AI-Driven RIS-Assisted 5G Wireless Sensor Networks for Energy-Efficient Smart City Communications

Abstract

In order to handle the growing energy demand, water scarcity, waste production, and environmental sustainability issues, smart cities need intelligent resource management. But current resource management frameworks generally optimize resource use in each infrastructure separately, reducing the ability of coordinated decision making among interacting urban services. To overcome this, this study introduces a Digital Twin Assisted AI Framework (DTAI-RM) for smart city resource management for sustainability. It combines hierarchical digital twin, multi-modal IoT data fusion, demand forecasting with Long Short-Term Memory (LSTM), and a Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, to facilitate cross-domain optimization of energy, water, waste, and building resources adaptively. A reward mechanism that is aware about sustainability further optimizes the use of resources by considering energy efficiency, operational cost, water conservation and carbon emission. The proposed framework is assessed with a city scale digital twin in relation to multiple urban infrastructures operating under realistic operating conditions. The results of the experiments show significant gains compared to the conventional approaches in energy efficiency (91.37%), water-loss reduction (88.54%), waste collection optimization (84.92%), reduction in operational costs (86.73%) and reduction in carbon emissions (83.68%). These findings illustrate the potential of using AI-powered digital twins in conjunction with predictive analytics and reinforcement learning to optimize the coordination of resources, increase operational resilience, and reduce environmental impact. The proposed DTAI-RM framework aims to be a scalable, intelligent and sustainable tool for next generation smart city resource management.