Cloud-Based Machine Learning for Sensor Data Classification: A Comprehensive Study

Abstract

Large volumes of real-time sensor data are being generated across various industries, including healthcare, smart cities, agriculture, and industrial systems, due to the widespread use of Internet of Things (IoT) devices and sensor networks. However, efficiently processing and classifying this massive volume of data remains a significant challenge due to the complexity and high velocity of sensor streams. Cloud-based machine learning (ML) offers an ideal solution to manage, analyze, and classify these large-scale sensor datasets. This paper presents a comprehensive study on cloud-based ML for sensor data classification, highlighting its methodologies, tools, and applications. It examines essential machine learning methods for sensor data classification applications, including supervised, unsupervised, and deep learning. Scalable, real-time sensor data processing is made possible by combining cloud computing and machine learning, providing effective anomaly detection solutions, predictive maintenance, and optimization in diverse applications.

Furthermore, the difficulties in this field are covered in the study, including scalability, privacy, latency, and data quality. It also explores the role of cloud platforms such as AWS, Google Cloud, and Microsoft Azure in supporting sensor data classification tasks. Finally, this study outlines future research directions to enhance cloud-based ML models for sensor data classification, addressing the increasing complexity and volume of sensor data in the IoT ecosystem.