Publication
AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management
| Summary: | Modern airports are complex Cyber-Physical Systems (CPS), where effective coordination between physical entities – like pedestrians and Autonomous Guided Vehicles (AGVs) – and computational components is essential for safety and efficiency. This study introduces a novel CPS architecture that integrates Artificial Intelligence (AI) and Visible Light Communication (VLC) to optimize mobility and enhance real-time responsiveness. Using tetrachromatic LED luminaires modulated via On-Off Keying (OOK) and amorphous SiC optical receivers in a mesh-based hybrid topology, the system creates a VLC infrastructure that delivers real-time, location-aware navigation. A custom protocol ensures low-latency, reliable data exchange between agents and the digital core. VLC receivers capture continuous data on agent positions and movements, which is processed by Deep Reinforcement Learning (DRL) agents trained via Q-learning. These agents adaptively manage traffic flow, minimize congestion, and improve throughput. Simulations and experiments confirm the system’s advantages over traditional methods, enabling GPS-independent indoor navigation, efficient mixed traffic coordination, and scalable deployment within smart airport environments. |
|---|---|
| Main Authors: | Vieira, Manuela |
| Other Authors: | Vieira, Manuel A.; Galvão, Gonçalo; Louro, Paula; Vieira, Pedro; Fantoni, Alessandro |
| Subject: | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| Year: | 2025 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| _version_ | 1879446563198074880 |
|---|---|
| author | Vieira, Manuela |
| author2 | Vieira, Manuel A. Galvão, Gonçalo Louro, Paula Vieira, Pedro Fantoni, Alessandro |
| author2_role | author author author author author |
| author_facet | Vieira, Manuela Vieira, Manuel A. Galvão, Gonçalo Louro, Paula Vieira, Pedro Fantoni, Alessandro |
| author_role | author |
| contributor_name_str_mv | UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas International Frequency Sensor Association (IFSA) RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Vieira, Manuela\"},{\"Person.name\":\"Vieira, Manuel A.\"},{\"Person.name\":\"Galvão, Gonçalo\"},{\"Person.name\":\"Louro, Paula\"},{\"Person.name\":\"Vieira, Pedro\"},{\"Person.name\":\"Fantoni, Alessandro\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas International Frequency Sensor Association (IFSA) RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Vieira, Manuela Vieira, Manuel A. Galvão, Gonçalo Louro, Paula Vieira, Pedro Fantoni, Alessandro |
| datacite.date.Accepted.fl_str_mv | 2025-11-28T00:00:00Z |
| datacite.date.available.fl_str_mv | 2026-02-12T17:35:02Z |
| datacite.date.embargoed.fl_str_mv | 2026-02-12T17:35:02Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| datacite.titles.title.fl_str_mv | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| dc.contributor.none.fl_str_mv | UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas International Frequency Sensor Association (IFSA) RUN |
| dc.creator.none.fl_str_mv | Vieira, Manuela Vieira, Manuel A. Galvão, Gonçalo Louro, Paula Vieira, Pedro Fantoni, Alessandro |
| dc.date.Accepted.fl_str_mv | 2025-11-28T00:00:00Z |
| dc.date.available.fl_str_mv | 2026-02-12T17:35:02Z |
| dc.date.embargoed.fl_str_mv | 2026-02-12T17:35:02Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/200347 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| dc.title.fl_str_mv | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Modern airports are complex Cyber-Physical Systems (CPS), where effective coordination between physical entities – like pedestrians and Autonomous Guided Vehicles (AGVs) – and computational components is essential for safety and efficiency. This study introduces a novel CPS architecture that integrates Artificial Intelligence (AI) and Visible Light Communication (VLC) to optimize mobility and enhance real-time responsiveness. Using tetrachromatic LED luminaires modulated via On-Off Keying (OOK) and amorphous SiC optical receivers in a mesh-based hybrid topology, the system creates a VLC infrastructure that delivers real-time, location-aware navigation. A custom protocol ensures low-latency, reliable data exchange between agents and the digital core. VLC receivers capture continuous data on agent positions and movements, which is processed by Deep Reinforcement Learning (DRL) agents trained via Q-learning. These agents adaptively manage traffic flow, minimize congestion, and improve throughput. Simulations and experiments confirm the system’s advantages over traditional methods, enabling GPS-independent indoor navigation, efficient mixed traffic coordination, and scalable deployment within smart airport environments. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/e44b352a-8b67-4b9f-9335-baf4937a83e7/download |
| id | run_65a95f46f90434c563a287212a01baea |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/200347 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/200347 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Vieira, Manuela Vieira, Manuel A. Galvão, Gonçalo Louro, Paula Vieira, Pedro Fantoni, Alessandro |
| publishDate | 2025 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenModern airports are complex Cyber-Physical Systems (CPS), where effective coordination between physical entities – like pedestrians and Autonomous Guided Vehicles (AGVs) – and computational components is essential for safety and efficiency. This study introduces a novel CPS architecture that integrates Artificial Intelligence (AI) and Visible Light Communication (VLC) to optimize mobility and enhance real-time responsiveness. Using tetrachromatic LED luminaires modulated via On-Off Keying (OOK) and amorphous SiC optical receivers in a mesh-based hybrid topology, the system creates a VLC infrastructure that delivers real-time, location-aware navigation. A custom protocol ensures low-latency, reliable data exchange between agents and the digital core. VLC receivers capture continuous data on agent positions and movements, which is processed by Deep Reinforcement Learning (DRL) agents trained via Q-learning. These agents adaptively manage traffic flow, minimize congestion, and improve throughput. Simulations and experiments confirm the system’s advantages over traditional methods, enabling GPS-independent indoor navigation, efficient mixed traffic coordination, and scalable deployment within smart airport environments.application/pdfenAI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic ManagementVieira, ManuelaVieira, Manuel A.Galvão, GonçaloLouro, PaulaVieira, PedroFantoni, AlessandroUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasInternational Frequency Sensor Association (IFSA)HostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf2306-8515URNIsPartOfPURE: 153162206URNIsPartOfPURE UUID: e4970472-672a-4018-bb1a-6f191cde61f0URNIsPartOfScopus: 1050259522432026-02-12T17:35:02Z2025-11-282025-11-28T00:00:00ZHandlehttp://hdl.handle.net/10362/200347http://purl.org/coar/access_right/c_abf2open accessAutonomous guided vehicles (AGVs)Cyber-physical systems (CPS)Deep reinforcement learning (DRL)Internet of things (IoT)Visible light communication (VLC)Control and Systems EngineeringElectrical and Electronic Engineering3001772 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/e44b352a-8b67-4b9f-9335-baf4937a83e7/download |
| spellingShingle | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management Vieira, Manuela Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| status | SINGLETON |
| subject.fl_str_mv | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| title | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| title_full | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| title_fullStr | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| title_full_unstemmed | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| title_short | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| title_sort | AI-enhanced VLC Cyber-physical Architecture for Adaptive Airport Traffic Management |
| topic | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| topic_facet | Autonomous guided vehicles (AGVs) Cyber-physical systems (CPS) Deep reinforcement learning (DRL) Internet of things (IoT) Visible light communication (VLC) Control and Systems Engineering Electrical and Electronic Engineering |
| url | http://hdl.handle.net/10362/200347 |
| visible | 1 |
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