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34 pages, 549 KiB

Open AccessReview

Graph Neural Networks for Routing Optimization: Challenges and Opportunities

by Weiwei Jiang, Haoyu Han, Yang Zhang, Ji’an Wang, Miao He, Weixi Gu, Jianbin Mu and Xirong Cheng

Sustainability 2024, 16(21), 9239; https://doi.org/10.3390/su16219239 - 24 Oct 2024

Cited by 12 | Viewed by 14561

Abstract

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks. Full article

(This article belongs to the Special Issue Sustainability of Future Satellite Communications: Opportunities and Challenges for 6G and Beyond)

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35 pages, 748 KiB

Open AccessReview

Graph Neural Network for Traffic Forecasting: The Research Progress

by Weiwei Jiang, Jiayun Luo, Miao He and Weixi Gu

ISPRS Int. J. Geo-Inf. 2023, 12(3), 100; https://doi.org/10.3390/ijgi12030100 - 27 Feb 2023

Cited by 91 | Viewed by 19518

Abstract

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research. Full article

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12 pages, 803 KiB

Open AccessArticle

Internet Traffic Prediction with Distributed Multi-Agent Learning

by Weiwei Jiang, Miao He and Weixi Gu

Appl. Syst. Innov. 2022, 5(6), 121; https://doi.org/10.3390/asi5060121 - 29 Nov 2022

Cited by 17 | Viewed by 3492

Abstract

Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches are trained and deployed in a centralized approach, without considering the realistic scenario in which multiple parties are concerned about the prediction process and the prediction model can be trained in a distributed approach. In this study, a distributed multi-agent learning framework is proposed to fill the research gap and predict Internet traffic in a distributed approach, in which each agent trains a base prediction model and the individual models are further aggregated with the cooperative interaction process. In the numerical experiments, two sophisticated deep learning models are chosen as the base prediction model, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). The numerical experiments demonstrate that the GRU model trained with five agents achieves state-of-the-art performance on a real-world Internet traffic dataset collected in a campus backbone network in terms of root mean square error (RMSE) and mean absolute error (MAE). Full article

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