Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Abstract

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

“…Monitoring is the second most studied issue, namely in of the included papers. Not surprisingly, in the context of monitoring, various applications have been identified, e.g., health [ 60 , 89 , 115 , 158 , 159 ], smart buildings [ 90 , 116 , 160 ], agriculture [ 161 , 162 ], stress [ 61 , 117 , 136 , 163 ], transportation [ 91 ], military defense [ 164 ], etc. Other challenges comparatively highly studied in the included papers are QoS [ 92 , 93 , 118 , 128 , 137 , 138 , 139 , 152 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 ] with , and energy saving [ 62 , 63 , 94 , 95 , 96 , 114 , 119 , 142 , 143 , 144 , 175 ] with …”

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

“…The perceptron, first introduced by Rosenblatt [46], is the fundamental unit of artificial neural networks, just as the neuron is the fundamental unit of our central nervous system. Hence, it conforms the basis of advanced ML methods that are used on several real-life applications on Industry 4.0, such as smart manufacturing [47], condition monitoring [48], material selection [49], building occupancy prediction [50], among others. In the basic applications of Machine Learning, the Simple Perceptron is used as a supervised binary classifier [51], and it has four parts:…”

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“… Algorithm Results [ 25 ] 2 Y Detection NO YES 1 min LTP, NB, CART Accuracy 90.9–93.5% [ 86 ] 10 D Num. People NO YES 15 min Yolo v4, BTM Accuracy 99.5% [ 87 ] 15 D Detection NO YES 1 min LSTM Accuracy 96.8% [ 84 ] 21 D Detection NO YES 5 seg SGF, SURE, PI-PRM Accuracy 97% [ 88 ] 60 D Detection NO YES 5 min SDLM, LSTM, RF, SVM Accuracy 63–70% [ 18 ] 20 D Num. People...…”

“…Meter Other m Num. Occ [ 25 ] 2 2 2 1 1 1 1 Office 19 5 1 [ 86 ] 1 1 1 1 1 1 1 Kitchen Apartment 20 1 [ 87 ] 1 1 1 1 …”