Intellectual Property India Publishes Patent Application for An Aiot-Enabled Edge Intelligence System For Real-Time Multi-Modal Environmental Pollution Detection, Source Identification And Predictive Risk Assessment

MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124633 A) filed by S Singaravelan; R. Swathi; G. S. Arun Kumar; N. Rigana Fathima; Dr. V. Vivek; P. Subramanian; Dr. S. V. Padmavathi Devi; S. Balaganesh; Dr. N. Prabhu; Dr. J. Kumaresan; S. S. Deepthi; and Dr. B. Aravind on October 17, 2026, for An Aiot-Enabled Edge Intelligence System For Real-Time Multi-Modal Environmental Pollution Detection, Source Identification And Predictive Risk Assessment.

Inventors include S Singaravelan; R. Swathi; G. S. Arun Kumar; N. Rigana Fathima; Dr. V. Vivek; P. Subramanian; Dr. S. V. Padmavathi Devi; S. Balaganesh; Dr. N. Prabhu; Dr. J. Kumaresan; S. S. Deepthi; and Dr. B. Aravind.

The application for the patent was published on October 30, 2026, under issue no. 44/2026.

Abstract: The present invention discloses an Artificial Intelligence of Things (AIoT)-enabled edge intelligence system and method for real-time multi-modal environmental pollution detection, localized source identification, and predictive risk assessment. The system comprises a distributed mesh network of heterogeneous sensor nodes, an edge compute gateway comprising a multi-modal feature fusion engine, a lightweight hybrid neural network inference module, and a spatial-temporal dispersion tracking engine communicatively coupled to a secure cloud-based analytics server. The multi-modal sensing array acquires ambient air particulate metrics, volatile organic compound concentrations, acoustic signatures, thermal profiles, and micro-meteorological parameters. Sensor raw data is preprocessed at the edge via an adaptive Kalman filter and quantized into lightweight multi-modal tensors. The edge intelligence module executes a quantized Spatio-Temporal Convolutional Graph Neural Network (ST-GNN) coupled with an inverted Gaussian plume dispersion estimation model to determine real-time emission flux, triangulate non-line-of-sight pollution origins, and project multi-horizon air quality hazard indices under localized meteorological constraints. The system operates autonomously with low latency, robust fault tolerance, and minimal bandwidth consumption, enabling rapid regulatory dispatch, automated mitigation triggers, and dynamic environmental risk containment across urban, industrial, and sensitive ecological corridors.

Disclaimer: Curated by HT Syndication.