MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641101729 A) filed by Madhankumar C; Mrs. N. Saranya - Hindusthan Institute Of Technology; Mr. J. Jeraldroy - Indra Ganesan College Of Engineering; Mr. K. Mahadevan - Care College Of Engineering Autonomous; Mrs. M. Agalya - Vivekanandha College Of Engineering For Women; Ms R. Vaishnavi - Coimbatore Institute Of Engineering And Technology; Ms. M. Lakshmi Priya - Erode Sengunthar Engineering College; Mrs. N. Nandhini - Kpr Institute Of Engineering And Technology; Ms. Chitra S - Kings Engineering College; Dr. R. Arun Kumar - V. S. B. Engineering College; Mrs. S. Suganya - Jct College Of Engineering And Technology; Ms. K. Sudhapriya - Nehru Institute Of Engineering And Technology; and Ms M. S. Vinu - Jct College Of Engineering And Technology on August 23, 2026, for Quantum-Inspired Swarm Learning Platform For Self-Healing And Self-Optimizing Industrial Iot Ecosystems.
Inventors include Mrs. N. Saranya - Hindusthan Institute Of Technology; Mr. J. Jeraldroy - Indra Ganesan College Of Engineering; Mr. K. Mahadevan - Care College Of Engineering Autonomous; Mrs. M. Agalya - Vivekanandha College Of Engineering For Women; Ms R. Vaishnavi - Coimbatore Institute Of Engineering And Technology; Ms. M. Lakshmi Priya - Erode Sengunthar Engineering College; Mrs. N. Nandhini - Kpr Institute Of Engineering And Technology; Ms. Chitra S - Kings Engineering College; Dr. R. Arun Kumar - V. S. B. Engineering College; Mrs. S. Suganya - Jct College Of Engineering And Technology; Ms. K. Sudhapriya - Nehru Institute Of Engineering And Technology; and Ms M. S. Vinu - Jct College Of Engineering And Technology.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: Quantum-Inspired Swarm Learning Platform for Self-Healing and Self-Optimizing Industrial IoT Ecosystems ABSTRACT The present invention relates to a Quantum-Inspired Swarm Learning Platform for Self Healing and Self-Optimizing Industrial Internet of Things (IIoT) Ecosystems, designed to provide intelligent, distributed, adaptive, and resilient management of industrial IoT environments. Conventional IIoT systems depend heavily on centralized monitoring and control mechanisms, which may experience performance degradation, communication delays, single points of failure, and limited adaptability when industrial conditions change dynamically. The proposed invention addresses these limitations through a distributed swarm based learning architecture enhanced with quantum-inspired optimization techniques. The platform comprises a plurality of intelligent industrial IoT nodes, sensor and actuator units, edge computing devices, communication interfaces, a swarm learning engine, a quantum inspired optimization module, an anomaly detection module, a self-healing controller, a self optimization engine, and a centralized or distributed monitoring interface. The IIoT nodes continuously collect operational parameters including temperature, pressure, vibration, energy consumption, machine status, production conditions, network conditions, and other industrial parameters. The collected data is processed locally or collaboratively at edge nodes to identify abnormal conditions, equipment degradation, communication failures, resource limitations, and performance deviations. The swarm learning engine enables multiple distributed IIoT nodes to collaboratively learn operational patterns without requiring continuous transfer of raw industrial data to a centralized server. Each node generates local learning information and exchanges selected knowledge with neighboring nodes through a secure swarm communication mechanism. The platform thereby supports collaborative intelligence while reducing communication overhead and improving scalability across geographically distributed industrial environments. The quantum-inspired optimization module enhances swarm decision-making by employing quantum-inspired probability representations, state transitions, and optimization strategies to explore multiple possible solutions for resource allocation, routing, workload distribution, fault recovery, energy management, and operational parameter tuning. The optimization process enables the platform to identify efficient solutions within complex industrial environments while reducing the computational limitations associated with conventional optimization approaches. The self-healing mechanism continuously evaluates the health status of industrial devices, communication links, computing resources, and software services. When a fault or abnormal condition is detected, the system determines an appropriate recovery strategy and automatically redirects workloads, reconfigures communication paths, isolates defective nodes, activates alternative resources, or modifies operational parameters. This enables the IIoT ecosystem to recover from selected failures with minimal human intervention and reduced operational downtime. The self-optimization engine continuously evaluates system performance using parameters such as latency, throughput, energy consumption, resource utilization, reliability, fault frequency, and production efficiency. Based on the evaluated parameters, the platform dynamically adjusts network configurations, workload distribution, computing resources, and operational policies. The swarm learning mechanism allows optimization knowledge obtained by one node or group of nodes to be shared with other relevant nodes, enabling continuous adaptation of the overall industrial ecosystem. The proposed platform therefore provides a unified intelligent framework capable of learning, detecting, predicting, healing, and optimizing industrial IoT operations. By combining swarm intelligence, distributed learning, quantum-inspired optimization, edge computing, and autonomous recovery mechanisms, the invention improves system resilience, resource utilization, operational efficiency, scalability, and adaptability. The platform can be applied to smart manufacturing, industrial automation, predictive maintenance, energy management, process monitoring, smart factories, robotics, and other mission-critical Industrial IoT environments.
Disclaimer: Curated by HT Syndication.
