Mohammadreza (Reza) Barzegaran
I currently serve as a postdoctoral scholar at the Center for Pervasive Communications and Computing (CPCC) at the Department of Electrical Engineering and Computer Science of the University of California, Irvine (UCI) . My research interests are in the areas of Wired/Wireless Sensor Networks, Robotics, and Automation; and in developing enabling technologies for embedded disicion making systems, such as the Industrial Internet of Things (IIoT) and Industrial Edge/Fog Computing.
From 2021 to 2023, I held postdoctoral scholar positions at the Embedded Systems Engineering (ESE) section of the Department of Applied Mathematics and Computer Science of the Technical University of Denmark (DTU) where I was the director of several research projects and I ran an Indusry 4.0 demonstrator.
I hold a PhD in Computer Science from the Department of Applied Mathematics and Computer Science of the Technical University of Denmark . I was a Marie Sklodowska-Curie Ph.D. fellow in the European Union’s Horizon 2020 research and innovation program on the Fog Computing for Robotics and Industrial Automation (FORA) project. I also hold an MSc in Control and Dynamics of Systems from the University of Tehran , and an BSc in Aerospace Engineering from the Amirkabir University of Technology .
Contact Details
4213 Engineering Hall
University of California Irvine
Irvine, Califronia 92691, USA
Email:
barzegm1@uci.edu
and
reza@barzegaran.xyz
Web:
www.barzegaran.xyz
(redirects here)
Short CV
Education
- PhD in Computer Science, Technical University of Denmark 2021
- MSc in Control and Dynamics of Systems, University of Tehran 2015
- BSc in Aerospace Engineering, Amirkabir University of Technology 2013
Positions
- Postdoctoral Scholar, Center for Pervasive Communications and Computing (CPCC), University of California Irvine (2023 - now)
- R&D Software Engineer, SOPHION BIOSCIENCE A/S (2022 - 2023)
- Postdoctoral Scholar, DTU Compute, Technical University of Denmark (2021 - 2022)
- Ph.D Researcher, DTU Compute, Technical University of Denmark (2018 - 2021)
- Visiting Ph.D. researcher, DANFOSS A/S (2022)
- Visiting Ph.D. researcher, Distributed Systems Group, Technische Universitat Wien (TUWien) (2019)
- Automation Engineer, TAM Irankhodro (2015 - 2017)
- Teaching Assistant, Aerospace Engineering Department, Amirkabir University of Technology (2012 - 2015)
- Research Assistant, Amirkabir Research Center, Amirkabir University of Technology (2012 - 2015)
Major Research Project
Publications
| Preprints | Journals | Conferences | Thesis |
Preprints
- R. Debnath, L. Zaho, M. Barzegaran, and S. Steinhorst. CyclicSim: Comprehensive Evaluation of Cyclic Shapers in Time-Sensitive Networking. arXiv:2409.19792, 2024. [pdf]
- F. Jurado Lasso, M. Barzegaran, J.F. Jurado, and X. Fafoutis. ELISE: A Reinforcement Learning Framework to Optimize the Sloftframe Size of the TSCH Protocol in IoT Networks. Authorea Preprints, 2023. [pdf]
- M. Barzegaran, N. Reusch, L. Zhao, S.S. Craciunas, and P. Pop. Real-time Guarantees for Critical Traffic in IEEE 802.1 Qbv TSN Networks with Unscheduled and Unsynchronized End-Systems. arXiv:2105.01641, 2021. [pdf]
Peer-Reviewed Journal Papers
- F. Jurado Lasso, M. Barzegaran, J.F. Jurado, and X. Fafoutis. ELISE: A Reinforcement Learning Framework to Optimize the Slotframe Size of the TSCH Protocol in IoT Networks. IEEE Systems Journal, 2024. In Press. [pdf] [video]
- N. Reusch, M. Barzegaran, L. Zhao, S.S. Craciunas, and P. Pop. Configuration Optimization for Heterogeneous Time-Sensitive Networks. Real-Time Systems,No. 59, pp. 705–747, 2023. [pdf]
- M. Barzegaran and P. Paul. Extensibility-aware Fog Computing Platform Configuration for Mixed-Criticality Applications. Journal of Systems Architecture, pp. 102776, 2022. [pdf]
