Adaptive AI Control for Energy-Efficient Thermal Management in Wearable Intelligent Devices

Abstract

The rise in the use of artificial intelligence, biomedical sensors, and wireless communications within wearable intelligent devices creates a need for effective thermal management. Approaches to managing heat in conventional methods are typically static, with fixed thresholds and preset power states; however, these approaches may be too rigid when dealing with varying workloads, environmental factors, and battery limitations. An adaptive AI control framework that incorporates thermal sensing, workload prediction, energy management, and safety-constrained feedback control is proposed in this paper. The proposed framework will allow dynamic workload scheduling, processor frequency adjustments, and communication duty cycles to take place while ensuring thermal safety and user comfort. This study uses a literature-based methodology to create an evaluation strategy that includes thermal response, energy consumption, battery life, prediction accuracy, control stability, and computational overhead. This study does not provide experimental data or simulation results demonstrating performance improvement over existing adaptive thermal management systems. The proposed framework establishes a basis for creating and testing energy-efficient thermal management strategies for wearable intelligent devices operating under limited resources.