BinarySDG: binary sensor data generation with R

TL;DR

BinarySDG is a new tool designed to efficiently generate synthetic binary sensor data, addressing the scarcity of real Smart Home datasets and facilitating machine learning applications.

Contribution

The paper introduces BinarySDG, a novel and flexible method for creating synthetic binary sensor data to overcome data scarcity in Smart Home research.

Findings

BinarySDG can generate large amounts of synthetic data quickly.

The generated data is useful for training machine learning models.

BinarySDG simplifies data collection for Smart Home applications.

Abstract

The scarcity of Smart Home data is still a pretty big problem, and in a world where the size of a dataset can often make the difference between a poor performance and a good performance for problems related to machine learning projects, this needs to be resolved. But whereas the problem of retrieving real data can't really be resolved, as most of the time the process of installing sensors and retrieving data can be found to be really expensive and time-consuming, we need to find a faster and easier solution, which is where synthetic data comes in. Here we propose BinarySDG (Binary Synthetic Data Generator) as a flexible and easy way to generate synthetic data for binary sensors.