Abstract
A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.
Highlights
Wireless Sensor Network (WSN) is the key component in the Internet of Things (IoT) technology and plays a key role in people’s lives to enabling many smart services from smart living to energy saving
In an upgraded smart home, the entire smart devices are interconnected with WSN, which assesses the residents through comfort and independent automated smart services
Long Short-Term Memory (LSTM) addresses the problem to overcome the issue with Recurrent Neural network (RNN) by incorporating the gating technique and gives effective results than conventional RNN. Unlike from the former approaches, in this research work, we propose Energy and Event AwareSensor Duty Cycling (EEA-SDC) model to detect a particular event and schedule sensor duty cycling, which improves the battery lifetime of the embedded ambient sensors of the smart home appliances in a Home Area Network (HAN)
Summary
Wireless Sensor Network (WSN) is the key component in the Internet of Things (IoT) technology and plays a key role in people’s lives to enabling many smart services from smart living to energy saving. These smart services are hinged with the interconnectivity of large scale smart sensors, which continuously monitor human activities and environmental dynamics to collect data for various. In an upgraded smart home, the entire smart devices are interconnected with WSN, which assesses the residents through comfort and independent automated smart services. The smart home application services can monitor the daily routine of the residents to give a convenient environment, e.g., control and manage temperature, Sensors 2020, 20, 5498; doi:10.3390/s20195498 www.mdpi.com/journal/sensors
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