Patents & Innovations

The present invention relates to a smart energy management system for wireless sensor networks that optimizes power consumption through intelligent scheduling and adaptive duty cycling mechanisms. The system employs machine learning algorithms to predict energy requirements and dynamically adjust network parameters to extend battery life while maintaining network performance.

This patent describes a comprehensive energy management framework for wireless sensor networks that can extend network lifetime by up to 40% compared to conventional approaches.

A novel fault-tolerant data aggregation method for IoT networks that ensures reliable data collection even in the presence of node failures. The method utilizes redundant data paths and machine learning-based anomaly detection to maintain data integrity and network reliability.

The invention provides a robust solution for data aggregation in IoT networks with built-in fault tolerance mechanisms that can handle up to 30% node failures without significant data loss.

An adaptive coverage optimization algorithm for wireless sensor deployment that automatically determines optimal sensor placement to maximize area coverage while minimizing the number of required sensors. The algorithm uses genetic algorithms and swarm intelligence techniques.

This patent covers a sophisticated deployment algorithm that can achieve 95% coverage efficiency with 25% fewer sensors compared to traditional grid-based deployments.

A blockchain-based security framework for IoT device authentication that provides decentralized identity management and secure communication channels. The framework ensures device authenticity and prevents unauthorized access through distributed consensus mechanisms.

The invention addresses critical security challenges in IoT networks by providing a scalable, decentralized authentication system that can handle thousands of devices simultaneously.

A machine learning-based predictive maintenance system that analyzes sensor data from industrial equipment to predict potential failures and optimize maintenance schedules. The system reduces downtime and maintenance costs through intelligent anomaly detection and failure prediction.

This patent describes a comprehensive predictive maintenance solution that can reduce unexpected equipment failures by up to 70% and maintenance costs by 30%.