abbr. SJ GMU
ISSN 2657-5841 (printed)
ISSN 2657-6988 (online)
DOI: 10.26408
Reconstructing Damaged Data in AIS and Other Telecommunications Systems: A Survey
Marta Szarmach*1 
https://orcid.org/0000-0002-3793-6641 Ireneusz Czarnowski2 https://orcid.org/0000-0003-0867-3114
* Corresponding author
1
Gdynia Maritime University, Department of Marine Telecommunications, 81-87 Morska St., 81–225 Gdynia, Poland,
e-mail: m.szarmach@we.umg.edu.pl
2
Gdynia Maritime University, Department of Information Systems, 81-87 Morska St., 81–225 Gdynia, Poland,
ORCID 0000-0003-0867-3114, e-mail: i.czarnowski@umg.edu.pl
Abstract:
AIS (Automatic Identification System) is a telecommunication system created to enable ships to transmit information regarding their trajectories (such as their position, speed, course, etc.) to other ships and shore stations. With the use of AIS, collisions between ships can be avoided. Unfortunately, AIS suffers from some technical issues that lead to part of the transmitted data being damaged (incorrect or missing). This paper contains a review of machine learning based methods of reconstructing this damaged AIS data as well as examples of inspiration from other telecommunication systems for dealing with this kind of a problem. Finally, after analysing frameworks available in the relevant literature, a novel algorithm for AIS data reconstruction is briefly presented.
Keywords:
AIS data analysis, trajectory reconstruction, machine learning, telecommunication
Issue:
Pages:
15
26
Doi:
Submitted:
16.06.2023
Accepted:
06.07.2023
Published:
30.09.2023
Download full text in pdf:
This article is an open access article distributed under a Creative Commoms Attribution (CCBY 4.0) licence
References:
Alippi, C., Boracchi, G., Roveri, M., 2012, On-line Reconstruction of Missing Data in Sensor/Actuator Networks by Exploiting Temporal and Spatial Redundancy, 2012, International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
Altman, N.S., 1992, An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression, The American Statistician, vol. 46, no. 3, pp. 175–185.
Balaji, V., Duraisamy, V., Umapathi, N., 2010, Cluster Based Packet Loss Prediction using Packet Lost Segment in Wireless Network, 2010, IEEE International Conference on Computational Intelligence and Computing Research, pp. 102–105.
Chen, T., Guestrin, C., 2016, XGBoost: A Scalable Tree Boosting System, KDD-16 Proceedings, pp.785–794.
Debnath, L., 1998, Wavelet Transform and Their Applications, PINSA-A, vol. 64, A, no. 6, pp. 685–713.
Dias, M.L.D., Mattos, C.L.C., da Silva, T.L.C., de Macedo, J.A.F., Silva, W.C.P., 2020, Anomaly Detection in Trajectory Data with Normalizing Flows, International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
Dobrkovic, A., Iacob, M.-E., van Hillegersberg, J., 2016, Maritime Pattern Extraction from AIS Data Using a Genetic Algorithm, 2016, IEEE International Conference on Data Science and Advanced Analytics, pp. 642–651.
ESA, European Space Agency, 2019, Satellite — Automatic Identification System (SATAIS) Overview, ESA Official Website, https://artes.esa.int/satellite-%E2%80%93-automatic-identification-system-satais-overview (19.04.2023).
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., 1996, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231.
Faten, M.Z., Elbiaze, H., 2009, Analysis and Prediction of Real Network Traffic, Journal of Networks, vol. 4, no. 9, pp. 855–865.
Hanyang, Z., Xin, S., Zhenguo, Y., 2019, Vessel Sailing Patterns Analysis from SAIS Data Based on K-means Clustering Algorithm, 4th IEEE International Conference on Big Data Analytics, pp. 10–13.
Ho, T.K., 1998, The Random Subspace Method for Constructing Decision Forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832–844.
Hsu, H.-S., Hung, Y.-J., Hsu, Z.-H., Yeh, J.-F., 2012, Speech Attribute Classifier Using Support Vector Machine for Speech Packet Loss Concealment, 2012 International Conference on Speech Database and Assessments, pp. 68–71.
Husain, A., Cuperman, V., 1995, Reconstruction of Missing Packets for CELP-Based Speech Coders, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 245–248.
ITU, International Telecommunication Union, 2014, Recommendation ITU-R M.1371-5, ITU Official Website https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1371-5-201402-I!!PDF-E.pdf (19.04.2023).
Jin, J., Zhou, W., Jiang, B., 2020, Maritime Target Trajectory Prediction Model Based on the RNN Network, [in:] Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z., Chen, B. (eds.), Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol. 572, pp. 334–342, Springer, Singapore.
Kontopoulos, I., Varlamis, I., Tserpes, K., 2020, Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection, [in:] Tserpes, K., Renso, C., Matwin, S. (eds.), Multiple-Aspect Analysis of Semantic Trajectories. MASTER 2019. Lecture Notes in Computer Science, vol 11 889, Springer, Cham.
Kowalska, K., Peel, L., 2012, Maritime Anomaly Detection using Gaussian Process Active Learning, 15th International Conference on Information Fusion, pp. 1164–1171.
Lei, B., Mingchao, D., 2018, A Distance-Based Trajectory Outlier Detection Method on Maritime Traffic Data, 4th International Conference on Control, Automation and Robotics, pp. 340–343.
Li, S., Liang, M., Wu, X., Zhao, L., Liu, R.W., 2020, AIS-Based Vessel Trajectory Reconstruction with U-Net Convolutional Networks, IEEE 5th International Conference on Cloud Computing and Big Data Analytics, pp. 157–161.
