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Article

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

1
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
2
Department of Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5498; https://doi.org/10.3390/s20195498
Received: 19 August 2020 / Revised: 22 September 2020 / Accepted: 22 September 2020 / Published: 25 September 2020
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. View Full-Text
Keywords: smart homes; event detection; activity detection; deep learning; long-short term memory; sensor duty cycling smart homes; event detection; activity detection; deep learning; long-short term memory; sensor duty cycling
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MDPI and ACS Style

Diyan, M.; Khan, M.; Nathali Silva, B.; Han, K. Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning. Sensors 2020, 20, 5498. https://doi.org/10.3390/s20195498

AMA Style

Diyan M, Khan M, Nathali Silva B, Han K. Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning. Sensors. 2020; 20(19):5498. https://doi.org/10.3390/s20195498

Chicago/Turabian Style

Diyan, Muhammad, Murad Khan, Bhagya Nathali Silva, and Kijun Han. 2020. "Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning" Sensors 20, no. 19: 5498. https://doi.org/10.3390/s20195498

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