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

合作研究：CISE-MSI：DP：CNS：具有边缘-云交互的智能医疗系统的高效数据通信和处理

• 批准号: 2219742
• 负责人: Sachin Shetty
• 金额: $ 24万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219742&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).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. 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.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。医疗行业的数字化转型计划由虚拟访问，远程患者监控和消费者可穿戴设备的需求驱动。该计划的执行将产生大量不同的数据工件，需要对这些数据工件进行分析，以便随时随地为患者提供护理。对COVID-19的应对也有助于发展全面和智能的医疗保健能力。传统上，云计算技术已被医疗保健提供商用于分析大量的健康数据。然而，该技术存在诸如依赖于中央仲裁器、网络延迟增加和高吞吐量等缺点。此外，缺乏可信的人工智能/机器学习（AI/ML）算法，这可能导致错误的结果，从而对医疗保健领域造成灾难性影响，并增加对云基础设施的安全攻击频率。该项目旨在开发一个边缘云交互平台，以实现智能医疗系统的高效数据通信和处理。该平台利用软件定义的5G和支持AI的分布式边缘云技术，确保关键健康数据得到安全管理，并以响应方式提供。 第一步是开发一个SDN驱动的架构，在边缘设备上对医疗数据进行分类，以实现实时服务交付。接下来，该项目打算开发基于AI/ML算法的模型，以识别患者的潜在医疗状况。通过计算从中央服务器到任何医疗保健单位的本地数据库的本地数据访问时间并执行比较时间分析来验证模型。 此外，还将研究边缘和云之间的相互作用，以支持医疗系统对实时应用的需求。该项目将为来自AI、SDN、云和边缘技术交叉领域的代表性不足的社区的学生提供研究经验，并支持Meharry医学院课程的现代化。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。