Time series analysis and anomaly detection for trustworthy smart homes

The IoT network is expected to harbor several zettabytes of information in the future. Since trust and integrity are critical to IoT, it is essential to imbibe trust into the IoT environment for ensuring dependability and reliability. We propose a machine learning-based trustworthy system for the IoT-based smart home environment. Multiple appliances connected through the internet are susceptible to privacy issues, hence utmost care must be taken to ensure trust in the network. We consider the energy data and weather information with respect to smart homes, for comprehending the relationship between energy consumption by appliances and time period for detecting anomalous usage of appliances using the SDAR-based Change Finder algorithm. Time series analysis is performed using ARIMA, SARIMA, LSTM, Prophet, Light GBM, and VAR. The evaluation has been performed using RMSE, MSE, and MAE, and the study establishes that the ARIMA model outperforms the other models.