Clarkson and Arizona State University Develop Algorithm to Assess Areas of Concern
Discovering COVID-19 Hot Zones
We have all had to learn new ways to navigate the world in the age of COVID-19. For many, just deciding to leave home for essential needs has become a more thoughtful and planned process. But as we slowly resume a sense of normalcy and get back into our routines, could we be entering viral hotspots? Researchers from Clarkson are working with their counterparts from Arizona State University (ASU) to empower communities with this knowledge. Leading the project is Mahesh Banavar, associate professor of electrical and computer engineering. Stephanie Schuckers, professor and director of the Center for Identification Technology Research (CITeR) joins Banavar in collaborating with ASU researchers Andreas Spanias and Cihan Tepedelenlioglu to develop the technology that will use data collected from cell phone towers, WiFi and Bluetooth traces 22 / 2020 PRESIDENT’S REPORT / ALUMNI MAGAZINE
and a specialized algorithm to help detect potential COVID-19 hotspots. The algorithm will then alert users of potential dangers. The collaboration brings together the capabilities of two IndustryUniversity Cooperative Research Centers (IUCRC): CITeR at Clarkson and the Sensor Signal and Information Processing (SenSIP) Center at ASU. The National Science Foundation awarded the inter-university team close to $200,000 in Rapid Response Research funding, also known as a RAPID grant. The NSF awards RAPID grants to projects deemed urgent, including quick-response research on natural disasters or unanticipated events like the COVID-19 pandemic. Banavar and Schuckers have been developing signal localization software platforms. Similarly, their counterparts at ASU have been working on sensor technologies that incorporate node counting and network size estimation using consensus-based methods. These patented methods have several applications, including
cellphone network area estimation. The COVID-19 Hotspot Network Size and Node Counting Using Consensus Estimation project will use advanced consensus-based methods for estimating network area/size, node locations and node counts in a network based on minimal transmit-receive data. Studies show that COVID-19 can spread in places where many people are congregated in close proximity. Using the assumption that several appconnected smartphones in a given area corresponds to more people gathering, the algorithm can help assess the increased potential for viral spread. In practical terms, an application on a user’s phone will estimate their location, using traces such as WiFi, Bluetooth and cell-tower data, and also evaluate other non-personal information to determine if certain areas have a large number of people in close proximity. Nearby users will be lumped together to form an ad hoc network, allowing the algorithm to limit data collection from devices in this specific user group.
From left: Mahesh Banavar, Stephanie Schuckers, Andreas Spanias and Cihan Tepedelenlioglu are working under an NSF RAPID grant to develop data collection technology to detect COVID danger zones.
Other users in the same network who are also running the mobile application will transmit similar data to the algorithm, which will refine the estimates of network size, number of users in the area covered by the network and network location. With this information, the app would send an alert to a smartphone user who is about to enter a densely populated risk area. Consumers are becoming increasingly wary about access to their personal data, so the research team has put user privacy at the forefront. Each user will choose how much data they transmit in a tier-based system. For one set of users, only basic location information is gleaned from a
ping on a cell tower or open Bluetooth and WiFi access points, letting the algorithm know approximately where they are without using GPS. Other users can give more permissions to allow mild sharing of some additional information, including precise location but not the user’s identity or personal information. Obtaining more data will improve the algorithm performance and provide refined location and network information. The team hopes to have the algorithm in the testing phase by January 2021. When rolled out to the public, the application will be available for any smartphone type. The data parsed by the algorithm
will also be available on the web. Implications for the Clarkson-ASU team’s solution go far beyond the user level. Businesses in or nearby hot zones will be able to use the technology to decide when and how to safely open. Beyond COVID-19, communities and relief agencies can use the data from the algorithm during natural disasters to determine where to send resources. While nothing short of a cure or a vaccine for COVID-19 will subdue some people’s concern about the virus, rapid response research like this innovative effort may offer peace of mind by providing smartphone users information about highly populated areas. CLARKSON UNIVERSITY / 23