Artificial intelligence of things for sustainable smart city brain and digital twin systems: Pioneering Environmental synergies between real-time management and predictive planning

Abstract

Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the urgent need for innovative paradigms in urban development. In response, sustainable smart cities are increasingly leveraging advanced technological frameworks—most notably the convergence of Artificial Intelligence of Things (AIoT) and Cyber-Physical Systems (CPS)—as critical enablers for transforming their management and planning processes. Within this dynamic landscape, Urban Brain (UB) and Urban Digital Twin (UDT) have emerged as prominent AIoT-powered city platforms. Defined by their complex functionalities and multi-layered architectures, these systems exemplify Cyber-Physical Systems of Systems (CPSoS), offering a cohesive foundation for integrating real-time operational responsiveness with strategic predictive foresight. Despite notable technological progress, a critical gap persists in effectively integrating the distinct yet complementary capabilities of UB and UDT within a structured and scalable framework. To the best of our knowledge, research on the explicit fusion of UB's real-time analytics—enabled through stream processing—with UDT's predictive analytics—driven by simulation modeling—is scant, if not absent. Most existing studies continue to treat UB and UDT as siloed systems, failing to recognize the critical need to synchronize their respective operational and strategic functions. This fragmentation limits the ability of urban systems to respond both adaptively and proactively to the complex, interrelated challenges of environmental sustainability. To address this critical gap, this study introduces a novel foundational framework—Artificial Intelligence of Things for Sustainable Smart City Brain and Digital Twin Systems—designed to synergistically integrate UB and UDT as AIoT-enabled platforms within a unified CPSoS architecture. This framework addresses the critical disconnect between real-time operational management and strategic predictive planning, delivering an integrated pathway for advancing environmentally sustainable smart city development goals. Harnessing the complementary strengths of UB and UDT, it empowers cities to respond dynamically to immediate urban demands while ensuring consistent alignment with long-term sustainability goals. UB's real-time analytics enhance the efficiency of daily urban operations, whereas UDT's predictive modeling anticipates and simulates future scenarios. Together, they establish a synergistic feedback loop: UB's real-time insights continuously inform UDT's strategic simulations, while UDT's long-range forecasts iteratively refine UB's operational decision-making. The framework thus equips researchers, practitioners, and policymakers with a robust methodology for designing and implementing adaptive, efficient, and resilient urban ecosystems. It facilitates the development of intelligent urban environments that can advance environmental sustainability by integrating solid theoretical foundations with actionable strategies.