Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study
In the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each ind...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/3/326 |
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| author | Mario E. Rivero-Angeles Víctor Barrera-Figueroa José E. Malfavón-Talavera Yunia V. García-Tejeda Izlian Y. Orea-Flores Omar Jiménez-Ramírez José A. Bermúdez-Sosa |
| author_facet | Mario E. Rivero-Angeles Víctor Barrera-Figueroa José E. Malfavón-Talavera Yunia V. García-Tejeda Izlian Y. Orea-Flores Omar Jiménez-Ramírez José A. Bermúdez-Sosa |
| author_sort | Mario E. Rivero-Angeles |
| collection | DOAJ |
| description | In the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each individual may be challenging. GPS tracking may not be available in many indoor cases; video surveillance may require expensive deployment (mainly due to the high-quality cameras and face recognition algorithms) and can be restrictive in case of low budget applications; RFID systems can be cumbersome and limited in the detection range. This information can later be used in many different scenarios. For instance, in case of earthquakes, fires, and accidents in general, the administration of the buildings can have a clear record of the people inside for victim searching activities. However, in the pandemic derived from the COVID-19 outbreak, a tracking that allows detecting of pedestrians in close range (a few meters) can be particularly useful to control the virus propagation. Hence, we propose a mobile clustering scheme where only a selected number of pedestrians (Cluster Heads) collect the information of the people around them (Cluster Members) in their trajectory inside the area of interest. Hence, a small number of transmissions are made to a control post, effectively limiting the collision probability and increasing the successful registration of people in close contact. Our proposal shows an increased success packet transmission probability and a reduced collision and idle slot probability, effectively improving the performance of the system compared to the case of direct transmissions from each node. |
| first_indexed | 2024-03-10T13:22:33Z |
| format | Article |
| id | doaj.art-a771914108544ffebac6b794c31a666b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:22:33Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a771914108544ffebac6b794c31a666b2023-11-21T09:56:57ZengMDPI AGEntropy1099-43002021-03-0123332610.3390/e23030326Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case StudyMario E. Rivero-Angeles0Víctor Barrera-Figueroa1José E. Malfavón-Talavera2Yunia V. García-Tejeda3Izlian Y. Orea-Flores4Omar Jiménez-Ramírez5José A. Bermúdez-Sosa6Communication Networks Laboratory, CIC-Instituto Politécnico Nacional, Mexico City 07738, MexicoSEPI-UPIITA-Instituto Politécnico Nacional, Mexico City 07740, MexicoTelematics Section, UPIITA-Instituto Politécnico Nacional, Mexico City 07738, MexicoBasic Sciences Section, UPIITA-Instituto Politécnico Nacional, Mexico City 07340, MexicoTelematics Section, UPIITA-Instituto Politécnico Nacional, Mexico City 07738, MexicoTelematics Section, UPIITA-Instituto Politécnico Nacional, Mexico City 07738, MexicoTelematics Section, UPIITA-Instituto Politécnico Nacional, Mexico City 07738, MexicoIn the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each individual may be challenging. GPS tracking may not be available in many indoor cases; video surveillance may require expensive deployment (mainly due to the high-quality cameras and face recognition algorithms) and can be restrictive in case of low budget applications; RFID systems can be cumbersome and limited in the detection range. This information can later be used in many different scenarios. For instance, in case of earthquakes, fires, and accidents in general, the administration of the buildings can have a clear record of the people inside for victim searching activities. However, in the pandemic derived from the COVID-19 outbreak, a tracking that allows detecting of pedestrians in close range (a few meters) can be particularly useful to control the virus propagation. Hence, we propose a mobile clustering scheme where only a selected number of pedestrians (Cluster Heads) collect the information of the people around them (Cluster Members) in their trajectory inside the area of interest. Hence, a small number of transmissions are made to a control post, effectively limiting the collision probability and increasing the successful registration of people in close contact. Our proposal shows an increased success packet transmission probability and a reduced collision and idle slot probability, effectively improving the performance of the system compared to the case of direct transmissions from each node.https://www.mdpi.com/1099-4300/23/3/326building accessmobile clustering schemetracking of pedestriansRFID systemscontrol of virus propagation |
| spellingShingle | Mario E. Rivero-Angeles Víctor Barrera-Figueroa José E. Malfavón-Talavera Yunia V. García-Tejeda Izlian Y. Orea-Flores Omar Jiménez-Ramírez José A. Bermúdez-Sosa Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study Entropy building access mobile clustering scheme tracking of pedestrians RFID systems control of virus propagation |
| title | Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study |
| title_full | Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study |
| title_fullStr | Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study |
| title_full_unstemmed | Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study |
| title_short | Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study |
| title_sort | mobile clustering scheme for pedestrian contact tracing the covid 19 case study |
| topic | building access mobile clustering scheme tracking of pedestrians RFID systems control of virus propagation |
| url | https://www.mdpi.com/1099-4300/23/3/326 |
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