2024 : 5 : 20
Mostafa Bastam

Mostafa Bastam

Academic rank: Assistant Professor
Education: PhD.
Faculty: Faculty of Technology and Engineering
Phone: 35305114


Monitoring the level of environmental pollution in smart buildings using IoT
IoT, sensor, CO, IAQ, Ensemble machine learning, RF, MLP, SVR, LASSO, ELASTIC NET
Researchers Saja Kareem Abed(Student)، Montajab Ghanem(Advisor)، Mostafa Bastam(PrimaryAdvisor)، Ehsan Ataie(PrimaryAdvisor)


IoT and remote sensing (RS) are utilized in numerous scientific domains to monitor, gather, and evaluate remote data. New wireless technologies have emerged. "Smart homes" are expanding the Internet of Things applications. The Internet of Things connects devices and things to the Internet so they can intelligently identify themselves to other devices and includes sensor devices in the infrastructure layer. The Internet of Things (IoT) allows "things" to sense, process, and act based on software choices, which may reduce indoor air pollution. A connected indoor air monitoring system can gather quantitative data to identify patterns, identify contributing factors, and suggest new ways to improve indoor air quality. IoT features, applications, and smart homes will be covered. To detect anomalies, we analyze air monitoring methods and articles. There is a risk of death from carbon monoxide poisoning. Carbon monoxide sensors can go off warnings if the gas concentration rises in a room with a known or suspected emission source, like a stove. The prediction of the value of CO is significant, so we collected a real dataset by using ensemble machine learning with six models: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGboost, and Support Vector Regression.