Abstract-In this paper we introduce MINT (Materialized InNetwork 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 recursivelydefined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.
Aiming at continuous top-k monitoring, ZeinalipourYazti et al. proposed MINT REF , a framework that maintains the complete results of a query and calculate the upper bounds, so as to reduce the cost of future queries by pruning phase.
MINT Views: Materialized In-Network Top-k Views in Sensor Networks
2007 International Conference on Mobile Data Management
2007 International Conference on Mobile Data Management
Computer Science