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

Abstract

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 generative models, such as Generative Adversarial Networks, Variational Autoencoders, 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. AIoT systems continue to face persistent challenges, including 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 AIoT functionalities across key domains—namely cognitive augmentation, resource efficiency, network traffic, cybersecurity, and anomaly detection—while examining their synergistic potential to improve system-level environmental performance across two interconnected layers in sustainable smart cities. At the operational layer, key findings reveal that integrating GenAI with AIoT systems enhances urban efficiency, adaptability, autonomy, robustness, and resilience by conserving resources, optimizing network traffic flows, securing infrastructures, and detecting anomalies before they escalate. Specifically, the fusion of generative intelligence with federated learning promotes sustainable, energy-efficient 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. They also strengthen cybersecurity through enhanced intrusion prevention and threat detection. Additionally, they enable early identification and mitigation of anomalies, boosting operational efficiency and system robustness. These improvements stabilize sustainable smart city system functioning and prevent disruptive failures. At the environmental layer, as key findings indicate, these operational gains cascade into indirect but tangible ecological benefits, while generative models advance the core pillars of AIoT by enabling proactive, autonomous, context-aware, and self-adaptive systems that further enhance the environmental performance of sustainable smart cities. Thus, while the five domains primarily underpin the operational backbone of urban systems, their cascading effects extend to ecological outcomes. The proposed conceptual framework, distilled from key findings, integrates GenAI and AIoT and highlights 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 gained 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.