WaveletBased Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things The operating cost minimization of smart homes can be achieved with the optimization of the management of the buildingu0027s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noisecanceling application was higher than 98% in selected experiments.

Occupancy Estimation Using Wifi Motion Detection via Supervised Machine Learning Algorithms WiFi signals have tendency of getting disturbed by the motion of occupants and other movements in a zone. If we measure the level of this variation, it can represent the human activity in that zone. In this paper, we have proposed estimation of the occupancy by classifying the activity level obtained by disturbance in WiFi signal using several supervised machine learning approaches. We have prepared class labels using the schedule of people in the zone and verified it by counting the number of persons each hour. The proposed framework is tested and validated by collecting the data from an office space in a building and different performance measures are computed to see the effectiveness of this framework in occupancy estimation. In this task, Decision Tree and Random Forest are most stable with the highest accuracy of 95%.