MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087311 A) filed by Dr. Shruthi P C; Dr. Nayana R Shenoy; Dr. Shubha P; and Kamala C on July 16, 2026, for A Machine Learning Based Iot Architecture For Adaptive Monitoring And Optimization Of Connected System.
Inventors include Dr. Shruthi P C; Dr. Nayana R Shenoy; Dr. Shubha P; and Kamala C.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: A machine learning based IoT Architecture for Adaptive Monitoring and Optimization of connected system ABSTRACT This invention shows a machine learning- based Internet of Things (IoT) architecture, which enables monitoring a connected system or system of systems in an adaptive manner and to optimize it. Classical approaches for monitoring a connected system or system of systems in a static manner and using a central unit for processing the received data in real time are known from the state of the art and are not sufficient in view of increasing complexity, rising amount of data, increasing dynamics and required performance. This invention connects up sensing units of IoT with a communication network, incorporates an edge intelligence, comprises a machine learning, an adaptive optimization and a feedback-based continuous learning, in order to improve monitoring and optimizing a connected system or system of systems in an adaptive manner. The architecture supports the real time collection of large amounts of heterogeneous data from thousands of different devices and sensors in real time. This allows for edge processing of the collected data for real time analysis, learning of typical patterns, real time abnormal behavior detection, and real time performance prediction. The architecture also supports machine learning models that, based on the collected data, generate real time optimized control commands for connected objects and learn in real time to increase the accuracy of their predictions. The proposed framework increases energy efficiency, reliability, scalability and performance of different types of systems in IoT ecosystems. It can be applied in industrial automation, smart manufacturing, various types of healthcare and medical monitoring, smart grid management, smart city and community infrastructure and other types of cyber-physical systems. It supports autonomous, intelligent, optimized and adaptive monitoring of next generation of connected systems and supports their efficient, real time, and predictive processing.
Disclaimer: Curated by HT Syndication.
