Remote Monitoring and Maintenance of Smart Ships: A Framework for Optimizing Performance Using IoT and Machine Learning

Xinkai Mi(CSSC Heavy Industry (Qingdao) Marine Equipment Research Institute Co., Ltd.,Qingdao City, Shandong)
Yujie Liu(CSSC Heavy Industry (Qingdao) Marine Equipment Research Institute Co., Ltd.,Qingdao City, Shandong)
Jiaojiao Sun(CSSC Heavy Industry (Qingdao) Marine Equipment Research Institute Co., Ltd.,Qingdao City, Shandong)

Abstract

The current paper offers such conceptual framework of the remote control and support of smart ships that are based on the joint synergies involving Internet of Things (IoT) technologies and machine learning (ML) algorithms. Induced solely by the use of secondary sources of data (i.e. scholarly literature, industry reports, and real-life case-studies), the study will address the feasibility of intelligent systems carrying out real-time diagnostics, anticipating equipment failures, and optimising vessel performance. Three-tier architecture is introduced which combines sensor networks, data transmission platforms, cloud-based analytics, and graphical user interface support. It is proven in practice by the implementation carried out in major maritime companies and tested under the following advantages: the shortened suspension period, the improvement of fuel consumption, and the increase of the safety. Although the operational benefits are immense, the research also discusses technical and organizational issues, such as the ability of IT systems produced by different vendors to communicate with each other, the lack of cybersecurity, and a gap between the skills of the maritime workforce. It has been concluded in the paper that flexible, scalable and interoperable framework are key to driving predictive maintenance as well as remote operations, towards next generation of smart maritime systems.

References

Kechagias EP, Chatzistelios G, Papadopoulos GA, Apostolou P. Digital transformation of the maritime industry: A cybersecurity systemic approach. International Journal of Critical Infrastructure Protection. 2022 Jul 1;37:100526.

Autsadee Y, Jeevan J, Bin Othman MR, Mohd Salleh NH. Maritime Society 5.0: a global transition on human economy and civilisation for maritime sustainability. Australian Journal of Maritime & Ocean Affairs. 2025 Jan 2;17(1):1-26.

Mourtzis D, Angelopoulos J, Panopoulos N. Design and development of an IoT enabled platform for remote monitoring and predictive maintenance of industrial equipment. Procedia Manufacturing. 2021 Jan 1;54:166-71.

Kalafatelis AS, Nomikos N, Giannopoulos A, Alexandridis G, Karditsa A, Trakadas P. Towards predictive maintenance in the maritime industry: A component-based overview. Journal of Marine Science and Engineering. 2025 Feb 25;13(3):425.

Sanchez-Gonzalez PL, Díaz-Gutiérrez D, Leo TJ, Núñez-Rivas LR. Toward digitalization of maritime transport?. Sensors. 2019 Feb 22;19(4):926.

Aslam S, Navarro A, Aristotelous A, Garro Crevillen E, Martınez-Romero A, Martínez-Ceballos Á, Cassera A, Orphanides K, Herodotou H, Michaelides MP. Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data. Sensors. 2025 Jun 24;25(13):3923.

Nguyen M, Tran L. From Shore to Sea: IoT Solutions for Enhanced Vessel Monitoring and Maritime Safety. Asian American Research Letters Journal. 2024 Jun 8;1(4).

Zou Y, Xiao G, Li Q, Biancardo SA. Intelligent Maritime Shipping: A Bibliometric Analysis of Internet Technologies and Automated Port Infrastructure Applications. Journal of Marine Science and Engineering. 2025 May 19;13(5):979.

Durlik I, Miller T, Cembrowska-Lech D, Krzemińska A, Złoczowska E, Nowak A. Navigating the sea of data: a comprehensive review on data analysis in maritime IoT applications. Applied Sciences. 2023 Aug 28;13(17):9742.

Aslam S, Herodotou H, Garro E, Martínez-Romero Á, Burgos MA, Cassera A, Papas G, Dias P, Michaelides MP. IoT for the maritime industry: challenges and emerging applications. In2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS) 2023 Sep 17 (pp. 855-858). IEEE.

Filom S, Amiri AM, Razavi S. Applications of machine learning methods in port operations–A systematic literature review. Transportation Research Part E: Logistics and Transportation Review. 2022 May 1;161:102722.

