TY - JOUR
T1 - A Crowd-Based Explosive Detection System with Two-Level Feedback Sensor Calibration
AU - Yang, Chengmo
AU - Cronin, Patrick
AU - Agambayev, Agamyrat
AU - Ozev, Sule
AU - Cetin, A. Enis
AU - Orailoglu, Alex
N1 - Funding Information:
The work is supported by National Science Foundation under grant No. 1739390, 1739396, 1739451, and 1739684.
Publisher Copyright:
© 2020 Association on Computer Machinery.
PY - 2020/11/2
Y1 - 2020/11/2
N2 - Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators and in some cases have open admission. As it is impracticable to guarantee the subjection of every event-goer to a security screening, we propose a crowd-based explosive detection system that uses a multitude of low-cost ChemFET sensors which are distributed to attendees. As the sensors offer limited accuracy, we further propose a server-based decision-making framework that utilizes a two-level feedback loop between the sensors and the server and explores spatial and temporal locality of the collected data to overcome the inherent low-accuracy of individual sensors. We thoroughly explore two distinct detection schemes, stressing their performance under a myriad of conditions, thus showing that such a crowd-based detection system comprised of low-cost and low-accuracy sensors can deliver high detection accuracy with minimal false positives.
AB - Large, open, public events, such as marathons and festivals, have always presented a unique safety challenge. These sprawling events, which can take up entire city blocks or stretch for many miles, can draw tens to hundreds of thousands of spectators and in some cases have open admission. As it is impracticable to guarantee the subjection of every event-goer to a security screening, we propose a crowd-based explosive detection system that uses a multitude of low-cost ChemFET sensors which are distributed to attendees. As the sensors offer limited accuracy, we further propose a server-based decision-making framework that utilizes a two-level feedback loop between the sensors and the server and explores spatial and temporal locality of the collected data to overcome the inherent low-accuracy of individual sensors. We thoroughly explore two distinct detection schemes, stressing their performance under a myriad of conditions, thus showing that such a crowd-based detection system comprised of low-cost and low-accuracy sensors can deliver high detection accuracy with minimal false positives.
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U2 - 10.1145/3400302.3415670
DO - 10.1145/3400302.3415670
M3 - Conference article
AN - SCOPUS:85097920545
SN - 1092-3152
VL - 2020-November
JO - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
JF - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
M1 - 9256458
T2 - 39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020
Y2 - 2 November 2020 through 5 November 2020
ER -