Digital twins for smart campus networks: An end-to-end framework for multi-domain data intelligence

Heterogeneous communication environments are expected to be pivotal in sixth-generation (6G) networks, enabling devices to seamlessly utilize various coexisting connectivity options, including cellular technologies (e.g., 4G/5G) and complementary access technologies like Wi-Fi, Bluetooth, and other short-range wireless systems. The challenge lies not just in device diversity but in effectively managing environments where nodes can dynamically switch between different network interfaces to optimize performance, reliability, and quality of service. This paper explores the role of Digital Twins (DTs) as an enabling framework for managing these multi-connectivity environments, where both mobile and non-cellular access technologies collectively enhance overall network functionality. We highlight key limitations in current DT implementations for these connectivity-rich scenarios and propose a scalable DT framework tailored explicitly for integrated cellular and auxiliary wireless access. Developed with commercial mobile devices as endpoints and a widely adopted twinning platform, our solution enhances management efficiency, optimizes resource allocation, and improves Quality of Service (QoS) in dynamic connectivity settings. We evaluate two architectural designs for DT deployment with respect to workload distribution and scalability. Additionally, we present a generative AI-driven analytics pipeline that consolidates descriptive, diagnostic, predictive, and prescriptive analytics into a unified closed-loop system. Experimental results from a real-world smart campus environment show that the offloaded architecture keeps CPU usage below 6% on mobile devices with up to 40 nodes while maintaining low latency and stable performance. The analytics pipeline achieves near-interactive response times, ensuring real-time network awareness and adaptive decision-making in multi-connectivity network environments.