2024-03-29T14:35:43Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72452023-03-17T11:23:02Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Alonso Rincón, Ricardo S.
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Sittón-Candanedo, Inés
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600
Casado Vara, Roberto Carlos
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500
0000-0003-0198-696X
Prieto, Javier
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Corchado, Juan M.
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2023-01-17T07:44:47Z
2023-01-17T07:44:47Z
2020-07
http://hdl.handle.net/10259/7245
10.3390/su12145706
2071-1050
The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor
devices gathering information and forwarding to the Cloud, where data is stored and processed
to infer knowledge and perform analysis and predictions. Cloud service providers charge users
based on the computing and storage resources used in the Cloud. In this regard, Edge Computing
can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered
in network edge before being sent to the Cloud, resulting in shorter response times and providing
a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover,
there is a growing trend to share physical network resources and costs through Network Function
Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks
(SDNs) are used to reconfigure the network dynamically according to the necessities during time.
For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques,
can be employed to manage virtual data flows in networks. In this work, we propose the evolution of
an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the
Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement
Learning techniques for the implementation of the SDN controller.
This work has been partially supported by the European Regional Development Fund (ERDF) through the Interreg Spain-Portugal V-A Program (POCTEP) under grant 0677_DISRUPTIVE_2_E (Intensifying the activity of Digital Innovation Hubs within the PocTep region to boost the development of disruptive and last generation ICTs through cross-border cooperation). Inés Sittón-Candanedo has been supported by scholarship program: IFARHU-SENACYT (Government of Panama).
application/pdf
eng
MDPI
Sustaunability. 2020, V. 12, n. 14, e5706
https://doi.org/10.3390/su12145706
info:eu-repo/grantAgreement/EC/POCTEP/0677_DISRUPTIVE_2_E/EU/Intensifying the activity of Digital Innovation Hubs within the PocTep region to boost the development of disruptive and last generation ICTs through cross-border cooperation/
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Industrial internet of things
Edge computing
Software defined networks
Network function virtualization
Deep reinforcement learning
Informática
Computer science
Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Sustainability
12
14
THUMBNAIL
Casado-sustainability_2020.pdf.jpg
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ORIGINAL
Casado-sustainability_2020.pdf
Casado-sustainability_2020.pdf
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10259/7245
oai:riubu.ubu.es:10259/7245
2023-03-17 12:23:02.019
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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