MipMap Digital Twin: A Scalable IoT-Based Framework for Smart Cities and Circular Economy

As cities expand and infrastructures grow more complex, traditional management approaches often fail to keep pace with the demands of sustainability, efficiency, and resilience. The MipMap Digital Twin framework addresses these challenges by integrating IoT, GIS, geospatial intelligence, and AI-driven analytics into a scalable platform that bridges day-to-day operations with urban-scale decision-making. Unlike static models, the digital twin continuously synchronizes with real-world data, enabling real-time monitoring, predictive insights, and automated optimization across diverse environments such as production facilities, retail chains, and smart campuses.

This study demonstrates the first-stage deployment of MipMap Digital Twin, connecting a production facility and multiple café locations into a shared digital environment. The system leverages IoT sensors for environmental monitoring, BLE for asset tracking, MQTT-based cloud–edge integration for low-latency data flow, and ArcGIS for geospatial analytics, unified within a game engine–based 3D visualization. Key applications include energy and resource management, safety simulations, workflow automation, and urban business intelligence.

Case studies illustrate the framework’s adaptability: from monitoring coffee roasting conditions and optimizing café operations to simulating safety asset placement in research labs and supporting business expansion with demographic-driven GIS analysis. Results highlight improvements in service efficiency, resource optimization, and decision-making while also revealing challenges such as data privacy, UI accessibility, and stakeholder engagement.

Looking ahead, MipMap Digital Twin will expand into multi-agent reinforcement learning (MARL), AI-driven demand forecasting, and integration with next-generation 6G connectivity to enhance scalability and responsiveness. Its support for circular economy principles—such as waste reduction, predictive maintenance, and resource lifecycle management—positions the framework as a powerful tool for building sustainable, intelligent, and adaptive urban ecosystems.

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