Quality-of-Things Based Machine Learning for the MIoT Applications
ORCID
- Shaymaa Al-Juboori: 0000-0001-5175-736X
- Lingfen Sun: 0000-0002-9921-2817
Abstract
Enhancing the Quality of Things (QoT) is urgently needed given the rapid evolution of the Multimedia Internet of Things (MIoT). One of the challenges with MIoT is Acceptable QoT. Achieving AQoT can optimize bandwidth and storage at a level that will satisfy MIoT application’s minimal requirements. Intelligent systems using Machine Learning (ML) techniques can improve the performance of MIoT applications by keeping the minimum requirements of the resources which are necessary to maintain AQoT. The aim of this study is to develop a MIoT system based on ML to provide high performance with an acceptable QoT. The Gaussian-Naive Bayes, Fine KNN, and AdaBoost ML algorithms were investigated against different video sequences with varying bitrates and network conditions. The results based on face recognition for a Ring Video Doorbell scenario showed that ML could be used on MIoT applications to achieve AQoT and significantly reduce bandwidth and storage usage.
Publication Date
2023-01-01
Recommended Citation
Al-Juboori, S., Al-Nuaimi, A., Karaadi, A., Mkwawa, I., Zhang, J., & Sun, L. (2023) 'Quality-of-Things Based Machine Learning for the MIoT Applications', Available at: 10.1109/ICASSPW59220.2023.10192929" >https://doi.org/10.1109/ICASSPW59220.2023.10192929
