MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202621063708 A) filed by Abhale Babasaheb Annasaheb on May 20, 2026, for Smart Environmental Monitoring With Blockchain Audit And Ml Forecasting.
Inventors include Abhale Babasaheb Annasaheb; Swapnil Sitaram Hengade; Shivani Gajanand Malusare; Yogesh Yadavrao Pagar; and Neelam Annasaheb Thombare.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Environmental pollution, climate change, and inefficient resource management have become major global challenges in recent years. Traditional environmental monitoring systems often suffer from limitations such as centralized data storage, lack of transparency, delayed reporting, poor scalability, and vulnerability to data manipulation. To address these issues, the proposed project titled “Smart Environmental Monitoring with Blockchain Audit and ML Forecasting” presents an edad, svecanucre, and intelligent environmental monitoring framework that integrates Internet of Things (IoT), Blockchain technology, and Machine Learning (ML) techniques.The primary objective of this project is to continuously monitor important environmental parameters such as temperature, humidity, air quality, gas concentration, noise levels, and pollution indicators using smart sensors and IoT devices. The collected environmental data is transmitted in real time to a centralized or cloud-based platform where it can be analyzed, visualized, and stored securely. Since environmental data is highly sensitive and critical for decision-making processes, ensuring data integrity and transparency is essential. This improves trust, accountability, and reliability in environmental monitoring applications. In the proposed system, Machine Learning algorithms play a crucial role in forecasting future environmental conditions and pollution trends. Historical environmental data collected through sensors is used to train ML models capable of identifying patterns, predicting pollution levels, and generating early warnings for hazardous environmental conditions. Forecasting techniques such as Linear Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), or Long Short-Term Memory (LSTM) models can be utilized depending on the dataset and prediction requirements. The predictive capability of the system helps government agencies, industries, smart cities, and environmental organizations take proactive measures before environmental conditions become critical.
Disclaimer: Curated by HT Syndication.
