The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.

Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series data that characterize the behavior of the system are crucial for improving the effectiveness of Digital Twins. Long Short-Term Memory (LSTM) networks have been used to represent complex temporal dependencies and identify long-term links in the Industrial Internet of Things (IIoT). This paper proposed a Digital Twin temporal dependency technique using LSTM to capture the long-term dependencies in IIoT time series data, estimate the lag between the input and intended output, and handle missing data. Autocorrelation analysis showed the lagged links between variables, aiding in the discovery of temporal dependencies. The system evaluated the LSTM model by providing it with a set of previous observations and asking it to forecast the value at future time steps. We conducted a comparison between our model and six baseline models, utilizing both the Smart Water Treatment (SWaT) and Building Automation Transaction (BATADAL) datasets. Our model’s effectiveness in capturing temporal dependencies was assessed through the analysis of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The results of our experiments demonstrate that our enhanced model achieved a better long-term prediction performance.

As the metaverse progresses, it requires real-time, low-latency, and secure computing capabilities that conventional cloud-based systems cannot fully support. Multiaccess edge computing (MEC) addresses this demand by moving processing closer to the user; however, it also introduces new challenges in task placement, resource management, security, and trust. The blockchain is a promising enabler to address the limitations of trust, transparency, and centralized control in these systems. This survey systematically reviews 61 high-quality studies that explore blockchain-based solutions for task offloading and resource allocation in MEC. This work identifies the prevailing trends and research gaps using a structured method. The analysis reveals that over 60% of studies employ artificial intelligence-based techniques. Additionally, this work quantifies the adoption of various performance metrics from the literature and examines the case study distribution across application domains. Finally, this work outlines the technical challenges and opportunities for future research toward scalable, efficient, and trustworthy blockchain-enabled MEC frameworks.

Network Digital Twins (NDTs) function as virtual replicas of real networks, enabling real-time monitoring and analysis of 5G core networks. Graph Neural Networks (GNNs) have emerged as a promising approach for node failure classification within NDTs. However, the issue of graph imbalance which adversely affects GNN performance, particularly for minority classes has received limited attention in existing research. Information propagation among nodes can amplify majority class dominance, resulting in reduced classification accuracy. To address this challenge, we propose a Graph Fourier Transform with a Cost-Sensitive Message-Passing Neural Network (GFT-COSMEP) for imbalanced node classification. This approach first applies the Graph Fourier Transform (GFT) to generate node embeddings, followed by message aggregation on a sampled graph to enrich node representations. A cost-sensitive learner then leverages these embeddings to improve imbalanced learning performance. Sensitivity analysis confirms that tuning the model's cost-sensitive parameters enhances accuracy, achieving optimal results at a specific balance of loss terms. Experiments on real-world 5G network and NDT-based failure classification datasets demonstrate that GFT-COSMEP outperforms existing methods, achieving up to a 21.1% accuracy improvement at a 0.3 imbalance rate. These results highlight GFT-COSMEP's effectiveness in managing class imbalances in graph-based data.

A new network named the "Digital Twin Network" (DTN) uses the "Digital Twin" (DT) technology to produce virtual twins of real things. The network load and size continue to grow as a result of the development of 5G, the Internet of Things, and cloud computing technology as well as the advent of new network services. As a result, network operation and maintenance are becoming more difficult. A digital twin connects the real and digital worlds, exchanging data in both directions and revealing information about the progression of a network process. The framework of the Industrial Internet of Things, data processing, and digital twin network is taken into consideration in this article as a key aspect. This paper proposed a data-driven digital twin network architecture, that comprises the physical network layer (PNL), the digital twin layer(DTL), the application layer (AL), and what those layers encompass and beyond. Also, we presented DTN data types and protocols to be used for data integration.