An Intelligent Framework for Optimal Consumer Electronics’ Management in Smart Pocket Parks for Stimulating the Vitality of the Elderly

Abstract

This study focuses on optimal energy management of smart pocket parks in smart city for promoting elderly vitality based on consumer electronic devices. Recognizing the challenges elderly individuals face in maintaining an active lifestyle, the proposed system integrates innovative smart energy management system to provide energy demand of personalized sports activities compatible with users’ needs. In the proposed Deep Q-Learning (DQL) smart model, the system continuously learns from user interactions and health data to tailor recommendations, ensuring optimal energy consumption of devices and improvement in fitness levels. A key feature of this approach is the emphasis on energy management optimization for the devices used in the park. Through smart energy solutions, including solar-powered devices and efficient energy usage models, the system minimizes the environmental impact while ensuring consistent performance. Simulated Annealing (SA) is employed to fine-tune system parameters and avoid energy wastage, ensuring that devices operate at peak efficiency. The combination of DQL and SA allows for a synergistic system that not only promotes physical activity but also ensures sustainability and energy efficiency in smart cities. This methodology sets the stage for future advancements in urban health solutions for the elderly.