مشخصات پژوهش

This study investigates methods to enhance data accuracy in Internet of Things (IoT)-enabled air quality monitoring systems. IoT platforms have become instrumental in collecting environmental data from various sensors, but ensuring the accuracy of this data remains a challenge. We address this issue by employing Normal Scan Statistics, a statistical technique typically used for cluster detection, to assess and validate air quality sensor measurements. Through a comprehensive simulation study, we evaluate the performance of this method in identifying temporal clusters or anomalies within time series data. Our findings reveal that Normal Scan Statistics can significantly improve data accuracy, particularly in scenarios where sensor data exhibits variations. This research provides valuable insights for stakeholders in environmental monitoring, highlighting the potential of Normal Scan Statistics to enhance the reliability of IoT-generated air quality data.