Durlik I, Miller T, Cembrowska-Lech D, Krzemińska A, Złoczowska E, Nowak A. Navigating the sea of data: a comprehensive review on data analysis in maritime IoT applications. Applied Sciences. 2023 Aug 28;13(17):9742.

Shaveko O. Maintenance of Autonomous Vessels (advantages and disadvantages) in comparison with Conventional Ships.

Sharma N, Shamkuwar M, Singh I. The history, present and future with IoT. InInternet of things and big data analytics for smart generation 2018 Dec 31 (pp. 27-51). Cham: Springer International Publishing.

Raptodimos Y. Combination of reliability tools and artificial intelligence in a hybrid condition monitoring framework for ship machinery systems.

Ullah A, Anwar SM, Li J, Nadeem L, Mahmood T, Rehman A, Saba T. Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex & Intelligent Systems. 2024 Feb;10(1):1607-37.

Aslam S, Michaelides MP, Herodotou H. Internet of ships: A survey on architectures, emerging applications, and challenges. IEEE Internet of Things journal. 2020 May 8;7(10):9714-27.

Nguyen H. Digitaalisen kaksosen hyödyntäminen meriteollisuudessa.

Durlik I, Miller T, Dorobczyński L, Kozlovska P, Kostecki T. Revolutionizing marine traffic management: a comprehensive review of machine learning applications in complex maritime systems. Applied Sciences. 2023 Jul 11;13(14):8099.

Liu RW, Liang M, Nie J, Lim WY, Zhang Y, Guizani M. Deep learning-powered vessel trajectory prediction for improving smart traffic services in maritime Internet of Things. IEEE Transactions on Network Science and Engineering. 2022 Jan 7;9(5):3080-94.

Xue J, Yang P, Li Q, Song Y, Gelder PV, Papadimitriou E, Hu H. Machine Learning in Maritime Safety for Autonomous Shipping: A Bibliometric Review and Future Trends. Journal of Marine Science and Engineering. 2025 Apr 8;13(4):746.

Sepasgozar SM, Davis SR, Li H, Luo X. Modeling the implementation process for new construction technologies: Thematic analysis based on Australian and US practices. Journal of Management in Engineering. 2018 May 1;34(3):05018005.

Lea P. IoT and Edge Computing for Architects: Implementing edge and IoT systems from sensors to clouds with communication systems, analytics, and security. Packt Publishing Ltd; 2020 Mar 6.

Berger ML, Mamdani M, Atkins D, Johnson ML. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part I. Value in health. 2009 Nov;12(8):1044-52.

Chen H, Wen Y, Huang Y, Xiao C, Sui Z. Edge Computing Enabling Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal. 2024 Nov 5.

Ferreira C, Gonçalves G. Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of manufacturing systems. 2022 Apr 1;63(May):550-62.

Parsons MA, Robinson KM, Kara MY, Stinson NT, Snyder DJ, Woodward DC, Brown AJ. Application of a distributed system architectural framework to naval ship concept and requirements exploration. Naval Engineers Journal. 2020 Dec 1;132(4):105-24.

Hassan SM. Application of Artificial Intelligence in Marine Engine Control System: Recent Advancements.

Tebni D. The digital platforms in the maritime industry: An exploratory case study of two multisided digital platforms (Master's thesis, University of South-Eastern Norway).

Drozhzhyn OL. IoT in container shipping industry: applications and examples of solutions. Systems and Technologies. 2024 Dec 17;68(2):112-20.

Christos SC, Panagiotis T, Christos G. Combined multi-layered big data and responsible AI techniques for enhanced decision support in Shipping. In2020 International Conference on Decision Aid Sciences and Application (DASA) 2020 Nov 8 (pp. 669-673). IEEE.

Nguyen M, Tran L. From Shore to Sea: IoT Solutions for Enhanced Vessel Monitoring and Maritime Safety. Asian American Research Letters Journal. 2024 Jun 8;1(4).

Islam SM, Munasinghe M, Clarke M. Making long-term economic growth more sustainable: evaluating the costs and benefits. Ecological Economics. 2003 Dec 1;47(2-3):149-66.

Angell LC, Klassen RD. Integrating environmental issues into the mainstream: an agenda for research in operations management. Journal of operations management. 1999 Aug 1;17(5):575-98.

Bonilla SH, Silva HR, Terra da Silva M, Franco Gonçalves R, Sacomano JB. Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges. Sustainability. 2018 Oct 17;10(10):3740.

DOI: http://dx.doi.org/10.12345/jms.v6i2.30406