Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.2/13525 |
Resumo: | The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%. |
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Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fireMulti-agent systemsRecommender systemsContext-based recommender systemsIoT—Internet of ThingsFire building evacuationOntologiesOccupant behavior conditioningBuilding occupant guidanceThe evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.Repositório AbertoNeto, JoaquimMorais, A. JorgeGonçalves, Ramiro Manuel Ramos MoreiraCoelho, António Leça2023-03-23T15:27:00Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.2/13525eng10.3390/electronics11213466info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T15:45:32Zoai:repositorioaberto.uab.pt:10400.2/13525Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:52:32.949992Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
title |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
spellingShingle |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire Neto, Joaquim Multi-agent systems Recommender systems Context-based recommender systems IoT—Internet of Things Fire building evacuation Ontologies Occupant behavior conditioning Building occupant guidance |
title_short |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
title_full |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
title_fullStr |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
title_full_unstemmed |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
title_sort |
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire |
author |
Neto, Joaquim |
author_facet |
Neto, Joaquim Morais, A. Jorge Gonçalves, Ramiro Manuel Ramos Moreira Coelho, António Leça |
author_role |
author |
author2 |
Morais, A. Jorge Gonçalves, Ramiro Manuel Ramos Moreira Coelho, António Leça |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Aberto |
dc.contributor.author.fl_str_mv |
Neto, Joaquim Morais, A. Jorge Gonçalves, Ramiro Manuel Ramos Moreira Coelho, António Leça |
dc.subject.por.fl_str_mv |
Multi-agent systems Recommender systems Context-based recommender systems IoT—Internet of Things Fire building evacuation Ontologies Occupant behavior conditioning Building occupant guidance |
topic |
Multi-agent systems Recommender systems Context-based recommender systems IoT—Internet of Things Fire building evacuation Ontologies Occupant behavior conditioning Building occupant guidance |
description |
The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-03-23T15:27:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.2/13525 |
url |
http://hdl.handle.net/10400.2/13525 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.3390/electronics11213466 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135119183708160 |