Analyses on Usage of MLP Regression with WSN Data for Predicting Room Occupancy

The recent events in the world, such as the Covid-19 pandemics raise the importance of tracking room occupancy. The importance of estimating the number of persons present is not related only to the epidemic scenarios and attempts to avoid people to people contact with the goal to stop the spreading of the diseases. This importance can be expanded to the scenarios when we want to avoid the presence of a higher number of people in certain areas to offer their safety and security for a variety of reasons, e.g. employee welfare, hazardous material presence, etc. Although there are numerous approaches to tackle this problem, this paper only deals with the usage of Wireless Sensor Networks (WSN), with standard or common nodes set to estimate the number of persons present in the targeted space.The significant factor that may facilitate the deployment of the system for occupancy monitoring is the possibility of the system being upgraded upon the existing Wireless Sensor Network (WSN). This paper presents the analysis of using Multi-layer Perceptron Regression (MLPR) on a dataset collected with WSN. The MLPR is implemented with the Python and scikit-learn open-source machine learning library and chosen on the basis of positive experience in numerous other scenarios and good predicting results. The methodology presented here predicts room occupancy based on the light, temperature, sound, CO
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, and PIR motion sensor data.