This article presents an innovative approach to optimizing energy efficiency in Bluetooth Low Energy (BLE) implementations for Android wearable devices. The article addresses critical challenges in power management through the development of an adaptive connection manager that utilizes machine learning techniques. The proposed solution integrates an intelligent layer between the application and Bluetooth stack, implementing dynamic power state adjustments and smart reconnection protocols. By analyzing various operational modes and connection parameters, this article demonstrates significant improvements in power consumption while maintaining optimal performance. The article findings validate the effectiveness of AI-driven power management strategies and provide insights into future developments in BLE technology, particularly focusing on enhancing battery life in healthcare monitoring and fitness tracking applications.
Keywords: Bluetooth low energy, energy optimization, machine learning, power management, wearable technology
