AIoT-Enabled Human–AI Collaboration for Smart City Environmental Monitoring

Real-time environmental monitoring is increasingly supported by Internet of Things (IoT)-based sensing infrastructures and machine learning techniques capable of processing high-frequency urban environmental data. However, many existing solutions remain focused primarily on data acquisition, visualization, or isolated prediction tasks, with limited integration of predictive analytics and human expertise within a unified decision-support framework. This limitation highlights the need for intelligent monitoring approaches that combine continuous sensing, short-term prediction, and Human-in-the-Loop interpretation. Hybrid Human–AI Collaborative Networks (HCNs) are increasingly relevant for supporting environmental monitoring and decision-making in smart cities. This paper proposes an Artificial Intelligence of Things (AIoT)-enabled collaborative framework that integrates distributed uRADMonitor sensors, cloud-based data management, machine learning models, and human stakeholders into a unified monitoring ecosystem. A distributed uRADMonitor sensing network was deployed across multiple locations in Sibiu for continuous environmental monitoring, while the machine learning experiments presented in this study were conducted independently using three sensing-node datasets collected from different neighbourhoods in Sibiu. The same overall modelling and validation methodology was applied to each dataset, while the predictor set reflected the environmental variables available at each sensing node. Several machine learning algorithms were evaluated for Air Quality Index (AQI) prediction, including Linear Regression, Random Forest, and Gradient Boosting. Under a random train–test split, Random Forest was the best-performing contemporaneous model across all three sensing nodes, although predictive performance varied substantially between locations. In contrast, the short-term forecasting experiment provided the operationally relevant predictive component, with Ridge Regression achieving positive skill relative to persistence across forecasting horizons from 5 to 120 min.