Published Paper Details:

Currently, smartphones are used for nearly all activities, including chatting, watching videos, studying, and using various applications. To conserve battery power, phones use power-saving features like sleep mode, but these methods are mostly reactive. This means they only start working when the battery begins to drain, which can slow down apps and delay background tasks. Because of this, users may experience lag or reduced performance. To solve this, this paper presents an Intelligent Predictive Framework for Dynamic Mobile Energy Optimization. The proposed framework adopts a lightweight predictive approach for intelligent and efficient mobile energy optimization. Instead of simply turning apps on or off, the system analyzes user behavior, app usage, system load, and phone temperature in real time. Based on this, it predicts what needs to be done and manages apps smartly. Applications are divided into three states: Active, Throttled, and Deep Hibernate. Important apps run smoothly, while unnecessary apps are controlled to save battery. The system also manages device temperature to avoid overheating. Results show improved battery savings, smoother performance, and a more comfortable user experience.