An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

[EN] In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed...

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Detalles Bibliográficos
Autores: Rehman, Amjad, Haseeb, Khalid, Saba, Tanzila, Lloret, Jaime|||0000-0002-0862-0533, Sendra, Sandra|||0000-0001-9556-9088
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/192817
Acceso en línea:https://riunet.upv.es/handle/10251/192817
Access Level:acceso abierto
Palabra clave:Multimedia sensors
Optimizing resources
Software-defined networks
Delay controlled
Artificial intelligence of things
INGENIERÍA TELEMÁTICA
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repository_id_str
spelling An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of ThingsRehman, AmjadHaseeb, KhalidSaba, TanzilaLloret, Jaime|||0000-0002-0862-0533Sendra, Sandra|||0000-0001-9556-9088Multimedia sensorsOptimizing resourcesSoftware-defined networksDelay controlledArtificial intelligence of thingsINGENIERÍA TELEMÁTICA[EN] In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.This research was technically supported by the Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia. The authors are thankful for the technical support. There is no funding for this research work.MDPI AGDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaInstituto de Investigación para la Gestión Integrada de Zonas CosterasArtificial Intelligence and Data Analytics Lab, Prince Sultan UniversityRepositorio Institucional de la Universitat Politècnica de València Riunet20212021-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/192817reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1928172026-06-13T07:49:27Z
dc.title.none.fl_str_mv An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
title An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
spellingShingle An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
Rehman, Amjad
Multimedia sensors
Optimizing resources
Software-defined networks
Delay controlled
Artificial intelligence of things
INGENIERÍA TELEMÁTICA
title_short An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
title_full An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
title_fullStr An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
title_full_unstemmed An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
title_sort An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things
dc.creator.none.fl_str_mv Rehman, Amjad
Haseeb, Khalid
Saba, Tanzila
Lloret, Jaime|||0000-0002-0862-0533
Sendra, Sandra|||0000-0001-9556-9088
author Rehman, Amjad
author_facet Rehman, Amjad
Haseeb, Khalid
Saba, Tanzila
Lloret, Jaime|||0000-0002-0862-0533
Sendra, Sandra|||0000-0001-9556-9088
author_role author
author2 Haseeb, Khalid
Saba, Tanzila
Lloret, Jaime|||0000-0002-0862-0533
Sendra, Sandra|||0000-0001-9556-9088
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Instituto de Investigación para la Gestión Integrada de Zonas Costeras
Artificial Intelligence and Data Analytics Lab, Prince Sultan University
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Multimedia sensors
Optimizing resources
Software-defined networks
Delay controlled
Artificial intelligence of things
INGENIERÍA TELEMÁTICA
topic Multimedia sensors
Optimizing resources
Software-defined networks
Delay controlled
Artificial intelligence of things
INGENIERÍA TELEMÁTICA
description [EN] In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/192817
url https://riunet.upv.es/handle/10251/192817
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406884929208320
score 15.300719

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