最具代表性的数据。从这个角度来看,抽样是一种很有前途的聚合方法,它充当整个数据的代理,一旦提取出来,就可以用来回答多种查询(如AVG、MEDIAN、SUM、COUNT等),而不需要额外的成本。此外,采样还保留了多维数据中的相关信息,这对进一步的数据挖掘非常有价值。在本文中,我们提出了一种新颖的、分布式的、加权的采样算法来对传感器网络数据进行采样,并与现有的随机采样算法进行了比较,据我们所知,随机采样算法是唯一适用于这种情况的算法
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