Journal is indexed in following databases:
- SCOPUS
- Web of Science Core Collection - Journal Citation Reports
- EBSCOhost
- Directory of Open Access Journals
- TRID Database - Transportation Research Board
- Index Copernicus Journals Master List
- BazTech
- Google Scholar
2024 Journal Impact Factor - 0.6
2024 CiteScore - 1.9
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Genetic Algorithm for Ship Robbery Emergency Reporting System
1 National Taiwan Ocean University, Zhongzheng, Keelung, Taiwan
ABSTRACT: In contemporary maritime navigation, ships in distress primarily rely on satellite systems in conjunction with radio systems within the framework of the Global Maritime Distress and Safety System (GMDSS) to transmit distress signals. However, the insufficient confidentiality of satellite data enables pirates engaged in ship hijacking to intercept these signals, potentially endangering the safety of hostages on board. Additionally, the high communication costs associated with satellite information transmission often discourage fishing ships from incurring these expenses. Given these cost constraints, this study seeks to develop an intelligent emergency distress notification method integrated with the Automatic Identification System (AIS). Specifically, this study introduces an innovative intelligent radio emergency notification system by incorporating the concept of radio relay stations. The proposed system integrates the Genetic Algorithm (GA) with the Maritime Geographic Information System (MGIS) as an alternative rescue method for ships in distress. The system collects all relevant information from the distressed ship through shore stations, enabling it to respond to the ship and verify the receipt of distress messages transmitted via AIS. The proposed method functions as an intermediary for distress signal transmission and confirmation. By gathering ship positions, it establishes a mobile network for message dissemination, thereby enhancing the reliability and efficiency of emergency distress communications at sea.
KEYWORDS: Automatic Identification System (AIS), Maritime Safety, Piracy, Satellite Communications, Global Maritime Distress and Safety System (GMDSS), Ship Security, Cybersecurity, Vessel Management Systems (VMS)
REFERENCES
Hemmati, A., Stålhane, M., Hvattum, L. M., & Andersson, H. (2015). An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping. Computers & Operations Research, 64, 274–282. - doi:10.1016/j.cor.2015.06.011
Ben Farah, M., Ahmed, Y., Mahmoud, H., Shah, S. A., Al-kadri, M. O., Taramonli, S., Bellekens, X., Abozariba, R., Idrissi, M., & Aneiba, A. (2024). A survey on blockchain technology in the maritime industry: Challenges and future perspectives. Future Generation Computer Systems, 157, 618–637. - doi:10.1016/j.future.2024.03.046
Alqurashi, F. S., Trichili, A., Saeed, N., Ooi, B. S., & Alouini, M.-S. (2023). Maritime communications: A survey on enabling technologies, opportunities, and challenges. IEEE Internet of Things Journal, 10(4), 3525–3547. - doi:10.1109/JIOT.2022.3219674
Thombre, S., Zhao, Z., Ramm-Schmidt, H., Vallet García, J. M., Malkamäki, T., Nikolskiy, S., Hammarberg, T., Nuortie, H., Bhuiyan, M. Z. H., Särkkä, S., & Lehtola, V. V. (2022). Sensors and AI techniques for situational awareness in autonomous ships: A review. IEEE Transactions on Intelligent Transportation Systems, 23(1), 64–83. - doi:10.1109/TITS.2020.3023957
Li, H., Çelik, C., Bashir, M., Zou, L., & Yang, Z. (2024). Incorporation of a global perspective into data-driven analysis of maritime collision accident risk. Reliability Engineering & System Safety, 249, 110187. - doi:10.1016/j.ress.2024.110187
