Occupancy Detection in Room Using Sensor Data

TL;DR

This paper explores using various machine learning algorithms on sensor data to accurately detect occupancy in indoor environments, contributing to smarter, energy-efficient building management.

Contribution

It evaluates and compares seven popular machine learning algorithms for occupancy detection using sensor data, providing insights into their effectiveness.

Findings

Decision Tree and Random Forest perform best in accuracy.

Machine learning models can reliably determine occupancy from sensor data.

Pattern recognition techniques enhance occupancy detection capabilities.

Abstract

With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments. Many researches have been implemented by scientists in different fields, to analyze such data for the purpose of security, energy efficiency, building reliability and smart environments. One study, that many researchers are interested in, is to utilize Machine Learning techniques for occupancy detection where the aforementioned sensors gather information about the environment. This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Additionally we show the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms is possible to determine the occupancy of indoor environments. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared in this study.