Development of a Real-time Radiation Detection System that Uses IoT to Provide Real-time Monitoring on a Custom-built...

Effective radiation monitoring is crucial for ensuring public security and safety, particularly in the event of nuclear (e.g., nuclear accident, fallout, etc.) or radiological (e.g., radiation source spill, dirty bombs, etc.) emergencies. Traditional monitoring methods often lack real-time capabilities and comprehensive data visualization and analysis, which can delay response time and increase further risks. To address this gap, this research proposes an Advanced Radiation Detector for real-time radiation monitoring. The novelty is in its use of an internet of things (IoT)-based framework to collect and transmit all the data (e.g., radiation level, temperature, pressure, humidity, and air quality) via a GSM module to web cloud all from one device with enhanced visualization in the self-made website. The study setup will obtain data across the area of the Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, Bangladesh, using this Advanced Radiation Detector. This data is visualized by creating a scatter circle map of spatial distribution of radiation measurements. By visualizing radiation data taken from the self-developed detector and visualizing it using the self-made website, we found the Advanced Radiation Detector meets its objectives perfectly. This research aims to enable real-time radiation monitoring and provides timely insights for emergency responses. By integrating IoT in the system, it has directly overcome the limitation of traditional methods. The Advanced Radiation Detector has immense potential to improve security and safety measures. The current Rooppur Nuclear Power Plant (RNPP) development in Bangladesh emphasizes the necessity of customized solutions for efficient radiological risk management. In order to improve Bangladesh's nuclear safety framework, this study presents a revolutionary IoT-based radiation monitoring system that uses machine learning for real-time assessment and predictive analytics.