Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.