Efficient Wireless Sensor Network for Radiation Detection in Nuclear Sites

Due to the severe damages of nuclear accidents, there is still an urgent need develop efficient radiation detection wireless sensor networks (RDWSNs) that precisely monitor irregular radioactivity. It should take actions mitigate costs accidental leakage, especially around sites are main sources electric power and a lot health industrial applications. Recently, leveraging machine learning (ML) algorithms RDWSNs promising solution due its several pros, such as online self-decision making. This paper addresses novel ML-based utilizes millimeter waves (mmWaves) cope with future requirements. Specifically, we leverage multi-armed bandit (MAB) algorithm called Thomson sampling (TS) 5G enabled RDWSN efficiently forward measured levels distributed sensors within monitoring area. The utilized nodes lightweight smart mounted on mobile devices measure radiations using software applications installed in these mobiles. Moreover, battery aware TS (BA-TS) proposed sensed fusion decision center efficiently. BA-TS reflects remaining each device prolong network lifetime. Simulation results ensure efficiency regards throughput lifetime over exhaustive search method.