RETRACTED CHAPTER: A Survey on Software-Defined Networks and Edge Computing over IoT

Alonso, Ricardo S.; Sittón-Candanedo, Inés; González, Sara Rodríguez; García, Óscar; Prieto, Javier

doi:10.1007/978-3-030-24299-2_25

Cited by 15 publications

(8 citation statements)

References 56 publications

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“…The second challenge to be solved is that, regardless of the transmission technology used, there may be multiple different user or applications using a common transmission infrastructure from the IoT to the Cloud. Sharing network resources and infrastructure by various organizations, operators and user groups can be done through Network Function Virtualization and Software-Defined Networks [13].…”

Section: Challenges In Internet Of Things and Industrial Internet Of Things Scenariosmentioning

confidence: 99%

“…Figure 1 shows the basic scheme of an edge computing architecture where the edge nodes allow user application processes to be executed closer to the data sources [30]. The edge nodes perform compute tasks such as filtering, processing, caching, load balancing by reducing data sent or received from the cloud and requesting services and information [13]. There is a wide variety of scenarios where solutions based on IoT and Edge Computing are being applied.…”

Section: Edge Computing and Edge-iot Platformsmentioning

confidence: 99%

“…The Global Edge Computing Architecture has already been applied, in fact, in Smart Energy [12] and Smart Farming [6] scenarios where it was necessary to reduce data transfer between the IoT and the Cloud and, therefore, the costs of computing and storage in the Cloud. However, in its previous version it did not have SDN/NFV capabilities, which have been explored by Alonso et al [13] for its current incorporation to the architecture. The new proposed architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

et al. 2020

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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.

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Section: Challenges In Internet Of Things and Industrial Internet Of Things Scenariosmentioning

confidence: 99%

Section: Edge Computing and Edge-iot Platformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

et al. 2020

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“…In recent times, IoT has become a common and integral part of everyone's daily lives. It has improved people's lifestyle in several domains through millions of sensors and devices which perform various tasks such as to measure, collect, store, process and transfer huge amounts of data everyday [5], [6].…”

Section: Related Workmentioning

confidence: 99%

Design and Deployment of a Flash Flood Monitoring IoT: Challenges and Opportunities

Basnyat

Singh

Roy

et al. 2020

2020 IEEE International Conference on Smart Computing (SMARTCOMP)

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Successful implementation of the Internet of Thing (IoT) is precursory to a thriving smart city. However, the technical, physical, and environmental conditions can often pose challenges in their successful deployments. The deployment is further complicated if the time and location of implementation are amidst a natural disaster. In this work, we use flash flood detection as a natural hazard testbed and describe various IoT deployment, our progression, and first-hand experience from those implementations. We compare and contrast three IoTs and their performance in real-time execution. Next, we discuss systems architecture and their end-toend design and present lessons learned from these heterogeneous deployments. Additionally, we evaluate and outline our observations, challenges, and opportunities for further improvement. We also formulate standard evaluation metrics for their scoring and document our deployment journey.

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“…Third, it supports heterogeneous environments to allow the interoperability of a diverse range of devices with different communication technologies. Besides, 5G networks support programmable and reconfigurable deployments of core network functions through Network Function Virtualization (NFV) [18] and Software-Defined Networks (SDN) [19]. Edge computing assists 5G networks for having a successful deployment in order to provide a large amount of broadband, low transmission latency, and a huge amount of communication connections simultaneously [20].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of a Hybrid Architecture for Edge Computing in 5G and Beyond

Rahimi,

Picaud,

Costanzo

et al. 2020

Preprint

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Edge Computing in 5G and Beyond is a promising solution for ultra-low latency applications (e.g. Autonomous Vehicle, Augmented Reality, and Remote Surgery), which have an extraordinarily low tolerance for the delay and require fast data processing for a very high volume of data. The requirements of delay-sensitive applications (e.g. Low latency, proximity, and Location/Context-awareness) cannot be satisfied by Cloud Computing due to the high latency between User Equipment and Cloud. Nevertheless, Edge Computing in 5G and beyond can promise an ultra-high-speed caused by placing computation capabilities closer to endpoint devices, whereas 5G encourages the speed rate that is 200 times faster than 4G LTE-Advanced. This paper deeply investigates Edge Computing in 5G and characterizes it based on the requirements of ultra-low latency applications. As a contribution, we propose a hybrid architecture that takes advantage of novel and sustainable technologies (e.g. D2D communication, Massive MIMO, SDN, and NFV) and has major features such as scalability, reliability and ultra-low latency support. The proposed architecture is evaluated based on an agent-based simulation that demonstrates it can satisfy requirements and has the ability to respond to high volume demands with low latency.

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RETRACTED CHAPTER: A Survey on Software-Defined Networks and Edge Computing over IoT

Cited by 15 publications

References 56 publications

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

Design and Deployment of a Flash Flood Monitoring IoT: Challenges and Opportunities

Design and Simulation of a Hybrid Architecture for Edge Computing in 5G and Beyond

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