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2020-12-02
Yu, C., Quan, W., Cheng, N., Chen, S., Zhang, H..  2019.  Coupled or Uncoupled? Multi-path TCP Congestion Control for High-Speed Railway Networks 2019 IEEE/CIC International Conference on Communications in China (ICCC). :612—617.

With the development of modern High-Speed Railway (HSR) and mobile communication systems, network operators have a strong demand to provide high-quality on-board Internet services for HSR passengers. Multi-path TCP (MPTCP) provides a potential solution to aggregate available network bandwidth, greatly overcoming throughout degradation and severe jitter using single transmission path during the high-speed train moving. However, the choose of MPTCP algorithms, i.e., Coupled or Uncoupled, has a great impact on the performance. In this paper, we investigate this interesting issue in the practical datasets along multiple HSR lines. Particularly, we collect the first-hand network datasets and analyze the characteristics and category of traffic flows. Based on this statistics, we measure and analyze the transmission performance for both mice flows and elephant ones with different MPTCP congestion control algorithms in HSR scenarios. The simulation results show that, by comparing with the coupled MPTCP algorithms, i.e., Fully Coupled and LIA, the uncoupled EWTCP algorithm provides more stable throughput and balances congestion window distribution, more suitable for the HSR scenario for elephant flows. This work provides significant reference for the development of on-board devices in HSR network systems.

2020-11-23
Guo, H., Shen, X., Goh, W. L., Zhou, L..  2018.  Data Analysis for Anomaly Detection to Secure Rail Network. 2018 International Conference on Intelligent Rail Transportation (ICIRT). :1–5.
The security, safety and reliability of rail systems are of the utmost importance. In order to better detect and prevent anomalies, it is necessary to accurately study and analyze the network traffic and abnormal behaviors, as well as to detect and alert any anomalies if happened. This paper focuses on data analysis for anomaly detection with Wireshark and packet analysis system. An alert function is also developed to provide an alert when abnormality happens. Rail network traffic data have been captured and analyzed so that their network features are obtained and used to detect the abnormality. To improve efficiency, a packet analysis system is introduced to receive the network flow and analyze data automatically. The provision of two detection methods, i.e., the Wireshark detection and the packet analysis system together with the alert function will facilitate the timely detection of abnormality and triggering of alert in the rail network.