Network Digital Twins: A Systematic Review

Network management is becoming more complex due to various factors. The growth of IoT increases the number of nodes to control. The combination of Edge and Fog Computing with distributed algorithms makes network synchronization challenging. Softwarized technologies simplify network management but create integration issues with legacy networks. Even in industrial settings where drones and mobile robots are used, proper network management is crucial yet challenging. In this context, digital twins can be used to replicate the structure and behavior of the physical network and at the same time can be used to successfully manage the complexity and heterogeneity of current networks. Despite the rapid growth of interest in the topic, a comprehensive overview of Network Digital Twin research is currently missing. To address this gap, in this paper, we present a systematic review of the Network Digital Twin literature. From the analysis of 138 primary studies, various insights emerge. Networking Digital Twin is a particularly recent concept that has been explored in the literature since 2017 and is experiencing a steady increase to this day. The vast majority of the studies propose solutions to optimize network performance, but there are also many oriented towards other goals such as security and functional suitability. The three most recurrent application domains, as self-reported in the primary studies, are those of smart industry, edge computing, and vehicular. The main research topics aim at network optimization, support for offloading, resource allocation, and floor monitoring, but also support in the implementation of machine learning algorithms such as federated learning. As a conclusion, Networking Digital Twin proves to be a promising emerging field both for academics and practitioners.