In this paper we present 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' (⊆ 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.
Published July 5, 2007
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