TY - GEN
T1 - Distributed Consensus based COVID-19 Hotspot Density Estimation
AU - Achalla, Monalisa
AU - Muniraju, Gowtham
AU - Banavar, Mahesh K.
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas
AU - Schuckers, Stephanie
N1 - Funding Information:
The authors from Arizona State University and Clarkson University are funded by NSF Awards 2032114 and 2032106, respectively.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The primary focus of this work is an application of consensus and distributed algorithms to detect COVID-19 transmission hotspots and to assess the risks for infection. More specifically, we design consensus-based distributed strategies to estimate the size and density of COVID-19 hotspots. We assume every person has a mobile device and rely on data collected from the user devices, such as Bluetooth and WiFi, to detect transmission hotspots. To estimate the number of people in a specific outdoor geographic location and their proximity to each other, we first perform consensus-based distributed clustering to group people into sub-clusters and then estimate the number of users in a cluster. Our algorithm has been configured to work for indoor settings where we consider the signal attenuation due to walls and other obstructions, which are detected by using the Canny edge detection and Hough transforms on the floor maps of the indoor space. Our results on indoor and outdoor hotspot simulations consistently show an accurate estimate of the number of persons in a region.
AB - The primary focus of this work is an application of consensus and distributed algorithms to detect COVID-19 transmission hotspots and to assess the risks for infection. More specifically, we design consensus-based distributed strategies to estimate the size and density of COVID-19 hotspots. We assume every person has a mobile device and rely on data collected from the user devices, such as Bluetooth and WiFi, to detect transmission hotspots. To estimate the number of people in a specific outdoor geographic location and their proximity to each other, we first perform consensus-based distributed clustering to group people into sub-clusters and then estimate the number of users in a cluster. Our algorithm has been configured to work for indoor settings where we consider the signal attenuation due to walls and other obstructions, which are detected by using the Canny edge detection and Hough transforms on the floor maps of the indoor space. Our results on indoor and outdoor hotspot simulations consistently show an accurate estimate of the number of persons in a region.
KW - Consensus
KW - applications of ad-hoc networks
KW - density
KW - distributed estimation
KW - transmission hotspots
UR - http://www.scopus.com/inward/record.url?scp=85141067202&partnerID=8YFLogxK
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U2 - 10.1109/IISA56318.2022.9904371
DO - 10.1109/IISA56318.2022.9904371
M3 - Conference contribution
AN - SCOPUS:85141067202
T3 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
BT - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Y2 - 18 July 2022 through 20 July 2022
ER -