## 2 Maritime Digitalization
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt}} \hline \hline Blockchain for AI & Provides ideal storage for deep learning data and supports the maintenance of data integrity, provenance, reliability and security, leading to more trustworthy and credible AI decisions [38; 39; 40]. \\ \hline \multirow{4}{*}{Blockchain for AI} & Supports trustworthy data-sharing, reduces trust friction, and orchestrates AI solutions and decentralized intelligence across multiple parties [41; 42; 43]. \\ \cline{2-3}  & Enables secure and efficient knowledge management [44]. \\ \cline{2-3}  & Coordinates untrustworthy devices and edge-AI-enabled IoT [44]. \\ \cline{2-3}  & Improves trust in AI-powered robotic decisions and collective decision-making [45; 46]. \\ \cline{2-3}  & Enables autonomous AI agents to engage and execute economic and financial transactions [47]. \\ \hline \multirow{4}{*}{AI for blockchain} & Makes blockchain applications more resilient against attacks [41; 48; 49]. \\ \cline{2-3}  & Supports blockchain network design and performance optimization, such as assisting in mining node pool decisions [50]. \\ \cline{2-3}  & Allows for better blockchain services with AI [51; 52]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synergy of AI and Blockchain Technologies. sensing hardware, information systems, and networks, which are responsible for gathering extensive data from various sources. These sources include Automatic Identification Systems (AIS) for ship tracking, coastal radar and Synthetic Aperture Radar (SAR) for maritime surveillance, meteorological and oceanographic data sensing systems, as well as data related to shipping, port operations, and human activities. This collected data is then fed into and managed with the data platform layer, which focuses on large-scale storage solutions tailored for spatial-temporal domain data. This layer also incorporates immutable storage, such as blockchain ledgers, to ensure data traceability and auditability, which are crucial for maintaining data integrity and compliance for cross-organization processes in the maritime ecosystem. Relying on the domain data, the model layer utilizes AI and advanced simulation and modeling techniques like Discrete Event Simulation (DES), Multi-Agent Systems (MAS), and Computational Fluid Dynamics (CFD) to create intelligent systems that support maritime operations. These models help predict, analyze, and optimize maritime operations. Leveraging the data and model intelligence from the underlying layers, these technologies create a synergistic effect, support domain applications and use cases from maritime traffic safety management [53], smart shipping [54], port management [19], ocean environment monitoring, and decarbonization [8]. Collectively, these layers illustrate how data is collected, processed, and utilized to enhance various aspects of the maritime industry, integrating technologies like blockchain for secure data handling and AI for predictive analytics and services intelligence. Integrating and further substantiating the transformative potential of blockchain, AI, and IoT in the maritime sector are crucial for addressing the increasing complexity of maritime operations and meeting the demands of a globalized trade environment.