Singh, S. K., & Heymann, F. (2020, April 20–23). Machine learning–assisted anomaly detection in maritime navigation using AIS data. In Proceedings of the IEEE/ION Position, Location and Navigation Symposium. IEEE. - doi:10.1109/PLANS46316.2020.9109806
Kontopoulos, I., Varlamis, I., & Tserpes, K. (2021). A distributed framework for extracting maritime traffic patterns. International Journal of Geographical Information Science, 35(4), 767–792. - doi:10.1080/13658816.2020.1792914
Wei, T., Feng, W., Chen, Y., Wang, C.-X., Ge, N., & Lu, J. (2021). Hybrid satellite-terrestrial communication networks for the maritime Internet of Things: Key technologies, opportunities, and challenges. IEEE Internet of Things Journal, 8(11), 8910–8934. - doi:10.1109/JIOT.2021.3056091
Soldi, G., Gaglione, D., Forti, N., Di Simone, A., Daffinà, F. C., Bottini, G. (2021). Space-based global maritime surveillance. Part I: Satellite technologies. IEEE Aerospace and Electronic Systems Magazine, 36(9), 8–28. - doi:10.1109/MAES.2021.3070862
Wang, H., Liu, Z., Liu, Z., Wang, X., & Wang, J. (2022). GIS-based analysis on the spatial patterns of global maritime accidents. Ocean Engineering, 245, 110569. - doi:10.1016/j.oceaneng.2022.110569
Riveiro, M., Pallotta, G., & Vespe, M. (2018). Maritime anomaly detection: A review. WIREs Data Mining and Knowledge Discovery, 8(5), e1266. - doi:10.1002/widm.1266
Han, Y., & Chu, L. (2025). A systematic review and bibliometric analysis for maritime emergency management. Journal of Sea Research, 205, 102585. - doi:10.1016/j.seares.2025.102585
Ma, Q., Zhang, D., Wan, C., Zhang, J., & Lyu, N. (2022). Multi-objective emergency resources allocation optimization for maritime search and rescue considering accident black-spots. Ocean Engineering, 261, 112178. - doi:10.1016/j.oceaneng.2022.112178
Ribeiro, C. V., Paes, A., & de Oliveira, D. (2023). AIS-based maritime anomaly traffic detection: A review. Expert Systems with Applications, 231, 120561. - doi:10.1016/j.eswa.2023.120561
Karahalios, H. (2018). The severity of shipboard communication failures in maritime emergencies: A risk management approach. International Journal of Disaster Risk Reduction, 28, 1–9. - doi:10.1016/j.ijdrr.2018.02.015
Xing, B., Zhang, L., Liu, Z., Sheng, H., Bi, F., & Xu, J. (2023). The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea. Journal of Marine Science and Engineering, 11(5), 1093. - doi:10.3390/jmse11051093
M.-C. Tsou, S. L. Kao, and C.-M. Su, "Decision support from genetic algorithms for ship collision avoidance route planning and alerts," J. Navig., vol. 63, no. 1, pp. 167-182, 2010. doi: 10.1017/S037346330999021X. - doi:10.1017/S037346330999021X
J. H. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
M. Mitchell, An Introduction to Genetic Algorithms. MIT Press, 1998.
K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, 2001.
International Maritime Organization (IMO), "Adoption of amendments to the International Convention for the Safety of Life at Sea (SOLAS)," MSC 99(73), 2000.https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/MSCResolutions/MSC.99%2873%29.pdf.
Citation note:
Chang T.H., Kao S.L., Chou C.C., Chang H.C.: Genetic Algorithm for Ship Robbery Emergency Reporting System. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 2, doi:10.12716/1001.19.02.33, pp. 609-615, 2025
Authors in other databases:
Tsai-Hsin Chang:
orcid.org/0009-0003-9997-6555
orcid.org/0009-0003-9997-6555
Sheng Long Kao:
orcid.org/0000-0002-4035-0406
15725736900
orcid.org/0000-0002-4035-0406
15725736900
Chien-Chang Chou:
orcid.org/0000-0002-9551-4228
orcid.org/0000-0002-9551-4228
Hsiao Cheng Chang:
Other publications of authors:
File downloaded 1 times
