Ensemble of Semi-Parametric Models for IoT Fog Modeling

Tony Jan, Saeid Iranmanesh, A. S.M. Sajeev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes an innovative machine learning algorithm for resource optimization in IoT fog network. The proposed model utilizes distributed semi-supervised learning with innovative ensemble learning for efficient resource optimization in the IoT fog network for improved availability and readiness. The proposed model shows a great potential for real-time IoT applications utilizing the efficient fog resource optimization. The proposed model is evaluated against other state-of the-art models using the benchmark data to demonstrate its readiness and usefulness in real-time mission critical IoT applications such as in unmanned vehicle control system. The proposed model shows an acceptable resource optimization performance with reasonable computational complexity which proves to be useful in real-time IoT applications.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2995-2998
Number of pages4
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/TerritoryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • IoT fog resource optimization
  • IoT real time applications.
  • machine learning for resource optimization

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