Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning

TL;DR

This paper discusses how integrating cloud and edge computing with machine learning enhances IoT monitoring and control, reducing latency, improving efficiency, and enabling intelligent decision-making across various industries.

Contribution

It introduces a comprehensive framework combining IoT, edge computing, and machine learning for improved monitoring and control, with practical case studies demonstrating its effectiveness.

Findings

Edge computing reduces latency in IoT systems.

Machine learning improves fault detection accuracy.

Practical applications show enhanced system efficiency.

Abstract

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies.