MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641097143 A) filed by D Prasanth Sai; Dr. Pragati Fatinge; G. Rajasri; Dr. Vijayalakshmi J; Dr. Ravindra Kale; Pranali Sardare; Mohini Thakre; Dr. Snehal Vairagade; Vijaya Kamble; Dr. Sandip Subrao Kanase; Dr. K. Mahendran; and Dr. K. Geetha on August 11, 2026, for Iot-Enabled Predictive Machine Learning Model For Intelligent Streetlight Automation In Smart Cities.
Inventors include D Prasanth Sai; Dr. Pragati Fatinge; G. Rajasri; Dr. Vijayalakshmi J; Dr. Ravindra Kale; Pranali Sardare; Mohini Thakre; Dr. Snehal Vairagade; Vijaya Kamble; Dr. Sandip Subrao Kanase; Dr. K. Mahendran; and Dr. K. Geetha.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: The current invention pertains to an IoT-Enabled Predictive Machine Learning Model designed for the automation of intelligent streetlights in smart cities, amalgamating Internet of Things (IoT), Machine Learning (ML), edge computing, cloud computing, and wireless communication technologies to deliver an efficient and intelligent street lighting solution. The innovation utilises IoT-integrated sensors to incessantly observe environmental illumination, vehicular congestion, foot traffic, meteorological factors, and electrical metrics. The gathered data is sent to edge and cloud platforms, where it undergoes preprocessing and analysis through machine learning algorithms like Random Forest, XGBoost, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks to forecast future lighting needs, energy usage, and equipment condition. According on these forecasts, the system autonomously regulates streetlight luminosity via adaptive dimming and smart ON/OFF toggling to enhance energy efficiency while ensuring roadway safety. The innovation additionally includes a predictive maintenance component that detects possible equipment malfunctions and issues repair notifications prior to failures, therefore minimising downtime and maintenance expenses. A cloud-based surveillance system facilitates centralised oversight, remote management, problem identification, energy analysis, and maintenance planning. The suggested innovation markedly enhances energy efficiency, operational dependability, equipment longevity, and public safety, while simultaneously decreasing electricity usage, operational costs, and carbon emissions. The adaptable framework is appropriate for implementation in smart cities, thoroughfares, residential neighbourhoods, industrial zones, academic institutions, airports, and various public infrastructures necessitating intelligent and sustainable streetlight oversight.
Disclaimer: Curated by HT Syndication.
