Probabilistic Clustering for Data Aggregation in Air Pollution Monitoring System.

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Air pollution monitoring systems generate vast amounts of complex data from distributed sensors.
• Efficient data aggregation is crucial for reducing communication overhead and ensuring data quality for decision-making.

Purpose of the Study:

• To propose an unsupervised learning approach for soft clustering of sensors in air pollution monitoring systems.
• To enable a dynamic data transmission policy based on sensor cluster membership, optimizing network resource utilization.

Main Methods:

• Utilized the Expectation-Maximization (EM) algorithm, an unsupervised machine learning and probabilistic technique.
• Clustered sensors into distinct sets representing normal and polluted zones based on environmental data.
• Leveraged cluster membership probability for a tunable trade-off between data redundancy and monitoring accuracy.

Main Results:

• The AI-based clustering demonstrated high efficiency in simulations.
• The EM algorithm achieved a relative error below 5% under common pollution scenarios with adequate sample sizes.
• The proposed method effectively differentiated between normal and polluted zones for adaptive data transmission.

Conclusions:

• The developed unsupervised learning approach provides an effective method for intelligent and adaptive data aggregation in air pollution monitoring.
• This technique lays the groundwork for advanced, energy-efficient, and accurate environmental monitoring systems.
• The EM algorithm offers a robust solution for optimizing sensor network performance and data management.