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

The technology of smart home systems 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 problem 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) WSN testing 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.