Distributed real-time operational data analytics
- Main supervisor: Prof. Lars Kai Hansen, firstname.lastname@example.org (contact person)
- Co-supervisor: Prof. Xenofon Fafoutis, email@example.com
- Propose data analytics for Industry 4.0 that exploit the connectivity and data access features of the Fog Nodes.
- Implement a distributed real-time data analytics solution based on Machine-Learning-as-a-Service.
- Demonstrate the data analytics solution on an industrial use case.
- Distributed data representation and modeling, using the services provided by the Fog Nodes, which are close to the machines (robots, actuators, sensors).
- Distributed real-time machine learning algorithms that use the Fog Computing Platform.
- Implementation of the ML algorithms using ML-as-a-Service microservices.
- Evaluation of the approaches, and quantification of value gains obtained from the improved operations.
Planned visits and collaboration:
- University of Kaiserslautern: work on the data privacy issue under Federated Learning.
Jia Qian, Sarada Prasad Gochhayat, and Lars Kai Hansen. Distributed active learning strategies on edge computing. In 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) pages 221–226. IEEE, 2019. [pdf]
Jia Qian, Lars Kai Hansen, Xenofon Fafoutis, Prayag Tiwari, and Hari Mohan Pandey. Robustness analytics to data heterogeneity in edge computing. Computer Communications , 164:229–239, 2020. [pdf]
Jia Qian, Prayag Tiwari, Sarada Prasad Gochhayat, and Hari Mohan Pandey. A noble double-dictionary-based ecg compression technique for ioth. IEEE Internet of Things Journal, 7(10):10160–10170, 2020. [pdf]
Mohammadreza Barzegaran, Nitin Desai, Jia Qian, Koen Tange, Bahram Zarrin, Paul Pop, and Juha Kuusela. Fogification of electric drives: An industrial use case. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), volume 1, pages 77–84. IEEE, 2020. [pdf]
Mohammadreza Barzegaran, Nitin Desai, Jia Qian, and Paul Pop. Electric drives as fog nodes in a fog computing-based industrial use case. The Journal of Engineering, 2021. [pdf]
Prayag Tiwari, Jia Qian, Qiuchi Li, Benyou Wang, Deepak Gupta, Ashish Khanna, Joel JPC Rodrigues, and Victor Hugo C de Albuquerque. Detection of subtype blood cells using deep learning. Cognitive Systems Research, 52:1036–1044, 2018. [pdf]
Jia Qian, Hiba Nassar, and Lars Kai Hansen. Minimal conditions analysis of gradient-based reconstruction in federated learning (submitted). arXiv preprint arXiv:2010.15718 , 2020. [pdf]
Bebortta, S., Senapati, D., Rajput, N.K., Singh, A.K., Rathi, V.K., Pandey, H.M., Jaiswal, A.K., Qian, J. and Tiwari, P., 2020. Evidence of power-law behavior in cognitive IoT applications. Neural Computing and Applications, 32(20), pp.16043-16055.[pdf]
Jaiswal, A.K., Tiwari, P., Rathi, V.K., Qian, J., Pandey, H.M. and Albuquerque, V.H.C., 2020. Covidpen: A novel covid-19 detection model using chest x-rays and ct scans. Medrxiv. [pdf].
For the full publication list, please check Here.