Sensor Profiling and Automated Quality Checks on Sensor Data

Sensor data has increased at an unheard-of rate as a result of the widespread use of sensor-based technologies. However, trust and utility depend on the quality of the data. In order to address these issues, this paper investigates sensor profiling and automated quality checks.Issues with sensor data quality, such as hardware flaws and signal noise, are complex. In this research work, a framework is proposed for characterising sensor behaviours, real-time anomaly detection, and corrective actions that integrates machine learning, signal processing, and data analytics.Various predictive models are reviewed throughout this paper along with cutting-edge methods for improving data quality. The objective is to increase the sensor data’s dependability, allowing for more precise decision-making. In the age of IoT and big data analytics, these approaches show promise for a variety of applications. This research contributes to the discourse on sensor data quality, offering practical solutions for sensor data challenges across various domains.