The ship’s automation system plays a crucial role in aiding users in making informed decisions by collecting and conveying operational data, while also integrating control components to ensure the ship adheres to these decisions. Figure 1 illustrates a comprehensive configuration consisting of two essential systems: The Integrated Navigation System (INS) for ship navigation and the Integrated Control and Monitoring System overseeing control of onboard facilities. Within the INS, critical unit systems like the Electronic Chart Display Information System (ECDIS), RADAR, Track Control System (TCS), Bridge Alarm Monitoring System (BAMS), and Autopilot work harmoniously, playing vital roles in planning and executing the ship’s route. The INS follows the IEC61924-2 standard, precisely defining its modular structure and performance criteria, ensuring the monitoring of connected sensor availability, effectiveness, and integrity ^[1][13].

While traditional INS systems mainly support human decision-making by visualizing data, ongoing research aims to minimize human intervention and empower the system to navigate autonomously by perceiving its surroundings. Studies focus on developing stable algorithms, guidance, and control mechanisms for collision avoidance, with the goal of significantly enhancing the system’s capability to effectively avert potential collisions ^[2][3][14,15].

The ship is outfitted with an Integrated Control and Monitoring System (ICMS), functioning as a decentralized control system overseeing the operation of machinery and electrical equipment. Figure 2 illustrates the system architecture of ICMS. The green section signifies the area where the controller manages each piece of equipment. Within the orange area is the Operator Work Station (OWS), offering a Human Machine Interface (HMI) for users to monitor or directly control the operational status. The rising demand for eco-friendly ships, such as LNG carriers, electric propulsion vessels, and Dual Fuel Engine ships, has introduced greater complexity and diversity of control factors within the ICMS. To address these advancements, dedicated research efforts are concentrated on creating an environment that supports the swift and secure development of sophisticated ships and control technologies, leveraging digital twin technology ^[4][16]. Furthermore, ongoing studies aim to integrate and optimize systems through virtual simulation environments ^[5][17]. However, to transform these research activities into tangible outcomes and effectively implement them on actual ships, the existence of a real-time data platform seamlessly connecting and managing ship data is imperative.

The ship’s navigation-related sensor data is generated following the IEC61162-1 standard ^[6][18] in the case of serial communication and the IEC61162-450 standard ^[7][19] for Ethernet communication, which has been expanded to handle various types of data. The Automatic Identification System (AIS) extracts the data generated by these protocols and disseminates messages containing the location and voyage information of the ship to surrounding vessels over the Very High Frequency (VHF) frequency. The transmission period is shorter for faster vessels, ensuring neighboring ships can promptly and safely detect them.

The AIS data encompasses different categories based on message types ^[8][20]. Among these, the message types directly related to navigation status are 1, 2, and 3, allowing ships to gain insight into the current navigation status and the locations of surrounding vessels through the distribution of these messages. Additionally, AIS transmits the data it sends and receives to the INS network using a standard protocol based on IEC61162-1 ^[6][18], enabling navigation equipment to comprehend the surrounding context and display relevant data to users.

Numerous ongoing studies are actively striving to enhance the operational and management performance of ships through the effective utilization of AIS data. In ^[9][21], researchers integrated AIS data to cluster ship trajectories, suggesting its potential use in collision avoidance by predicting ship paths. In ^[10][22], a preprocessing technique was proposed to manage and analyze the substantial quantities of AIS data collected. This technique aimed to enhance data management performance and route monitoring capabilities. Recognizing the limitations imposed by the limited transmission distance of AIS data through VHF due to Earth’s curvature, research is underway to harness satellites for collecting and utilizing a broader spectrum of AIS data, as demonstrated in ^[11][23]. This researchtudy successfully tracked ships through coastal images and AIS data obtained from satellites, effectively detecting unauthorized ship access.

While AIS data provides substantial navigational information, it is confined to data pertinent to the current ship state due to bandwidth restrictions. As the deployment of Low Earth Orbit (LEO) satellites expands the communication connectivity of ships and the phased realization of Maritime Autonomous Surface Ships (MASS) SS necessitates more advanced automation, a broader array of data delivery methods is imperative. This need arises to address the evolving requirements of maritime systems and unlock the potential benefits of advanced autonomous ship capabilities.

In conjunction with AIS data, a multitude of sensors embedded in engine systems, including the engines themselves, generate real-time data based on the ship’s operational status. Diverse research endeavors are actively contributing to the creation of a comprehensive platform capable of gathering such ship-related data and facilitating the expansion of data sharing and functionalities between ships and onshore systems.

In ^[12][24], the research centers on applying IoT technologies to unmanned ships, exploring intelligent recognition, sensor fusion, and communication. This woresearchk culminates in the proposal of an E-Navigation framework aimed at connecting ships with onshore systems. In ^[13][6], a Machine-Type Communication Framework is presented to enable efficient communication between peripheral ships and cloud systems, utilizing short-range and broadband connections. It aims to enhance MASS capabilities, promote interoperability, and facilitate seamless data exchange and collaboration in the maritime domain.

As the integration of IoT technology in ships advances, research has also delved into new services and service management. In addition to the earlier discussion on collision avoidance, there is a significant focus on creating optimal routes using weather forecast data to enhance operational performance in a safe and cost-effective manner ^[14][25]. In addition, there has been research aimed at creating an Operator Training Simulator (OTS) environment. This simulation setup onshore closely replicates the ship’s operating conditions ^[15][26]. The primary purpose of the OTS environment is to train both the crew and the ship’s system to collaborate effectively, eliminating the need to physically visit the site during the development stage of ship operation technology, which is becoming increasingly intricate. An investigation in ^[16][27] revealed that education through remote OTS environments contributes to enhanced navigation skills, especially as we move towards an era of training remote operators to handle actual MASS.

The expansion of technology and services in the maritime industry has led to a growing number of software applications that need to be managed. This increase in software complexity has posed challenges in management. In ^[17][28], a study was conducted to address this issue by efficiently allocating Software Quality Assurance (SQA) resources in ship operating environments. The focus was on predicting defects in newly developed software based on models trained using historical software defect data. As software becomes increasingly vital for ship operations, the importance and reliance on S/W quality management techniques have grown. This has created an essential research area dedicated to maintaining service quality within the unique context of the maritime environment.

Research was also conducted to build a cloud-based IoT service platform in conjunction with ship IoT devices ^[18][19][20][10,11,29]. Cloud platforms provide scalability, flexibility, security, and availability, which makes them well-suited for creating expansive IoT environments ^[21][30]. In ^[22][31], prominent cloud-based IoT platforms like AWS IoT, Azure IoT, Watson IoT, PTC ThingWorx, and Google IoT are identified. The researchtudy affirms that these public clouds provide essential functionalities for constructing extensive IoT environments. Among them, AWS IoT is the most widely used public cloud, boasting numerous references and ongoing research in designing and implementing structures tailored to specific applications and purposes ^[23][24][32,33].