Generative AI of things for sustainable smart cities: Synergizing cognitive augmentation, resource efficiency, network traffic, cybersecurity, and anomaly detection for environmental performance

• A novel GIoT and GAIoT framework is introduced to advance sustainable smart cities • GenAI augments AIoT pillars to enable proactive, context-aware, and self-adaptive urban systems • GenAI improves network traffic realism and communication efficiency in IoT and AIoT • GenAI enhances anomaly and threat detection in IoT- and AIoT-enabled systems • GenAI and FL promotes sustainable, energy-efficient IoT and AIoT system deployments Artificial Intelligence of Things (AIoT) has emerged as a transformative technology driving environmental sustainability in smart city development. However, the integration of Generative Artificial Intelligence (GenAI) within AIoT ecosystems remains largely unexplored. Current research predominantly addresses conventional AIoT frameworks, overlooking the innovative potential of advanced deep generative models, such as GANs, VAEs, Diffusion Models, transformers, and hybrid architectures, to significantly enhance situational awareness, system optimization, operational robustness, real-time responsiveness, and adaptive decision-making in complex urban environments. Current IoT and AIoT systems continue to face persistent challenges such as data scarcity, poor data quality, limited adaptability, imbalanced datasets, and inadequate context-awareness. This study addresses these gaps by systematically exploring how GenAI can enhance IoT and AIoT functionalities across key domains—namely cognitive augmentation, resource efficiency, cybersecurity, network traffic, and anomaly detection—while examining their synergistic potential to improve environmental performance in sustainable smart cities. Key findings reveal that emerging advancements in integrating GenAI with IoT and AIoT systems greatly improve urban adaptability, autonomy, and resilience by fostering more dynamic, autonomous, and robust systems and infrastructures. Generative models advance the five core pillars of AIoT: sensing, perceiving, learning, visualizing, and decision-making, by enabling proactive, context-aware, and self-adaptive urban systems to enhance their environmental performance. The fusion of generative intelligence with federated learning promotes sustainable, energy-efficient IoT and AIoT deployments by reducing data transmission, thereby lowering communication overhead and safeguarding user privacy. In networked environments, generative models improve synthetic traffic realism and communication efficiency. GenAI also strengthens cybersecurity through enhanced intrusion prevention, threat detection, and resilience in IoT- and AIoT-enabled smart cities. Furthermore, GenAI-driven anomaly detection facilitates early identification and mitigation of irregularities, boosting operational efficiency and system robustness. These findings underscore the powerful synergies of these domains enabled by GAIoT, emphasizing their collaborative potential to cultivate resilient, efficient, and environmentally conscious smart city ecosystems. The proposed conceptual framework, distilled from key findings, integrates GenAI and AIoT by highlighting both domain-specific advancements and their synergistic interactions. This framework holds significant potential to drive sustainable smart city development by fostering AIoT ecosystems that are more intelligent, resource-efficient, adaptive, secure, robust, and autonomous through the strategic application of generative intelligence. The insights from this study provide policymakers, urban planners, system designers, and technology developers with practical guidance to harness GAIoT for enhancing smart city resilience, sustainability, and operational intelligence.