Multiresolution Cube Estimators for Sensor Network Aggregate Queries

TL;DR

This paper introduces in-network multiresolution cube hierarchies for sensor networks to efficiently process spatial aggregate queries, enabling distributed construction, failure recovery, and optimized query planning.

Contribution

It presents a novel distributed method for constructing and maintaining multi-resolution cube hierarchies within sensor networks for efficient spatial query processing.

Findings

Query plans over cube summaries can be computed in polynomial time.

A PTIME algorithm minimizes data requests for spatial queries.

The approach supports recovery from node and area failures.

Abstract

In this work we present in-network techniques to improve the efficiency of spatial aggregate queries. Such queries are very common in a sensornet setting, demanding more targeted techniques for their handling. Our approach constructs and maintains multi-resolution cube hierarchies inside the network, which can be constructed in a distributed fashion. In case of failures, recovery can also be performed with in-network decisions. In this paper we demonstrate how in-network cube hierarchies can be used to summarize sensor data, and how they can be exploited to improve the efficiency of spatial aggregate queries. We show that query plans over our cube summaries can be computed in polynomial time, and we present a PTIME algorithm that selects the minimum number of data requests that can compute the answer to a spatial query. We further extend our algorithm to handle optimization over multiple queries, which can also be done in polynomial time. We discuss enriching cube hierarchies with extra summary information, and present an algorithm for distributed cube construction. Finally we investigate node and area failures, and algorithms to recover query results.