Mint views: Materialized in-network top-k views in sensor networks

Zeinalipour-Yazti, Demetrios, Andreou, Panayiotis orcid iconORCID: 0000-0002-6369-1094, Chrysanthis, Panos K and Samaras, George (2008) Mint views: Materialized in-network top-k views in sensor networks. In: IEEE/ACM 7th International Conference on Mobile Data Management (MDM’07), 1 May 2007, Mannheim, Germany. (Unpublished)

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In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.

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