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Genetic Algorithm for Ship Robbery Emergency Reporting System

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EN
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EN
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.
Twórcy
  • National Taiwan Ocean University, Keelung, Taiwan
  • National Taiwan Ocean University, Keelung, Taiwan
  • National Taiwan Ocean University, Keelung, Taiwan
autor
  • National Taiwan Ocean University, Keelung, Taiwan
Bibliografia
  • [1] 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. https://doi.org/10.1016/j.cor.2015.06.011
  • [2] 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. https://doi.org/10.1016/j.future.2024.03.046
  • [3] 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. https://doi.org/10.1109/JIOT.2022.3219674
  • [4] 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. https://doi.org/10.1109/TITS.2020.3023957
  • [5] 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. https://doi.org/10.1016/j.ress.2024.110187
  • [6] 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.
  • https://doi.org/10.1109/PLANS46316.2020.9109806
  • [7] 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. https://doi.org/10.1080/13658816.2020.1792914
  • [8] 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. https://doi.org/10.1109/JIOT.2021.3056091
  • [9] 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. https://doi.org/10.1109/MAES.2021.3070862
  • [10] 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. https://doi.org/10.1016/j.oceaneng.2022.110569
  • [11] Riveiro, M., Pallotta, G., & Vespe, M. (2018). Maritime anomaly detection: A review. WIREs Data Mining and Knowledge Discovery, 8(5), e1266. https://doi.org/10.1002/widm.1266
  • [12] Han, Y., & Chu, L. (2025). A systematic review and bibliometric analysis for maritime emergency management. Journal of Sea Research, 205, 102585. https://doi.org/10.1016/j.seares.2025.102585
  • [13] 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. https://doi.org/10.1016/j.oceaneng.2022.112178
  • [14] Ribeiro, C. V., Paes, A., & de Oliveira, D. (2023). AIS-based maritime anomaly traffic detection: A review. Expert Systems with Applications, 231, 120561. https://doi.org/10.1016/j.eswa.2023.120561
  • [15] 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.
  • https://doi.org/10.1016/j.ijdrr.2018.02.015
  • [16] 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. https://doi.org/10.3390/jmse11051093
  • [17] 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.
  • [18] J. H. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
  • [19] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
  • [20] M. Mitchell, An Introduction to Genetic Algorithms. MIT Press, 1998.
  • [21] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, 2001.
  • [22] 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.
Uwagi
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Bibliografia
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