- P. Paul, B. Zarrin, M. Barzegaran, S. Schulte, S. Punnekkat, J. Ruh, and W. Steiner. The FORA Fog Computing Platform for Industrial IoT. Information Systems,No. 98, pp. 101727, 2021. [pdf]
- M. Barzegaran, and P. Paul. Communication Scheduling for Control Performance in TSN-based Fog Computing Platforms. IEEE Access, No. 9, pp. 50782–50797, 2021. [pdf]
- M. Barzegaran, N. Desai, J. Qian, and P. Paul. Electric Drives as Fog Nodes in a Fog Computing-based Industrial Use Case. The Journal of Engineering, No. 12, pp. 45–761, 2021. [pdf]
- M. Barzegaran, A. Cervin, and P. Paul. Performance Optimization of Control Applications on Fog Computing Platforms Using Scheduling and Isolation. IEEE Access, No. 8, pp. 104085–104098, 2020. [pdf]
- M. Barzegaran and A. Kosari. A Model of Flow Aeparation Controlled by Dielectric Barrier Discharge. Chinese Journal of Aeronautics, No. 30, pp. 660–1669, 2017. [pdf]
Peer-Reviewed Conference Papers
- M. Barzegaran and H. Jafarkhani. A Framework for Constrained Deployment Optimization of Wireless Mobile Sensor Networks. Proceedings of the IEEE Vehicular Technology Conference (VTC2024-Fall), pp. 1-6, 2024. [pdf]
- M. Barzegaran and P. Paul. The FORA European Training Network on Fog Computing for Robotics and Industrial Automation. Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2023. [pdf]
- M. Barzegaran, N. Reusch, L. Zhao, S.S. Craciunas, and P. Pop. Real-Time Traffic Guarantees in Heterogeneous Time-sensitive Networks. Proceedings of the International Conference on Real-Time Networks and Systems, pp. 46-57, 2022. [pdf]
- J. Qian and M. Barzegaran. A Decomposed Deep Training Solution for Fog Computing Platforms. Proceedings of IEEE/ACM Symposium on Edge Computing (SEC), pp. 423–431, 2021. [pdf]
- I. Murturi, M. Barzegaran, and S. Dustdar. A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks. Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CogMI), pp. 147–156, 2020. [pdf]
- M. Barzegaran, V. Karagiannis, C. Avasalcai, P. Pop, S. Schulte, and S. Dustdar. Towards Extensibility-Aware Scheduling of Industrial Applications on Fog Nodes. Proceedings of the International Conference on Edge Computing, pp. 67–75, 2020. [pdf]
- M. Barzegaran, N. Desai, J. Qian, K. Tange, B. Zarrin, P. Pop, and J. Kuusela. Fogification of Electric Drives: An Industrial Use Case. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1-5, 2020. [pdf]
- M. Barzegaran, B. Zarrin, and P. Pop. Quality-of-control-aware Scheduling of Communication in TSN-based Fog Computing Platforms Using Constraint Programming. Proceedings of the Workshop on Fog Computing and the IoT, pp. 1-4, 2020. [pdf]
- A. Cervin, P. Pazzaglia, M. Barzegaran, and R. Mahfouzi. Using Jittertime to Analyze Transient Performance in Adaptive and Reconfigurable Control Systems. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1025–1032, 2019. [pdf]
- M. Barzegaran, A. Cervin, and P. Pop. Towards Quality-of-Control-aware Scheduling of Industrial Applications on Fog computing Platforms. Proceedings of the Workshop on Fog Computing and the IoT (IoT-Fog), pp. 1-5, 2019. [pdf]
Thesis
- M. Barzegaran. Configuration Optimization of Fog Computing Platforms for Control Applications. PhD Thesis, Technical University of Denmark, Denmark, 2021. [pdf] [presentation] [video]
©
2024 Elsevier. The Elsevier preprint manuscripts are made available under the
CC-BY-NC-ND 4.0 license.
©
2024 IEEE. Personal use of IEEE material is permitted. Permission from IEEE must
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reprinting / republishing this material for advertising or promotional
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works.
