On Computing Compression Trees for Data Collection in Sensor Networks

TL;DR

This paper introduces algorithms for constructing near-optimal compression trees in sensor networks, significantly reducing data collection costs while providing provable guarantees and addressing different communication models.

Contribution

It proposes a novel approach to data collection using compression trees, with approximation algorithms and theoretical guarantees for energy-efficient sensor network data gathering.

Findings

Algorithms achieve near-optimal data collection costs.

Significant reduction in energy consumption demonstrated.

Applicable to various communication models, including broadcast.

Abstract

We address the problem of efficiently gathering correlated data from a wired or a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding an optimal or a near-optimal {\em compression tree} for a given sensor network: a compression tree is a directed tree over the sensor network nodes such that the value of a node is compressed using the value of its parent. We consider this problem under different communication models, including the {\em broadcast communication} model that enables many new opportunities for energy-efficient data collection. We draw connections between the data collection problem and a previously studied graph concept, called {\em weakly connected dominating sets}, and we use this to develop novel approximation algorithms for the problem. We present comparative results on several synthetic and real-world datasets showing that our algorithms construct near-optimal compression trees that yield a significant reduction in the data collection cost.