Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security

Abstract

Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.