Sensor data collection and visualization for self-care indoor environment monitoring systems
Date of Issue2016-05-23
School of Electrical and Electronic Engineering
It is known that human beings spend 90% of their lifetime indoor, and this ratio is still in a growth trend with the development of internet services and emergence of new technologies. In consequence, people start paying more attention to maintaining a healthy indoor environment condition. However, the existing indoor environment monitoring products in the market are commonly facing the problem of high cost, unable to check historian environment data and not user specific. Therefore, an innovative indoor environment monitoring and control system is designed and implemented in this Final Year Project to overcome those limitations. The system is developed through 4 stages: data collection, data analysis data visualization and indoor environment control. Multiple sensors are connected to the microcontroller Raspberry Pi though GPIO to set up the basic environment factors data collection system. User’s preference towards environment condition is learned by random forest algorithm through continuous data training and a user specific comfort model is then developed. With web application development, functions of real-time data visualization, historian data trend checking, and healthy environment maintenance recommendation are fulfilled. With the aid of the indoor environment monitoring system developed in this project, people can check potential unhealthy hazard for the environment they care about via any internet terminal and take timely action to maintain a comfortable space for themselves and their family.
Final Year Project (FYP)
Nanyang Technological University