Occupancy Detection Using Optimization Based SVNN Classifier

Abstract

In the Internet of Things (IoT), numerous scientific and industrial researchers are enticed by the smart buildings. Occupancy detection stands as a striking research domain on account of its potentially significant benefits. Though the researchers developed various approaches, the reliable ones in detecting occupancy are still in demand and those methods showed slow learning convergence and worse performance. Hence, this work proposed an efficient occupancy detection system utilizing an optimized SVNN classifier. For this, the phases, namely, i) sensor data acquisition, ii) feature extraction (FE), iii) feature reduction (FR), and iii) classification, are performed. The input data are gathered from different sensors deployed in a tutorial room. The data are extracted from temperature, humidity, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , and light sensors. The features are highly reduced by utilizing PCA. At last, the reduced features are sent to the Support Vector Neural Network (SVNN) classifier as input for predicting whether occupants in the room are present or not. The extensive outcomes are analyzed by the performance shown by the proposed classification methodology and other existing approaches regarding accuracy, and False positive rate(FPR).