Service Provisioning in Digital Twin Networks with Semantic-Aware Information Centric Networking

Digital Twin Networks (DTNs) are an emerging paradigm where DTs collaborate to share knowledge and deliver intelligent services. To meet the need for low-latency and localized processing, DTs can be deployed at the network edge. However, effective mechanisms for inter-twin communication and service provisioning remain largely unexplored. In this paper, we address these challenges by leveraging a name-based communication paradigm, namely Information Centric Networking (ICN), enhanced with semantic-awareness. To overcome the limitations of exact name matching, we integrate deep learning models into the ICN forwarding fabric to compute semantic similarity. This enables the discovery of relevant cached service results or routes to DT service providers, even under naming heterogeneity. A preliminary evaluation shows improved service provisioning success and reduced latency compared to standard ICN delivery.