Digital Twins and Artificial Collective Intelligence: Synergies for the Future

Digital twins (DTs) and artificial collective intelligence (ACI) are transformative technologies that, when combined, hold significant potential for managing complex systems across diverse domains, such as smart cities, health care, and manufacturing. DTs encompass both physical objects and their vi...

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Detalles Bibliográficos
Autores: Pretel Fernández, María Elena, Navarro Martínez, Elena María, Casamayor Pujol, Víctor, Dustdar, Schahram
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/42598
Acceso en línea:https://doi.org/10.1109/MIC.2024.3521607
https://hdl.handle.net/10578/42598
Access Level:acceso abierto
Palabra clave:Collective intelligence
Complex systems
Digital twins
Fault tolerance
Fault tolerant systems
Manufacturing
Medical services
Predictive models
Scalability
Smart cities
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spelling Digital Twins and Artificial Collective Intelligence: Synergies for the FuturePretel Fernández, María ElenaNavarro Martínez, Elena MaríaCasamayor Pujol, VíctorDustdar, SchahramCollective intelligenceComplex systemsDigital twinsFault toleranceFault tolerant systemsManufacturingMedical servicesPredictive modelsScalabilitySmart citiesDigital twins (DTs) and artificial collective intelligence (ACI) are transformative technologies that, when combined, hold significant potential for managing complex systems across diverse domains, such as smart cities, health care, and manufacturing. DTs encompass both physical objects and their virtual counterparts, enabling real-time monitoring, control, and predictive modeling, while ACI enhances decision-making by leveraging the collective knowledge from multiple models. This article explores the synergies between DT and ACI, focusing on their integration into federated DTs (FDTs), which are networks of autonomous, collaborative DTs. By leveraging collaboration, FDTs optimize processes, improve scalability, and adapt to dynamic environments. We analyze the properties of DTs and ACI and identify opportunities for innovation and challenges in areas, such as scalability, adaptability, and fault tolerance. This integration paves the way for smarter systems capable of addressing the complexities of modern technological and societal challenges.IEEE202520252025info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1109/MIC.2024.3521607https://hdl.handle.net/10578/42598reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésPID2022-140907OB-I00SBPLY/21/180501/0000302022-GRIN-34436CNS2023-144359info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/425982026-05-27T07:36:41Z
dc.title.none.fl_str_mv Digital Twins and Artificial Collective Intelligence: Synergies for the Future
title Digital Twins and Artificial Collective Intelligence: Synergies for the Future
spellingShingle Digital Twins and Artificial Collective Intelligence: Synergies for the Future
Pretel Fernández, María Elena
Collective intelligence
Complex systems
Digital twins
Fault tolerance
Fault tolerant systems
Manufacturing
Medical services
Predictive models
Scalability
Smart cities
title_short Digital Twins and Artificial Collective Intelligence: Synergies for the Future
title_full Digital Twins and Artificial Collective Intelligence: Synergies for the Future
title_fullStr Digital Twins and Artificial Collective Intelligence: Synergies for the Future
title_full_unstemmed Digital Twins and Artificial Collective Intelligence: Synergies for the Future
title_sort Digital Twins and Artificial Collective Intelligence: Synergies for the Future
dc.creator.none.fl_str_mv Pretel Fernández, María Elena
Navarro Martínez, Elena María
Casamayor Pujol, Víctor
Dustdar, Schahram
author Pretel Fernández, María Elena
author_facet Pretel Fernández, María Elena
Navarro Martínez, Elena María
Casamayor Pujol, Víctor
Dustdar, Schahram
author_role author
author2 Navarro Martínez, Elena María
Casamayor Pujol, Víctor
Dustdar, Schahram
author2_role author
author
author
dc.subject.none.fl_str_mv Collective intelligence
Complex systems
Digital twins
Fault tolerance
Fault tolerant systems
Manufacturing
Medical services
Predictive models
Scalability
Smart cities
topic Collective intelligence
Complex systems
Digital twins
Fault tolerance
Fault tolerant systems
Manufacturing
Medical services
Predictive models
Scalability
Smart cities
description Digital twins (DTs) and artificial collective intelligence (ACI) are transformative technologies that, when combined, hold significant potential for managing complex systems across diverse domains, such as smart cities, health care, and manufacturing. DTs encompass both physical objects and their virtual counterparts, enabling real-time monitoring, control, and predictive modeling, while ACI enhances decision-making by leveraging the collective knowledge from multiple models. This article explores the synergies between DT and ACI, focusing on their integration into federated DTs (FDTs), which are networks of autonomous, collaborative DTs. By leveraging collaboration, FDTs optimize processes, improve scalability, and adapt to dynamic environments. We analyze the properties of DTs and ACI and identify opportunities for innovation and challenges in areas, such as scalability, adaptability, and fault tolerance. This integration paves the way for smarter systems capable of addressing the complexities of modern technological and societal challenges.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1109/MIC.2024.3521607
https://hdl.handle.net/10578/42598
url https://doi.org/10.1109/MIC.2024.3521607
https://hdl.handle.net/10578/42598
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2022-140907OB-I00
SBPLY/21/180501/000030
2022-GRIN-34436
CNS2023-144359
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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