Work

The human digital twin for service management: Architecture and user modeling

Year: 2026

Type: article

Abstract: Human Digital Twins (HDTs) are increasingly adopted across various domains, yet their application to network and service management remains limited. Nevertheless, HDTs offer significant potential for optimizing service configurations based on human behavior, preferences, and profiles. In this paper, we analyze the role of HDTs in network and service management, identifying key functionalities such as collaborative learning for user modelling, Quality of Experience (QoE) and emotion prediction, application personalization, and behavioral forecasting for network “what-if” analysis. We propose an architectural framework designed to monitor user status and generate a corresponding digital replica that interacts with other network components to enhance service delivery. Our solution integrates collaborative learning for QoE modelling and applies it to service optimization. By aggregating user data from multiple HDTs, the approach improves prediction accuracy and resource optimization. Extensive performance evaluations demonstrate that the proposed collaborative HDT framework enhances the final utility function that considers perceived quality and resource usage by 27% compared to non-collaborative methods.

Source: Computer Communications

DOI: 10.1016/j.comcom.2026.108526

Authors: Matteo Fratta, Alessandro Floris, Simone Porcu, Luigi Atzori

Institution: University of Cagliari

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,620