TY - GEN
T1 - Ensemble of Semi-Parametric Models for IoT Fog Modeling
AU - Jan, Tony
AU - Iranmanesh, Saeid
AU - Sajeev, A. S.M.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - IoT fog resource optimization
KW - IoT real time applications.
KW - machine learning for resource optimization
UR - http://www.scopus.com/inward/record.url?scp=85080960476&partnerID=8YFLogxK
U2 - 10.1109/SSCI44817.2019.9003089
DO - 10.1109/SSCI44817.2019.9003089
M3 - Conference contribution
AN - SCOPUS:85080960476
T3 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
SP - 2995
EP - 2998
BT - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Y2 - 6 December 2019 through 9 December 2019
ER -