Ensemble Meteorological Cloud Classification Meets Internet of Dependable and Controllable Things

Jinglin Zhang, Pu Liu, Feng Zhang, Hironobu Iwabuchi, Antonio Artur De H.E.Ayres De Moura, Victor Hugo C. De Albuquerque

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Advances in Internet of Things (IoT) and cloud/edge computing systems could precisely monitor the meteorological elements and environmental conditions. Remote automated observation system (RAOS) makes the full use of IoT to communicate with other sensors, enabling the active responses from passive devices for smart weather. Cloud observation and classification have been regarded as a successful application that could automatically perform emergency tasks in RAOS. However, with the increasing growth of resource exploitation, the performance of communications among the automatic observation platforms, and the efficiency of task allocation among them has become a critical challenge. In this article, an ensemble learning method and resource allocation scheme are proposed to realize the cloud observation and classification with the help of reliable and controllable infrastructures. On the one hand, several ensemble methods, like Bagging, AdaBoost, and Snapshot are selected as a base classifier to capture the cross-semantic and structure features of cloud, while applying them to the ensemble using convolutional neural networks with different base learners and residual neural networks with different depths. on the other hand, a particular cloud-edge distributed framework is proposed for cloud classification approach based on the intelligent network, to overcome the difficulty in the massive data transmission. The experimental results verify that the proposed ensemble approach achieves high accuracy of cloud classification, and effectively improves the number of allocated tasks. Ensemble methods can generate a more accurate prediction than any single classifier or the majority algorithms. It consistently yields lower error rates than single state-of-the-art models at no additional training cost.

Original languageEnglish
Article number9286534
Pages (from-to)3323-3330
Number of pages8
JournalIEEE Internet of Things Journal
Issue number5
Publication statusPublished - 2021 Mar 1


  • Cirrus cumulus stratus nimbus data set
  • convolutional neural networks
  • dependable and controllable things
  • ensemble learning (EL)
  • meteorological cloud classification


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