Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

作     者：Yuan Ai Ho Chee Keong Tan Yin Hoe Ng 

作者机构：School of Information TechnologyMonash University MalaysiaJalan Lagoon SelatanBandar Sunway47500Selangor Darul EhsanMalaysia Faculty of EngineeringMultimedia UniversityPersiaran MultimediaCyberjaya63100Selangor Darul EhsanMalaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：907-924页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by Ministry of Education Malaysia,Grant Number FRGS/1/2019/ICT02/MMU/02/1 in part by Monash Malaysia,School of Information Technology(SIT)Collaborative Research Seed Grants 2020 

主　　题：Indoor location analytics COVID-19 contact tracing social distancing spatial-temporal dimensions human mobility 

摘      要：The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location *** on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of *** the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing *** specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor *** feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the *** network of users is used to model an optimization problem to manage the human mobility on a *** objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on *** simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering *** adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.