Improving Accuracy of Sensor Data by Frequent Pattern Mining Algorithm Using Edge Computing

Abstract

Sensor data plays a crucial role in various applications, including industrial automation, environmental monitoring, and healthcare. However, the accuracy of sensor data can be adversely affected by factors such as noise, latency, and data transmission issues in existing systems. This study focuses on identifying the disadvantages associated with current sensor data collection and analysis methods and explores the use of frequent pattern mining to enhance data accuracy. The research presents a comprehensive overview of Edge computing in conjunction with sensor systems and the Internet of Things, highlighting the complexities in processing sensor data using conventional methods and the advantages of employing frequent pattern mining. The study concludes that the utilization of frequent pattern mining in edge sensor data processing offers optimized response time, resource utilization, and better scalability. It is also capable of handling the massive amount of data generated from sensors and mobile devices in the Internet of Things.