The smart power optimization for sensor based wearable IoT devices using AI based deep learning

The intelligent power optimization for sensor-based wearable IoT devices with AI-based deep learning reduces the energy consumption of sensor-based wearable devices in IOT applications. The process employs deep learning algorithms based on artificial intelligence (AI) that can optimize the power consumption in such devices, making economical use of the battery and reducing frequent charging. Wearable IoT devices have long been a power-hungry type of wearable because they always need to sense and transmit data. This causes the battery life to shorten and your battery maintenance costs to increase. But at the same time, using deep learning-based algorithms of AI, they can understand and learn from their user's behavior so that it could save up to 40% power. This is done by real-time sensor data analysis and discovering user patterns, communication styles, and activity levels. This optimization process can actively change the power of different sensors and communication modules by identifying many patterns. This helps cut down power consumption and improves the overall performance of wearable IoT. In addition, energy harvesting from ambient sources such as body heat can be possible using this approach to extend battery lifetime even longer.