TY - CHAP
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 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
PY - 2022/1/1
Y1 - 2022/1/1
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 Wi-Fi, 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 subclusters 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 people 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 Wi-Fi, 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 subclusters 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 people in a region.
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U2 - 10.1007/978-981-19-1356-3_9
DO - 10.1007/978-981-19-1356-3_9
M3 - Chapter
AN - SCOPUS:85161133795
SN - 9789811913556
SP - 127
EP - 147
BT - Studies to Combat COVID-19 using Science and Engineering
PB - Springer Nature
ER -