TensorFlow-based smart home using semi-supervised deep learning with context-awareness

Abstract

In recent years, Smart homes play a significant role in improving the quality of human life due to the rapid proliferation of the Internet of Things (IoT) technology. The previous research works on the smart home system have adopted the machine learning and deep learning algorithms to predict the sequential activities in the smart home. This work presents a model of SMART home automation with Context-Awareness using Stacked AutoEncoder (SAE) -Long Short-Term Memory (LSTM) in TensorFlow (SMART-CAST). The SMART-CAST approach comprises three main processes, including the integration of internal and external home data, SAE-assisted unsupervised learning, and LSTM with back propagation-assisted supervised learning. By inter-linking the spatial and temporal attribute-values, the SMART-CAST unifies the smart home and weather data for facilitating decision-making. It employs the SAE to generate the compressed representation of the unified smart home data from the unlabeled information. In consequence, the SMART-CAST approach applies the LSTM with the extracted compressed representation for learning the labeled data and updates the weight of the LSTM through backpropagation to predict the sequential activities in the smart home system. To further improve the decision-making performance, the experimental model executes the proposed semi-supervised deep learning algorithm in the TensorFlow deep learning framework.