Modeling of Intelligent Sensor Duty Cycling for Smart Home Automation

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users' living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors' energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users' future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users' unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor's role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme's work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors' energy consumption and prolongs the battery lifetime to approximately 137 days. Note to Practitioners - This article presented an energy-efficient duty-cycling scheme for automating sensors' operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users' next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users' activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user's activities with less energy, which ultimately prolongs the battery lifetime of the sensors.