EMANet: Revolutionizing Energy Efficiency in Smart Spaces through Machine Learning

In the modern smart environments, optimizing energy efficiency stands as a primary objective for attaining sustainability and resource management. Smart spaces, encompassing a variety of environments such as smart buildings, cities, and industrial settings, rely on efficient energy utilization to meet the growing demands of modern society while minimizing environmental impact This study investigates the optimization of energy efficiency within smart environments, a critical objective for sustainable resource management. Smart spaces, encompassing diverse environments like buildings, cities, and industries, require efficient energy utilization to address growing societal demands while minimizing environmental impact. To achieve this, an approach is proposed that leverages Machine Learning (ML) algorithms integrated with Adaptive Personal Area Networks (PANs) for optimized energy consumption. A novel algorithm, EMANet (EnergyManaged Attention Networks), is introduced for dynamic regulation of resource allocation and network configurations based on attention-driven insights. The performance of EMANet is evaluated through comparative simulation analysis against existing algorithms like Q-learning, Particle Swarm Optimization, and Ant Colony Optimization. Metrics including Energy Consumption Reduction, Network Lifetime Extension, and Resource Utilization Efficiency are employed for comparison. This research aims to achieve substantial improvements in energy efficiency through the combined application of ML algorithms and PANs, thereby promoting the sustainability of smart spaces. The results are expected to demonstrate the effectiveness of EMANet in optimizing energy usage and its potential to revolutionize energy management strategies in smart environments.