Abstract
In this work, an uncertainty prediction method for the home environment is proposed using the IoT devices (sensors) for predicting uncertainties using place-based approach. A neural network (NN) based smart communication system was implemented to test the results obtained from place-based approach using the inputs from sensors linked with internet of things (IoT). In general, there are so many smart systems for home automation is available for alerting the owners using IoT, but they can communicate only after an accident happens. But it is always better to predict a hazard before it happens is very important for a safe home environment due to the presence of kids and pet animals at home in the absence of parents and guardians. Therefore, in this work, the uncertainty prediction component (UPC) using place-based approach helps to make suitable prediction decisions and plays a vital role to predict uncertain events at the smart home environment. A comparison of different classifiers like multi-layer perceptrons (MLP), Bayesian Networks (BN), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) is made to understand the accuracy of the obtained results using the proposed approach. The results obtained in this method shows that place-based approach is providing far better results as compared to the global approach with respect to training and testing time as well. Almost a difference of 10 times is seen with respect to the computing times, which is a good improvement to predict uncertainties at a faster rate.