Wireless Radiation Monitoring System

Avram, Camelia; Folea, Silviu; Radu, Dan; Aştilean, Adina

doi:10.7148/2017-0416

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Real-time monitoring of ionizing radiation levels cannot be achieved through this means. With the advanced wireless technology, it's now possible to have real-time monitoring of radioactivity levels in uranium waste dumps, uranium mining, and deposits (Avram et al, 2017).…”

Section: Uranium Mining Radiation Monitoring Systemsmentioning

confidence: 99%

Remote radiation monitoring system at uranium exploration site in Mkuju river

Geofrey¹

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Usage of radioactive materials in a number of countless activities has increased rapidly worldwide, thus, increasing Uranium exploration in different countries including Tanzania. As Uranium decays, it emits ionizing radiations, which has health risks associated with it. The major threats occur when ionizing radiations produced during uranium decay exceed their levels. Causing skin burns and acute radiation syndrome, though in the long-term cause cancer and cardiovascular disease, are the substantial health effects to mention a few. Thus real-time and remote monitoring of radiation at uranium mining sites is mandatory to ensure environmental and human safety. Radiation detectors are used to monitor radiation at uranium mining sites by frequently visiting however achieving real-time and remote monitoring is still the challenge. This method is time-consuming and the existing radiation detectors used in Tanzania are exported from developed countries and are expensive. This design science study presents an effective and affordable system for remote monitoring of radiation at uranium mining sites using internet of things (IoT) technology. The system used prototype methodology and the radiation detector was programmed by using ESP32 microcontroller board. Which through its inbuilt Wi-Fi it was able to send data to the cloud data storage. The Message Queuing Telemetry Transport protocol facilitated remote visualization of radiation levels from the cloud storage dashboard and the developed mobile application. The end-users monitored radiation data and received notifications in case of excess radiation through the developed mobile application. The internet of things technology facilitated the end-users to visualize and monitor radiation levels anytime and anywhere via the internet.

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Section: Uranium Mining Radiation Monitoring Systemsmentioning

confidence: 99%

Remote radiation monitoring system at uranium exploration site in Mkuju river

Geofrey¹

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Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Abdelhakim

2023

NUCL SCI TECH

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Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires an exhaustive search for parameter estimation, which is time-consuming. In this study, heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors. Hence, the time consumption of MLE would be effectively reduced. First, the radiation source was detected using the k-sigma method. Subsequently, the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source. A comparative study was performed in which the estimation accuracy and time consumption of the MLE were evaluated for traditional methods and heuristic techniques. The traditional MLE was performed via a grid search method using fixed and multiple resolutions. Additionally, four commonly used heuristic algorithms were applied: the firefly algorithm (FFA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program. The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE. The time consumption for the heuristic-based MLE was 0.75, 0.03, 0.02, and 0.059 s for FFA, PSO, ACO, and ABC, respectively. The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE. Hence, heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.

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