AN INTELLIGENT TRIGGER-ENABLED DEEP LEARNING MODEL FOR IOT-BASED ENVIRONMENTAL MONITORING AND REAL-TIME AIR QUALITY EVALUATION

Abstract

Shortcomings of conventional air quality monitoring systems, particularly their inability to self-audit and respond to harmful pollution levels in a proactive manner, have been revealed through an increasing demand for real-time environmental intelligence. Along with perform-ing high-precision AQI classification, this work presents a new hybrid deep learning architec-ture that includes a binary trigger module to support spontaneous environmental audits. The architecture learns to recognize sophisticated pollutant interaction patterns and predict action-able trigger events with bidirectional LSTM layers and multi-head attention. Real-time data acquisition, edge inference, and visual reporting are supported by the seamless integration of the presented architecture with a real Internet of Things infrastructure consisting of advanced sensors and microcontrollers. Severity-level pollutant mapping and threshold-configured trigger construction are supported using a customized preprocessing pipeline. As far as iden-tification of key pollution situations and low false activations is concerned, experimental re-sults show robust performance at all severity levels. Through this transition from fixed classi-fication to responsive intelligence, air quality intelligence improves, and the model is certi-fied as a saleableplatform for intelligent, autonomous environmental systems