Towards Efficient Communication in Digital Twin Networks: Experimental Analysis of Transport Protocols Under Long Delays

Abstract

Ensuring reliable and efficient communication under high-delay conditions remains a major challenge for Network Digital Twin (NDT) systems operating across heterogeneous networks. This paper experimentally evaluates the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), QUIC, Stream Control Transmission Protocol (SCTP), the DTN7 Bundle Protocol Version 7 (BPv7) implementation, and the NASA Interplanetary Overlay Network (ION) using a controlled delay-generation environment. Performance is assessed through throughput, bandwidth utilization, transmission time, transfer time, End-to-End Completion Time (ECT), and packet inter-arrival variability. The results show that conventional feedback-driven transport protocols suffer significant performance degradation as network delay increases, whereas UDP maintains high throughput but does not guarantee reliable delivery. In contrast, BPv7-based communication mechanisms, particularly ION using the Licklider Transmission Protocol convergence layer (ION-LTP), achieve superior end-to-end responsiveness under the evaluated long-delay conditions. To enable unified comparison across heterogeneous protocol families, this paper introduces ECT as a protocol-independent application-level metric. The experimental benchmark demonstrates that no single communication mechanism is universally optimal, highlighting the need for adaptive communication strategies in NDT systems. Based on the observed protocol trade-offs, a conceptual Artificial Intelligence (AI)-assisted Deep SARSA framework is presented as a potential approach for dynamic protocol selection under varying network conditions. Its implementation, agent training, and experimental validation are reserved for future work.