Collaborative Research: CISE-MSI: DP: CNS: Efficient Data Communication and Processing for Intelligent Medical Systems with Edge-Cloud Interplay

Information

  • NSF Award
  • 2219741
Owner
  • Award Id
    2219741
  • Award Effective Date
    9/1/2022 - 2 years ago
  • Award Expiration Date
    8/31/2025 - 7 months from now
  • Award Amount
    $ 360,000.00
  • Award Instrument
    Standard Grant

Collaborative Research: CISE-MSI: DP: CNS: Efficient Data Communication and Processing for Intelligent Medical Systems with Edge-Cloud Interplay

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>The healthcare industry’s digital transformation initiative is driven by the need for virtual visits, remote patient monitoring, and consumer wearables. The execution of this initiative will generate vast and diverse data artifacts that need to be analyzed to deliver care to patients anywhere and anytime. The response to COVID-19 has also contributed to developing a holistic and intelligent healthcare capability. Traditionally, cloud computing technology has been employed by healthcare providers for analyzing vast health data. However, the technology suffers from drawbacks such as dependency on a central arbitrator, increase in network latency, and high throughput. In addition, there is a lack of trusted Artificial Intelligent/ Machine Learning (AI/ML) algorithms which can lead to erroneous results causing catastrophic impacts in the healthcare domain and increasing the frequency of security attacks on the cloud infrastructure. <br/><br/>This project aims to develop an edge-cloud interplay platform to realize efficient data communication and processing for Intelligent Medical Systems. The platform leverages software-defined 5G and AI-enabled distributed edge-cloud technologies to ensure critical health data is managed securely and made available in a responsive manner. The first step is to develop an SDN-driven architecture to classify healthcare data at the edge devices for real-time service delivery. Next, the project intends to develop models based on AI/ML algorithms to identify patients’ potential medical conditions. The models are to be validated by calculating the access time of the local data from the central server to the local database in any healthcare unit and performing a comparative time analysis. Besides, the interplay between the edge and cloud will be investigated to support the demand for real-time applications for healthcare systems. The project will provide research experiences to students from underrepresented communities in areas at the intersection of AI, SDN, cloud, and edge technologies and also support the modernization of the curriculum at Meharry Medical College.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Subrata Acharyaacharyas@nsf.gov7032922451
  • Min Amd Letter Date
    7/29/2022 - 2 years ago
  • Max Amd Letter Date
    7/29/2022 - 2 years ago
  • ARRA Amount

Institutions

  • Name
    Meharry Medical College
  • City
    NASHVILLE
  • State
    TN
  • Country
    United States
  • Address
    1005 DR DB TODD JR BLVD
  • Postal Code
    372083501
  • Phone Number
    6153276738

Investigators

  • First Name
    Uttam
  • Last Name
    Ghosh
  • Email Address
    ughosh@mmc.edu
  • Start Date
    7/29/2022 12:00:00 AM

Program Element

  • Text
    CISE MSI Research Expansion

Program Reference

  • Text
    COVID-Disproportionate Impcts Inst-Indiv