@Article{cmes.2022.020791,
AUTHOR = {Naeem Ahmed Nawaz, Norah Saleh Alghamdi, Hanen Karamti, Mohammad Ayoub Khan},
TITLE = {An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {133},
YEAR = {2022},
NUMBER = {2},
PAGES = {327--350},
URL = {http://www.techscience.com/CMES/v133n2/48966},
ISSN = {1526-1506},
ABSTRACT = {Assemblage at public places for religious or sports events has become an integral part of our lives. These gatherings
pose a challenge at places where fast crowd verification with social distancing (SD) is required, especially during a
pandemic. Presently, verification of crowds is carried out in the form of a queue that increases waiting time resulting
in congestion, stampede, and the spread of diseases. This article proposes a cluster verification model (CVM) using
a wireless sensor network (WSN), single cluster approach (SCA), and split cluster approach (SpCA) to solve the
aforementioned problem for pandemic cases. We show that SD, cluster approaches, and verification by WSN can
overcome the management issues by optimizing the cluster size and verification time. Hence, our proposed method
minimizes the chances of spreading diseases and stampedes in large events such as a pilgrimage. We consider the
assembly points in the annual pilgrimage to Makkah Al-Mukarmah and Umrah for verification using Contiki/Cooja
tool. We compute results such as verified cluster members (CMs) to define cluster size, success rate to determine the
best success rate, and verification time to determine the optimal verification time for various scenarios. We validate
our model by comparing the results of each approach with the existing model. Our results show that the SpCA with
SD is the best approach with a 96% success rate and optimization of verification time as compared to SCA with SD
and the existing model.},
DOI = {10.32604/cmes.2022.020791}
}