Driving Intelligent IoT Monitoring and Control through Cloud Computing
  and Machine Learning
Hanzhe Li Xiangxiang Wang Yuan Feng Yaqian Qi Jingxiao Tian
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.