Autonomy is the core capability of future systems, and architecture design is one of the critical issues in system development and implementation. To discuss the architecture of autonomous systems in the future, this paper reviews the developing progress of architectures from automation systems to autonomous systems. Firstly, the autonomy and autonomous systems in different fields are summarized. The article classifies and summarizes the architecture of typical automated systems and infer three suggestions for building an autonomous system architecture: extensibility, evolvability, and collaborability. Accordingly, this paper builds an autonomous waterborne transportation system, and the architecture is composed of the object layer, cyberspace layer, cognition layer, and application layer, the proposed suggestions made in the construction of the architecture are reflected in the inter-relationships at all layers. Through the cooperation of four layers, the autonomous waterborne transportation system can autonomously complete the system functions, such as system control and transportation service. In the end, the characteristics of autonomous systems are concluded, from which the future primary research directions and the challenges of autonomous systems are provided.

The Internet of Ships (IoS), by integrating advanced technologies, such as the Internet of Things, cloud computing, and artificial intelligence, aims to interconnect and communicate various physical devices related to maritime transportation, including ships, ports, traffic infrastructure, and warehouses. This integration is designed to optimize transportation decision making, reduce costs, enhance efficiency, improve safety, and promote environmental sustainability. Traditional IoS adopts a cloud computing-based data processing and service model, which, due to its centralized and remotely deployed nature, often places computing nodes far from the data collection and service demand points. This setup struggles to meet the high real-time and low-latency requirements of intelligent ships, traffic organization, remote control, and other applications. Edge computing, by decentralizing computing, storage, and network resources to the edge of the IoS, enables more responsive handling of device requests. It addresses critical requirements, such as intelligent access, real-time communication, and privacy protection in the IoS environment, facilitating intelligent, green communication, efficient data processing, and timely service responses. In this article, the current state of IoS and the relevant concepts of edge computing are introduced. The edge computing enabling IoS (EC-IoS) architecture and the core technologies driving EC-IoS development are systematically discussed. Emerging applications and the case studies of EC-IoS, including intelligent ships at different autonomy levels, intelligent transportation, smart ports, and warehouses, are summarized. Finally, challenges and future opportunities in open computing environments, maritime data management, system security, and resource management are outlined, providing a reference for optimizing maritime management and autonomous navigation.

Simulation technology has been extensively utilized in the study of ship entry and exit from ports, as well as navigation through waterways. It effectively mirrors the stochastic dynamic changes and interrelationships among various elements within the port system. This paper provides a comparative analysis of the advantages and disadvantages of various modeling methods used in ship navigation simulations. It proposes a simulation modeling approach for ship–port systems based on multi-agent information interaction, which simulates the entire process of ships entering and exiting ports and navigating through complex waterways, achieving a precise and detailed simulation of the entire port entry and exit process in complex waters. Using the Qiongzhou Strait as a case study, the validity and accuracy of the model are verified. The model is employed to quantitatively identify port navigation elements, assess waterway capacity, and evaluate port operational capability. Furthermore, the model enables the analysis of coordination among port channels, berths, and anchorages. Based on simulation results and port development plans, recommendations are provided to enhance port service levels and promote scientific, rational development and efficient operation of ports.

The remote control is an important function of intelligent ships. In this article, first, the functions and communication requirements of remote control ships are sorted out based on the “Rules for Intelligent Ships (2020)” issued by the China Classification Society (CCS). Second, a domain controller-based electronic and electrical architecture (EEA) for inland remote control ships is proposed. The functional domain of the ship is divided, combined with the physical layout of the ship, into the perception domain, communication domain, motion control domain, marine bridge domain, deck domain, cargo management domain, and cabin domain. Third, the real-time network communication system and the central gateway controller are designed, and the efficient information interaction between domain controllers is realized through a service-oriented communication architecture. The simulation experiment shows that the designed domain controller architecture and communication network can meet the information interaction requirements of the ship. Compared with the traditional distributed architecture, the ship EEA based on the domain controller has apparent advantages in system cost, system redundancy, functional load distribution, architecture scalability, communication load distribution, and information security and can give a design reference for inland remote control ships.

Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%.