Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications

Durlik, Irmina; Miller, Tymoteusz; Cembrowska-Lech, Danuta; Krzemińska, Adrianna; Złoczowska, Ewelina; Nowak, Aleksander

doi:10.3390/app13179742

Cited by 18 publications

(7 citation statements)

References 102 publications

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“…Another area of focus is data analysis in maritime IoT applications. Durlik et al (2023) review the role of data analysis in maritime IoT, demonstrating its impact on areas such as predictive maintenance and efficient port operations. This highlights the importance of data-driven decision-making in maximizing the benefits of IoT in specific sectors.…”

Section: Analysis and Discussion On Iot Implementation Challenges And...mentioning

confidence: 99%

Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA - vision, architectural elements, and future directions

Idoko Peter Idoko,

Onuh Matthew Ijiga,

Daniel Obekpa Agbo

et al. 2024

World J. Adv. Eng. Technol. Sci.

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This paper presents a comprehensive comparative analysis of the Internet of Things (IoT) implementation in Ghana and the USA, focusing on their respective visions, architectural elements, and future directions. The study begins with an introduction to IoT's significance in modern society, followed by a detailed methodology outlining the research design, data collection methods, and criteria for comparative analysis. The findings reveal distinct approaches to IoT implementation in Ghana and the USA, reflecting varying levels of technological infrastructure, economic impact, and societal influence. The analysis delves into the comparative aspects of IoT vision, including strategic goals and technological advancements, and examines the architectural elements such as hardware, software, network solutions, and data management. The paper also addresses the challenges and barriers in IoT implementation, highlighting technical, policy, and economic constraints. Furthermore, it explores emerging trends, opportunities, and recommendations for future IoT development, providing final thoughts on the trajectories of IoT in both countries. This study contributes to the understanding of IoT's role in different national contexts and offers valuable insights for future IoT initiatives and policies.

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Section: Analysis and Discussion On Iot Implementation Challenges And...mentioning

confidence: 99%

Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA - vision, architectural elements, and future directions

Idoko Peter Idoko,

Onuh Matthew Ijiga,

Daniel Obekpa Agbo

et al. 2024

World J. Adv. Eng. Technol. Sci.

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“…For instance, Ebrahimi et al [23] employed machine learning to prioritize regulatory tasks, achieving intelligent allocation of regulatory resources and enhancing regulatory efficiency. Similarly, Durlik et al [24] utilized machine learning to analyze and forecast regulatory data, enabling the dynamic adjustment of regulatory resources and further improving resource utilization efficiency. Moreover, there are endeavors aimed at exploring novel applications and methodologies of machine learning in marine supervision.…”

Section: Literature Reviewmentioning

confidence: 99%

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Tian,

Wang

2024

Water

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This study investigates the prioritization and resource allocation strategies adopted by the coastal local governments of Qingdao, Dalian, and Xiamen in the context of marine regulatory reform aimed at enhancing regulatory efficiency. Data on relevant opinions, departmental requirements, and existing resource allocations were collected through a questionnaire survey. A backpropagation (BP) neural network was then applied to analyze the survey data, prioritize regulatory tasks, and propose resource allocation schemes. The findings demonstrate that integrating machine learning into marine regulation can significantly improve resource utilization efficiency, optimize task execution sequences, and enhance the scientific and refined nature of regulatory work. The BP neural network model exhibited strong predictive capabilities on the training set and demonstrated good generalization abilities on the test set. The performance of the BP neural network model varied slightly across different management levels. For the management level, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. For the supervisory level, these metrics were 81%, 83%, and 78%, respectively. At the employee level, the accuracy, precision, and recall rates were 79%, 81%, and 76%, respectively. These results indicate that the BP neural network model can provide differentiated resource allocation recommendations based on the needs of different management levels. Additionally, the model’s performance was assessed based on the employees’ years of experience. For employees with 0–5 years of experience, the accuracy, precision, and recall rates were 82%, 84%, and 79%, respectively. For those with 5–10 years of experience, the metrics were 83%, 86%, and 80%, respectively. For employees with over 10 years of experience, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. These data further confirm the applicability and effectiveness of the BP neural network model across different experience groups. Thus, the adoption of machine learning technologies for optimizing marine regulatory resources holds significant practical value, aiding in the enhancement of regulatory capacity and effectiveness within coastal local governments.

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“…It is possible to identify underlying issues or deterioration using these measurements. In addition, machine learning-based predictive maintenance models can provide actionable and precise insights regarding the state and functionality of critical apparatus and systems situated aboard vessels [102], [103]. Proactive scheduling of maintenance operations, efficient management of spare parts inventories, and resource allocation are all achievable for ship operators who can forecast equipment failure probabilities and estimate the equipment's remaining useful life [104].…”

Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications

Cited by 18 publications

References 102 publications

Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA - vision, architectural elements, and future directions

Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA - vision, architectural elements, and future directions

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

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