Distributed Principal Component Analysis for Wireless Sensor Networks

TL;DR

This paper presents a distributed algorithm for Principal Component Analysis in sensor networks, reducing communication and energy use while maintaining accuracy, validated through real data experiments.

Contribution

It introduces a distributed power iteration method for PCA in sensor networks, with detailed analysis and validation on real data.

Findings

The distributed PCA algorithm reduces communication costs.

The method maintains high accuracy with fewer resources.

Experimental results validate the approach's effectiveness.

Abstract

The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear transform where the sensor measurements are projected on a set of principal components. When sensor measurements are correlated, a small set of principal components can explain most of the measurements variability. This allows to significantly decrease the amount of radio communication and of energy consumption. In this paper, we show that the power iteration method can be distributed in a sensor network in order to compute an approximation of the principal components. The proposed implementation relies on an aggregation service, which has recently been shown to provide a suitable framework for distributing the computation of a linear transform within a sensor network. We also extend this previous work by providing a detailed analysis of the computational, memory, and communication costs involved. A compression experiment involving real data validates the algorithm and illustrates the tradeoffs between accuracy and communication costs.