Li, Y., Zhang, Y., Zhu, F., 2016, The Method of Detecting AIS Isolated Information Based on Clustering and Distance, 2nd IEEE International Conference on Computer and Communications, pp. 870–873.
Liang, M., Liu, R.W., Zhong, Q., Liu, J., Zhang, J., 2019, Neural Network-Based Automatic Reconstruction of Missing Vessel Trajectory Data, IEEE 4th International Conference on Big Data Analytics (ICBDA).
Liu, F.T., Ting, K.M., Zhou, Z.-H., 2008, Isolation Forest, 8th IEEE International Conference on Data Mining, pp. 413–422.
Machado, T., Maia, R., Santos, P., Ferreira, J., 2019, Vessel Trajectories Outliers, 9th International Symposium on Ambient Intelligence, pp. 247–255.
Mieczyńska, M., Czarnowski, I., 2021, DBSCAN Algorithm for AIS Data Reconstruction, Procedia Computer Science, vol. 192, pp. 2512–2521.
Pallotta, G., Vespe, M., Bryan, K., 2013, Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction, Entropy, vol. 15, pp. 2218–2245.
Pan, L., Li, J., 2010, K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks, Wireless Sensor Network, vol. 2, pp. 115–122.
Prevost, R., Coulon, M., Bonacci, D., LeMaitre, J., Millerioux, J., Tourneret, J., 2012, Extended Constrained Viterbi Algorithm for AIS Signals Received by Satellite, IEEE First AESS European Conference on Satellite Telecommunications (ESTEL).
PyNetLabs, 2022, Difference between TCP and UDP, PyNetLabs.com, https://www.pynetlabs.com/
udp-vs-tcp/ (28.04.2023).
Seta, T., Matsukura, H., Aratani, T., Tamura, K., 2016, An Estimation Method of Message Receiving Probability for a Satellite Automatic Identification System Using a Binomial Distribution Model, Scientific Journals of the Maritime University of Szczecin, vol. 46, no. 118, pp. 101–107.
Shai, S.-S., Shai, B.-D., 2014, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, New York.
Shi, B., Su, Y., Zhang, D., Wang, C., Abouomar, M.S., 2019, Research on Trajectory Reconstruction Method Using Automatic Identification System Data for Unmanned Surface Vessel, IEEE Access, vol. 7, pp. 170 374–170 384.
Singh, S., Heymann, F., 2020, Machine Learning-Assisted Anomaly Detection in Maritime Navigation using AIS Data, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 832–838.
Szarmach, M., Czarnowski, I., 2022, Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform, IEEE Access, vol. 10, pp. 109 119–109 131.
Szarmach, M., Czarnowski, I., 2023, Decision Tree-Based Algorithms for Detection of Damage in AIS Data, International Conference on Computational Science.
Theodoropoulos, G.S., Tritsarolis, A., Theodoridis, Y., 2019, EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime Data, [in:] Tserpes, K., Renso, C., Matwin, S. (eds.), Multiple-Aspect Analysis of Semantic Trajectories. MASTER 2019. Lecture Notes in Computer Science, vol. 11 889, Springer, Cham.
Wang, G., Meng, J., Li, Z., Hesenius, M., Ding, W., Han, Y., Gruhn, V., 2020, Adaptive Extraction and Refinement of Marine Lanes from Crowdsourced Trajectory Data, Mobile Networks and Applications, vol. 25, pp. 1392–1404.
Wang, S., Gao, S., Yang, W., 2017, Ship Route Extraction and Clustering Analysis Based on Automatic Identification System Data, 8th International Conference on Intelligent Control and Information Processing, pp. 33–38.
Wang, T., Ye, C., Zhou, H., Ou, M., Cheng, B., 2020, AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder, [in:] Arai, K., Kapoor, S., Bhatia, R. (eds.), IntelliSys 2020. Advances in Intelligent Systems and Computing, Intelligent Systems and Applications, vol. 1251, pp. 529–546, Springer, Cham.
Wawrzaszek, R., Waraksa, M., Kalarus, M., Juchnikowski, G., Gorski, T., 2019, Detection and Decoding of AIS Navigation Messages by a Low Earth Orbit Satellite, [in:] Sasiadek, J. (ed.), Aerospace Robotics III. GeoPlanet: Earth and Planetary Sciences, Springer, Cham.
Xia, Z., Gao, S., 2020, Analysis of Vessel Anomalous Behavior Based on Bayesian Recurrent Neural Network, IEEE 5th International Conference on Cloud Computing and Big Data Analytics, pp. 393–397.
Yu, S., Tong, X., Huang, Y., Xie, R., Song, L., 2020, Learning-Based Quality Enhancement For Scalable Coded Video Over Packet Lossy Networks, 2020 IEEE International Conference on Multimedia and Expo (ICME).
Zhang, Q., Yuan, Q., Zeng, C., Li, X., Wei, Y., 2018, Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network, IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4274–4288.
Zhang, W., Goerlandt, F., Montewka, J., Kujala, P., 2015, A Method for Detecting Possible Near Miss Ship Collisions from AIS Data, Ocean Engineering, vol. 107, pp. 60–69.
Zhang, X., He, Y., Tang, R., Mou, J., Gong, S., 2018, A Novel Method for Reconstruct Ship Trajectory Using Raw AIS Data, 3rd International Conference on Intelligent Transportation Engineering, pp. 192–198.
Zhang, Z., Ni, G., Xu, Y., 2020, Trajectory Prediction Based on AIS and BP Neural Network, IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 601–605.
Marta Szarmach