MuSA: Multivariate Sampling Algorithm for Wireless Sensor Networks

TL;DR

This paper introduces MuSA, a multivariate sampling algorithm for wireless sensor networks that reduces data volume, energy consumption, and delay while maintaining data representativeness through component analysis techniques.

Contribution

MuSA is the first algorithm to apply component analysis for multivariate data sampling in wireless sensor networks, improving efficiency and data quality.

Findings

Data volume is significantly reduced.

Energy consumption decreases during data transmission.

Network delay is minimized with MuSA.

Abstract

A wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced.