Grants & Awards

The global economic impact of IoT Devices and Services is predicted to surpass 3D printing, autonomous vehicles, and advanced robotics from 2013 to 2025. ISSACs has organized key laboratories with a goal of applying science and engineering to societal problems through an interdisciplinary, collaborative lens. 

The following grants and awards have been awarded in this effort. 

Case Western Reserve University and Cleveland State University have received a $3.1 million, two-year grant from the Cleveland Foundation to further advance the Internet of Things Collaborative (IOTC), an initiative forged between both universities in 2017 to shape the Northeast Ohio region into a digital innovation leader.

The funding, approved by the Cleveland Foundation Board of Trustees, supports completing the establishment of two technology research hubs and expanding faculty engagement.

Read the full press announcement in The Daily for details.

The proposed research investigates in-network control to mitigate network congestion which remains the biggest challenge for High Performance Computing (HPC) processors. It will significantly improve the training efficiency of the existing distributed training frameworks, while providing insights to the algorithm and system co-design solutions.

The developed framework will also help students and researchers in their big data research projects. New courses will be developed based on the outcomes of the proposed work and new curriculum and training sessions on networking and distributed Machine Learning (ML) will be developed in High School Tech Camps during the summer.

For additional information on Dr. Wang's project, see the full NSF Award Abstract.

Existing solutions to create a secure and trustworthy data communication link have primarily focused on complicated energy-intensive digital encoding performed in the backend. While effective, this approach is inherently inconsistent with the low-energy nature of the wireless link. This project aims to address this challenge by developing low-energy radio frequency (RF) and analog security features that can be implemented in the RF front-end. The new knowledge gained from this research will form the foundation for low-power low-latency trusted wireless communications.

Dr. Lavasani will be working in collaboration with researchers from The Ohio State University.

For more information about this project, please see the full NSF Award Abstract.

The U.S. Department of Energy’s (DOE’s) Clean Energy Smart Manufacturing Innovation Institute (CESMII) announced selections for five projects to identify ways to use smart-manufacturing technologies to improve productivity, precision, performance and energy efficiency. Case Western Reserve University, under the leadership of ISSACS Faculty Director Dr. Kenneth Loparo, will partner with Rafter Equipment Corporation to develop a fault detection and predictive diagnostics system for tube mills and roll-forming manufacturing equipment using the CESMII Smart Manufacturing Innovation Platform (SMIP). The goal is to develop and demonstrate a mill monitoring system that will detect and diagnose faults, provide guidance on incipient faults and predictive diagnostics, maximize uptime and energy efficiency, and identify preventative maintenance actions.

By advancing artificial intelligence (AI) innovations, the goal of this project is to design and develop an AI-driven paradigm for collective and collaborative community resilience in response to a variety of crises and exposed vulnerabilities in the COVID-19 era and beyond. With additional validation, this research will provide a foundation to assist the federal and state governments, corporations, societal leaders to develop and implement strategies that will guide local and regional communities, and the nation into a successful new normal future.

The co-Principal Investigator for this project is Dr. Kenneth Loparo.

To learn more about their research, please review the NSF Award Abstract.

The COVID-19 pandemic has driven a tremendous growth in online activity, as individuals seek to avoid close contact with others. Bad actors have capitalized on the fear and profitability created by the virus to create COVID-19 themed malware.

This award supports Dr.Yanfang (Fanny) Ye's efforts to develop innovative techniques to combat the exponential growth of increasingly sophisticated COVID-19 themed malware so that users can be better protected in the cyberspace.

By advancing capabilities of artificial intelligence (AI), the goal of this project is to develop innovative links between AI and security to design and develop an integrated framework for COVID-19 themed malware detection to help mitigate its negative effects on public health, society, and the economy. The outcomes of this project (including open-source codes and generated benchmarks) will be made publicly available.

The project integrates research with education through innovative curriculum development, student mentoring activities, and broadening participation of underrepresented groups.

For details surrounding this important research, see the full NSF Award Abstract.

This Google Gift 2020 award supports Dr. Wang’s work related to "P4TM: Accurate Traffic Matrix Estimation with Programmable Switches.”

Per Dr. Wang, a traffic matrix is often used to represent the traffic volumes within certain time intervals between any origin/destination pairs in data center networks and is essential for the deployments of traffic engineering solutions. Traffic matrix estimation is not a trivial task since raw packets collected from network measurement mechanisms only provide source and destination information of each individual packet. Flexible aggregation of statistics might be needed for various network management tasks. In addition, traffic collectors might observe duplicate packets from different vantage points, leading to inaccurate estimation of traffic matrix. To address these challenges, we propose to build a distributed framework, called P4TM, that leverages the programmable network data plane to estimate traffic matrix. P4TM also utilizes the Map-Reduce programming model to improve the efficiency of matrix estimations.

A major global public health challenge surrounding COVID-19 prior to the development and distribution of a vaccine is the development of a set of actions that persons and communities can take to effect community mitigation of this respiratory virus infection by slowing its spread. 

The team led by Dr. Ye, along with co-Principal Investigator Dr. Kenneth Loparo, will design and develop an AI- and data-driven integrated framework to provide real-time hierarchical community-level risk assessment to help combat the COVID-19 pandemic.

Details on the research strategy can be found in the NSF Award abstract.

This planning grant supports a study of the feasibility of establishing a multi- university-industry Center for High-Assurance Secure Systems and the Internet-of-Things (CHASSI) that will focus on application areas where both security and high assurance are necessary to support operations of high mission criticality, due to safety or economic impact. Examples include medical devices, manufacturing, the energy grid, real-time financial markets, construction, and defense.

Continues funding to the IoT Collaborative, a joint effort between Case Western Reserve University, Cleveland State and the city of Cleveland, to catalyze efforts in the region to bring national research prominence to our region, strengthen our community and neighborhoods, and seize the $6.2 trillion economic opportunity of the Industrial Internet of Things (IoT).

Establishes the IoT Collaborative, a joint effort between Case Western Reserve University, Cleveland State and the city of Cleveland, to catalyze efforts in the region to bring national research prominence to our region, strengthen our community and neighborhoods, and seize the $6.2 trillion economic opportunity of the Industrial Internet of Things (IoT).