An Approach of Consensus-Based Double-Layer Blockchain System for Multi-Ship Collision Risk Mitigation Considering COLREGs

Abstract

Ship collision avoidance (CA) is the most fundamental and essential issue of maritime safety, regardless of whether the ship is a conventional ship or a future maritime autonomous surface ship (MASS). However, effective ship-to-ship communication has been recognized as a critical issue and one of the major challenges affecting the success of ship CA. This study intends to investigate the basic trust concerns in the cybersecurity components of the ship CA process and develop a blockchain-based solution for trustworthy communication. The novel idea behind applying blockchain to ship CA decision-making is that the various participating ships in a scenario of dynamically changing ship encounters form a decentralized network of opportunities, which makes blockchain an appealing tool to offer a solution for evaluating and maximizing trust in entity dynamics. The mechanism of entity participation in upholding a master chain for avoiding ship encounters is described in this work. The research begins by analyzing conventional ship CA decision scenarios under COLREGs in order to identify formal communication strategies that can make information more understandable. Based on this, it is demonstrated how beliefs of trust (BoT) between entities are encoded and combined on the chain to enable entities in the encounter scenario to form an initial opinion about another entity before becoming familiar with it. This first encounter occurs during ship CA. Second, this article examines how encounter ships have temporary rights in the process of making CA decisions. This generates blocks and attaches them to the chain at the consensus layer of blockchain technology. Finally, this study undertakes a case test of ship CA based on the aforementioned communication principles. In order to ensure safe and effective communication during brief, one-time ship encounters, the results offer a practical method for conveying CA reasoning in encounter scenarios. As a result, it can aid in the selection of better (more reliable) nodes to uphold the evidence-based understanding of the ship CA process and assess the reliability of unidentified members.