MRI: Acquisition and Development of Mobile Edge Computing Equipment for Research and Education of Big Data
Analytics with Applications in Smart Grid at PVAMU (supported by NSF)

Project Number: CNS-2018945
Duration: Sep 2020 – Aug 2024
PI: Lijun Qian; Co-PI: John Attia, Xishuang Dong, John Fuller, Shuza Binzaid

Department of Electrical & Computer Engineering
Roy G. Perry College of Engineering
Prairie View A&M University
Prairie View, TX 77446

Project Summary:

With pervasive interconnected smart objects operating together, a huge amount of data has been generated that needs to be processed in an efficient and timely fashion. Mobile edge computing is promising to bring computing closer to data. This MRI project will provide much-needed equipment for research in big data analytics through mobile edge computing of massive Internet-of-Things (IoT) data, and explore its applications in a smart microgrid. This project will leverage the complementary expertise in two research centers of big data and smart grid at Prairie View A&M University (PVAMU) and boost the research and education in the areas of big data science, distributed machine learning, mobile edge computing, and smart power grid. If successful, this project will provide near real-time analysis and processing of massive IoT data, and foster the digital transformation of smart grid. Furthermore, this project will involve a team of researchers from PVAMU, a Historically Black College and University (HBCU), to carry out research and education activities to engage more students, especially underrepresented minority students in research and provide research training. The acquisition and development of the testbeds will allow PVAMU researchers to further enhance the existing research, perform experiments and testing in the areas of big data analytics, edge computing, and smart micro-grid, and train students to become highly-skilled future workforce, which is extremely important to the nation.

Smart grid has emerged as the Internet of power supply, where a large number of IoT devices with measurement and control capability will be deployed to monitor the status of the power grid, and the collected data will allow us to better manage and control power generation, transmission, and distribution. However, a systematic study of design and deployment in a distributed environment for IoT-supported smart grids must be carried out to achieve the Internet-of-Energy vision. Although there are several simulation studies and software-in-the-loop emulators, a real-world testbed is lacking for experiments, testing, and validation. To address these challenges, a team of researchers from two research centers, the big data research center (CREDIT center) and the smart microgrid research center (SMART center) at PVAMU will acquire mobile edge computing equipment and smart grid monitoring and data collection devices using the NSF MRI mechanism, and develop a real-world testbed for big data analytics using edge computing in smart grid. The multidisciplinary team will leverage their existing research capacities and use the testbeds for more effective and efficient big data processing and predictive analysis in the smart grid via mobile edge computing. The acquired equipment and testbed will establish a unique research capability at PVAMU, an HBCU. It will advance the research in mobile edge computing and big data analytics for mission-critical applications and smart grid modernization. It will also help validate the theoretical results in many current studies. It will greatly strengthen and broaden big data and smart grid research activity at PVAMU and across disciplines, complementing the existing research portfolio of the two research centers at PVAMU. Furthermore, the team is committed to making the proposed testbed available to the research community at large.

Results:

I. Publications

This material is based upon work supported by the National Science Foundation under Grant No. 2018945.
Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Journal papers:

  • Olatunde A. Adeoye, Samir I. Abood, John H. Fuller, and John O. Attia (2022). “Comparisons Between Distributed Power Flow Controller (DPFC) and Unified Power Flow Controller (UPFC),” London Journal of Engineering Research, Volume 22 | Issue 9 | Compilation 1.0, 2022.
  • Fagbohungbe, Omobayode and Reza, Sheikh Rufsan and Dong, Xishuang and Qian, Lijun “Efficient Privacy Preserving Edge Intelligent Computing Framework for Image Classification in IoT”, IEEE Transactions on Emerging Topics in Computational Intelligence , v.6 , 2022.
  • Shamim, Nabila and Binzaid, Shuza and Gabitto, Jorge Federico and Attia, John Okyere “A Combined Chemical-Electrochemical Process to Capture CO2 and Produce Hydrogen and Electricity”, Energies, v.14, 2021.

Books

  • John Fuller, Pamela Obiomon, Samir I. Abood, Power System Operation, Utilization,  and Control, 1st Edition, Publisher: CRC Press, Taylor & Francis Group, 2022.

Conference papers:

  • Faisal A. Ahmed, Samir Abood and John Attia, “Smart Home Monitoring System for Reduced Power Usage.” 2023 ASEE GSW Conference Proceedings, March 2023.
  • Bob Patthammavong, Samir Abood, and John Attia, “Smart Home Warning System for a Safe Environment.”  2023 ASEE GSW Conference Proceedings, March 2023.
  • K.M Kabir, S. Binzaid, “Portable Solar-Powered Smart System for Reverse Osmosis Process of Drinkable Rainwater”, IEEE Global Energy Conference 2022, Turkey, pp. 1-5, October 2022.
  • K.M Kabir, S. Binzaid, and J.O. Attia “Design and Implementation of a Sustainable Energy Generating Pad for Lightweight Transportation,” IEEE Global Energy Conference 2022, Turkey, pp. 1-4, October 2022.

II. Test Bed

III. Experimental Results

Coming soon.

Contact information:

Office: NENR Building Room 332
Phone: (936) 261-9908
Fax: (936) 261-9930
E-mail: Lijun Qian

Last Modified: August 2023