Dynamic feature shedding in sensor enabled networks

Abstract

Described herein are devices, systems, methods, and processes for managing power consumption in network nodes by dynamically disabling and reenabling features based on real-time power consumption monitoring and historical data analysis. A machine learning model is trained using historical sensor data and historical feature data to predict power consumption and derive feature-to-power association data. The power budget is determined based on sustainability goals. Real-time power consumption is monitored, and features are disabled or reenabled based on their priorities and power consumption levels to maintain the power budget. The machine learning model is validated and updated using new historical data to improve its prediction accuracy and adaptability. The feature-to-power association data is distributed to network nodes and management systems for power management purposes. These devices, systems, methods, and processes enable efficient power management in network nodes while maintaining optimal network performance and contributing to sustainability goals.