2026-04-18T09:33:58Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72452023-03-17T11:23:02Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Alonso Rincón, Ricardo S. Sittón-Candanedo, Inés Casado Vara, Roberto Carlos Prieto, Javier Corchado, Juan M. 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