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• Rule based complex event processing for smart city air quality monitoring. • Siddhi as a CEP engine for event processing. • RDF based SPARQL query processing. • RDF based static and stream processing execution analysis. • Event filtering based air pollutant. 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 (NH 3 , O 3 , SO 2 , NO 2) 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. [ABSTRACT FROM AUTHOR]

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for various applications in industries such as medical, environmental monitoring, home automation, etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and the converted dataset into Resource Description Framework (RDF). This research describes the various steps in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation and supports reasoning and the general semantics of the open world, followed by all the ontologies. [ABSTRACT FROM AUTHOR]