MUMBAI, India, March 13 -- Intellectual Property India has published a patent application (202641024909 A) filed by Madhankumar C; Vijayalakshmi M; C. Sathishkumar; Payal Shah; G. Maryam Banu; Ms Akileshwari; and P. Kiruthika, Pollachi, Tamil Nadu, on March 3, for 'a self-learning autonomous sensor network for real-time intelligent decision making in smart environments.'
Inventor(s) include Vijayalakshmi M; C. Sathishkumar; Payal Shah; G. Maryam Banu; Ms Akileshwari; and P. Kiruthika.
The application for the patent was published on March 13, under issue no. 11/2026.
According to the abstract released by the Intellectual Property India: "A Self-Learning Autonomous Sensor Network for Real-Time Intelligent Decision Making in Smart Environments Abstract The rapid evolution of smart environments-spanning smart cities, industrial automation, healthcare, and intelligent transportation-demands sensor networks that are not only connected but also adaptive, autonomous, and intelligent. This paper proposes a Self-Learning Autonomous Sensor Network (SLASN) designed to enable real-time intelligent decision making through continuous learning, decentralized control, and context-aware optimization. Unlike conventional sensor networks that rely on static configurations and centralized processing, the proposed framework integrates distributed machine learning, adaptive sensing policies, and autonomous collaboration among heterogeneous sensor nodes. Each node is equipped with lightweight learning mechanisms that dynamically adjust sensing frequency, communication patterns, and energy consumption based on environmental context, data relevance, and predicted system states. A federated and reinforcement learning-inspired paradigm enables collective intelligence without compromising data privacy or scalability. The architecture supports real-time anomaly detection, event prediction, and proactive response by fusing multimodal sensor data and evolving decision rules over time. Experimental evaluations in simulated smart environment scenarios demonstrate significant improvements in decision accuracy, latency reduction, energy efficiency, and system resilience compared to traditional static and semi-adaptive sensor networks. The proposed SLASN framework establishes a foundation for next-generation intelligent environments that can self-optimize, self-heal, and evolve autonomously in response to dynamic real-world conditions."
Disclaimer: Curated by HT Syndication.
