Risk management of (IoT) (internet of things) and big data on LNG carrying

LNG transportation is the process of extracting natural gas composed primarily of methane and small amounts of ethane, propane, water, carbon dioxide and other components from underground, purifying it in treatment facilities, liquefying it and transporting it safely and economically over long distances. However, the LNG transportation process involves significant safety and operational risks. The aim of this study is to systematically identify the potential risk arising from the use of Internet of Things and Big Data Analytics in LNG transportation and to contribute to the development of an effective risk management framework by prioritizing these risks according to their significance. Within the scope of this study, a literature review and expert opinions were utilized to identify 7 main and 26 associated sub-risk criteria. The identified risk factors were analyzed using the interval Type-2 Fuzzy Sets (IT2FS) logic-based method to better model uncertainty and variations in expert judgments. According to the results, among the main risk categories Environmental and Safety Risk was identified as the most critical, followed respectively by Cybersecurity, Data Reliability and Misanalysis, Data Privacy and Legal Risks, Operational Technology, Cost and Investment Risk and System Integration Challenges. Regarding the sub-risk criteria, Inadequate System Performance During Emergencies was found to be the most important risk factor, followed by Incorrect Sensor Data, Theft of Sensitive Data, Sensor Failures, System Crash, System Lockdown/ransomware, Failure to Detect Anomalies, Disclosure of sensitive Operational Data, Operational Manipulation, Ongoing Maintenance and Updates, Failure to Achieve Expected Returns, Incompatibility with Legacy Systems, Poorly Designed Algorithms, Complexity in Managing Multiple Systems, Faulty Data in Predictive Maintenance, Communication Failures, High Initial Costs, Data Privacy Breaches, Disclosure of Crew's Personal Data, Data Overload and Decision-Making Challenges, Legal Compliance, Violation of Competition Rules, Online Monitoring and Commercial Violations, Port and Customs Authorities Data Demands and Overdependence. The least significant sub-risk factor was identified as Cross-border Data Transfer Issues.