The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations. 

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

A rule-based maritime traffic situation complex network approach for enhancing situation awareness of vessel traffic service operators

The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean.

/pdf/an-efficient-feature-augmentation-and-lstm-based-method-to-2p8dm4z1.pdf

An Efficient Feature Augmentation and LSTM-Based Method to Predict Maritime Traffic Conditions

An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2 also can reach 0.9954, which can provide decision support for the operation of shipping companies. 

/pdf/fuel-consumption-prediction-models-based-on-machine-learning-1fehruam.pdf

Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods

Trends of digitalization, intelligence and greening of global shipping industry based on CiteSpace Knowledge Graph

Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring.

https://www.mdpi.com/2077-1312/11/1/191/pdf?version=1673507076

Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management

Improving the planning and management of maritime operations is a key element in driving the maritime supply chain. Although technological innovations facilitate the management of maritime operations, they are still affected by disruptions and uncertainties related to the need for more reliable information. One of the most crucial pieces of information for steering the maritime supply chain is the estimated time of arrival (ETA) of vessels at their destination, as accurate ETA predictions can improve the determination of optimal vessel routes, the monitoring of maritime traffic and the planning of port operations.In this context, we propose to leverage three years of historical vessel trip data. The objective of this work is to present an approach based on deep learning models to predict the ETA of vessels in the Saint Lawrence River. The models were trained on different data sources, taking into account various types and characteristics of vessels and their respective itineraries. This study highlights the potential of deep learning-based methods to leverage maritime historical data to predict ETA, providing a valuable solution for managing maritime supply chains. 

Deep Learning Approaches for Vessel Estimated Time of Arrival Prediction: A Case Study on the Saint Lawrence River

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Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management

Deep Learning Approaches for Vessel Estimated Time of Arrival Prediction: A Case Study on the Saint Lawrence River