Główne pojęcia
Efficiently monitoring air quality in smart cities using rule-based complex event processing is crucial for stakeholders to make timely decisions and improve living standards.
Streszczenie
The content discusses the integration of rule-based complex event processing (CEP) with SPARQL queries for real-time air quality monitoring in smart cities. It emphasizes the importance of monitoring air quality due to its impact on health and the environment. The research proposes an integrated framework that collects data from CPCB, preprocesses it, converts it into RDF data, and uses Apache Jena for query processing. Rules are generated based on decision trees and standard parameters to categorize air quality conditions. The Siddhi CEP engine is employed for event processing, correlating events, and executing queries efficiently. The study evaluates performance through various experiments, including query execution time analysis based on different RDF chunks and event processing time variations.
Structure:
- Introduction to Smart Cities and Air Quality Monitoring Importance
- Proposed Integrated Framework Overview
- Dataset Collection and Preprocessing
- Rule Generation Using Decision Trees
- RDF Conversion and Knowledge Graph Development
- Siddhi CEP Engine for Event Processing
- Query Execution Analysis Based on Different RDF Chunks
- Performance Evaluation Results Discussion
Statystyki
According to WHO, around 6.7 million people die prematurely annually due to air pollution.
Lancet report states that air pollution caused 1.67 million fatalities in 2019.
Particulate matter (PM) is measured in terms of size (2.5µm) or PM2.5.
Data collected from CPCB includes pollutants like SO2, NOX, CO, NH3, etc., monitored twice a week.
Rules developed based on CPCB guidelines classify AQI categories as Good, Moderate, Moderately Polluted.
Cytaty
"Smart city operations include ICT, IoT, ML, Big Data & CEP for effective decision-making."
"CEP-based smart air quality monitoring utilizes rule-based filtering of event streams."
"Rules extracted using decision trees help categorize air quality conditions effectively."