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A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications
Razin Farhan Hussain, Mohsen Amini Salehi
Deep neural network (DNN) models are effective solutions for industry 4.0 applications (\eg oil spill detection, fire detection, anomaly detection). However, training a DNN network…
Resource Allocation of Industry 4.0 Micro-Service Applications across Serverless Fog Federation
Razin Farhan Hussain, Mohsen Amini Salehi
The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at…
Federated Fog Computing for Remote Industry 4.0 Applications
Razin Farhan Hussain, Mohsen Amini Salehi
Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 late…
Analyzing the Performance of Smart Industry 4.0 Applications on Cloud Computing Systems
Razin Farhan Hussain, Alireza Pakravan, Mohsen Amini Salehi
Cloud-based Deep Neural Network (DNN) applications that make latency-sensitive inference are becoming an indispensable part of Industry 4.0. Due to the multi-tenancy and resource h…
Serverless Edge Computing for Green Oil and Gas Industry
Razin Farhan Hussain, Mohsen Amini Salehi, Omid Semiari
Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-…
Robust Resource Allocation Using Edge Computing for Vehicle to Infrastructure (V2I) Networks
Anna Kovalenko, Razin Farhan Hussain, Omid Semiari +1
Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-…