Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies
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Original versionPerera, L. P., & Czachorowski, K. (2019, 17-20 June 2019). Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies. Paper presented at the OCEANS 2019 - Marseille. 10.1109/OCEANSE.2019.8867045
Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.