Hierarchical In-Network Data Aggregation with Quality Guarantees
Hierarchical In-Network Data Aggregation with Quality Guarantees
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DOI：
10.1007/978-3-540-24741-8_38
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发表时间：
2004-03
期刊：
影响因子：
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通讯作者：
Antonios Deligiannakis;Y. Kotidis;N. Roussopoulos
中科院分区：
文献类型：
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作者：
Antonios Deligiannakis;Y. Kotidis;N. Roussopoulos
早期的工作已经证明了网络内数据聚合的有效性，以尽量减少在大型传感器网络中的连续查询过程中交换的消息量。关键思想是构建一个聚合树，其中父节点聚合从其子节点接收的值。然而，对于具有严重能量约束的大型传感器网络，通过聚合树获得的减少可能是不够的。在本文中，我们扩展了以前的工作在网络数据聚合，以支持近似评估的查询，以进一步减少节点之间交换的消息的数量，延长网络的寿命。我们的框架的一个关键因素是剩余操作模式的概念，用于消除兄弟节点的消息时，他们的累积变化很小。我们引入了一种新的算法，基于潜在的收益，自适应地重新分配的错误阈值，受益最大的节点，并试图尽量减少在网络中传输的消息的总数。我们的实验表明，我们的技术显着优于以前的方法，并通过利用叠加树层次结构减少网络流量。
Earlier work has demonstrated the effectiveness of in-network data aggregation in order to minimize the amount of messages exchanged during continuous queries in large sensor networks. The key idea is to build an aggregation tree, in which parent nodes aggregate the values received from their children. Nevertheless, for large sensor networks with severe energy constraints the reduction obtained through the aggregation tree might not be sufficient. In this paper we extend prior work on in-network data aggregation to support approximate evaluation of queries to further reduce the number of exchanged messages among the nodes and extend the longevity of the network. A key ingredient to our framework is the notion of the residual mode of operation that is used to eliminate messages from sibling nodes when their cumulative change is small. We introduce a new algorithm, based on potential gains, which adaptively redistributes the error thresholds to those nodes that benefit the most and tries to minimize the total number of transmitted messages in the network. Our experiments demonstrate that our techniques significantly outperform previous approaches and reduce the network traffic by exploiting the super-imposed tree hierarchy.