Designing A WSNs-based Smart Home Monitoring System through Deep Reinforcement Learning

Designing A WSNs-based Smart Home Monitoring System through Deep Reinforcement Learning

smart home monitoring system; wireless sensor networks; deep reinforcement learning; deep learning; internet of things (IoT)

Smart home system technology has developed rapidly and provides convenience for human life. Several smart
home technologies, especially monitoring systems, have been developed by integrating several aspects, including
security systems, fuzzy methods, and energy-saving methods. However, the issue is how to build a smart home
system that is accurate, convenient, and low-cost. In this research, the development of a smart home monitoring
system that integrates wireless sensor networks (WSNs) and deep reinforcement learning (DRL) is carried out
based on three parameters, i.e., temperature, humidity, and CO2 level. The experimental method is carried out
by (1) validating the accuracy quality of WSNs; (2) determining the best model implemented in the system; and
(3) measuring the quality of the DRL system on the smart home monitoring system. Based on the test results,
several indicators were obtained: (1) Testing the WSNs resulted in an accuracy of 98.52%; (2) the accuracy of
the modeling results implemented in the system is 97.70%; and (3) DRL system test on the smart home
monitoring system through 21 test scenarios resulted in an accuracy of 95.52%. The indicators of testing this
smart monitoring system prove that the developed system provides the advantages of accuracy, ease of use, and
low cost

Ahmad Taqwa, Indra Griha Tofik Isa, Indri Ariyanti

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

October 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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