COMING SOON
SERVICE
Journal Reviewer
- Ad Hoc Networks
- Computer Networks
- The Journal of Systems Architecture
- IEEE Transactions on Sustainable Computing
- IEEE Transactions on Embedded Computing Systems
- IEEE Communication Letters
- IEEE Systems Journal
- IEEE Transactions on Design Automation of Electronic Systems
- IEEE Open Journal of the Communications Society
- IEEE Transactions on Parallel and Distributed Systems
- IEEE Transactions on Industrial Informatics
- IEEE Transactions on Computers
- IEEE Transactions on Communication
- IEEE Intelligent Transportation Systems Magazine
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- The Journal of Internet Technology
- The Journal of Systems and Control Engineering
- The Journal of Engineering Manufacturing
- The Journal of Computing
Conference TPC Member / Reviewer
- IEEE International Conference on Communications (2024)
- IEEE/RSJ International Conference on Intelligent Robots and Systems (2024)
- Workshop on Real-time and Intelligent Edge Computing (2023)
- IEEE Real-Time and Embedded Technology and Applications Symposium, Brief Presentation (2022, 2023)
- Workshop on Web 3.0 and Decentralized Data Processing and Storage Platforms (2022)
- Vehicular Technology Conference (2020)
- International Conference on Factory Communication Systems (2020)
Research Supervision (Master / PhD)
- Rubi Debnath, Ph.D Thesis: "Run-time Time Critical Traffic Scheduling using Deep Learning", Technical University of Munich, ongoing
- Muhammed Numan INCE, Ph.D Thesis "PusOS: An Operating System Approach in Supervisor Mode for Fog Computing", AKDENİZ UNIVERSITY, 2024
- Emil Alexander Juul Hansen, Master Thesis: "Configuration of In-Car Computing Platforms for Autonomous Vehicle Applications", Technical University of Denmark, 2020
- Shahab Akbari, Master Thesis: "Routing algorithms for Large-Scale Multi-Domain IEEE 802.1 TSN Networks", Technical University of Denmark, 2020
- Niklas Reusch, Master Thesis: "Flexible Scheduling of Time-Triggered Traffic in Time-Sensitive Networking", Technical University of Denmark, 2019
- Shane Daniel McLean, Master Thesis: "Mapping and Scheduling of Real-Time Tasks on Multi-Core Autonomous Driving Platforms", Technical University of Denmark, 2019
Research projects
| Robot Deployment Framework | PusOS | FORA |
Robot Deployment Framework: Constrained Dynamic Deployment of Sensor Networks
-
This study focuses on developing a framework for constrained optimization of dynamic robot deployment.
We have identified various realistic robot deployment constraints , including but not limited to energy constraints, connectivity constraints, collision constraints, obstacle constraints, regulatory constraints, and maneuverability constraints.
Our framework utilizes Bézier curves to model robot trajectories.
Bézier curves are parametric curves that define robot trajectories with their parameters.
They facilitate the prediction of robot states over a time horizon by sampling the curves as inputs to the robot's dynamics.
Additionaly, constraints can be imposed directly on the curve geometry, embedding them into the curve formulation.
This framework is proposed in "A Framework for Constrained Deployment Optimization of Wireless Mobile Sensor Networks".
This
web application
demonstrates the embedded formulation.
We also extended this framwork and proposed DisCo-Guide algorithm.
This algorithm is decentralized and optimizes the sensing performance of drone swarms. The algorithm utilizes Gradient Ascent method to search and find the optimal deployment.
DisCo-Guide decides on the drone trajtories under realistic constraints and renders the drone dynamics to avoid unsafe manouvers.
This
video
presents some concepts used in this study.
This
video
presents evaluation on additional test cases .
The results of the study are under review for publication.
The relevant code is available per request at
Github.
PusOS: An Operating System Approach in Supervisor Mode for Fog Computing
-
Fog computing platforms are architectural means to realize Industry 4.0 and have recently received a lot of attention
in research communities. This study proposes a tailored operating system for such platforms, which accelerates the
realization of this vision and contributes to this research field. The study dentifies the requirements for Fog Computing platforms, designs
the necessary building blocks to address these requirements, and tailors the architectures based on these building
blocks. This study's output is PusOS which is available per request at
Github.
The study also performs experiments to evaluate the key performance indicators related to the identified
requirements, see this
video.
FORA: Fog Computing for Robotics and Industrial Automation
-
FORA
was a European Training Network (ETN), which funded and trained 15 PhD candidates in the area of Fog Computing, during the period 2017-2021. Fog Computing, also sometimes called Edge Computing, brings the Cloud “closer to the ground”, to the edge of the network. FORA was an interdisciplinary, international, intersectoral network that trained the next generation of researchers in Fog Computing with applicability to industrial automation and manufacturing.