Agents and Computer Vision for Processing Stereoscopic Images

Rodrı́guez, Sara; Prieta, Fernando De la; Tapia, Dante I.; Corchado, Juan M.

doi:10.1007/978-3-642-13803-4_12

Cited by 35 publications

(5 citation statements)

References 10 publications

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“…One of the first scenarios in which the usefulness of Edge Computing has been validated has been Industry 4.0., where it has been presented as an alternative to solve the problems generated by the large data flows generated by IoT devices. In the context of Industry 4.0, A state-of-the-art review have conducted of the main references architectures that include EC technology [31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: ]mentioning

confidence: 99%

GECA: A Global Edge Computing Architecture

Sittón-Candanedo

2020

The Role of Artificial Intelligence and Distributed Computing in IoT Applications

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The smart scenarios are in continuous advance thanks to the group of devices that are always connected to Internet. In the last 10 years this phenomenon has been called Internet of Things (IoT). The adoption of the IoT to generate these intelligent scenarios or smart has motivated that governments, universities, research centers or companies are in constant evolution to face the challenges brought by the deployment of IoT platforms. Its disruption in all environments generates large volumes of data, requirements by users for their applications to respond in real time, but with low bandwidth or power consumption, and without delays. The challenges presented by the development of applications for the IoT has occasioned the emergence of technologies such as Edge Computing. This paper presents GECA: A Global Edge Computing Architecture, an architecture based on Edge Computing, which has been deployed in smart farming and smart energy scenarios, with the aim of demonstrating that it is possible to reduce latency, energy consumption and bandwidth costs and integrate Edge computing in IoT platforms.

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Section: ]mentioning

confidence: 99%

GECA: A Global Edge Computing Architecture

Sittón-Candanedo

2020

The Role of Artificial Intelligence and Distributed Computing in IoT Applications

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“…In this case, «the traveller» would correspond to the set of quantum keys, and «the places» would correspond to the municipalities. The problem of the traveller is NP-complete [54][55][56][57][58][59][60][61][62], and it requires approximation methods in order to find local solutions.…”

Section: Quantum Key Distributionmentioning

confidence: 99%

Distribution of quantum keys over commercial networks

García-Cobo¹

2020

The Role of Artificial Intelligence and Distributed Computing in IoT Applications

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Modern cryptography -as it was conceived -is under a threat by the development of quantum mechanics applications. The abilities of quantum computers for solving complex mathematical problems, as a strong computational novelty, is the root of that risk. The main challenge is to find commercial exploits of quantum properties and developments, following these directions for both, theoretic and test tube environments.This work proposes a pilot experiment that implements a quantum communication system on a commercial fiber optic network, covering an area of almost 100,000 km 2 .

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“…In addition to Fog Computing, Edge Computing aims at the same objective. However, additional computing power and device storage at the edge are used [64][65][66][67][68][69][70].…”

Section: Edge Computing and Fog Computingmentioning

confidence: 99%

A deep tech architecture for intelligent IoT systems

Öztürk

2020

The Role of Artificial Intelligence and Distributed Computing in IoT Applications

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The increase in the number of connected devices on the Internet of Things (IoT), interactions and the amount of data raises a number of issues. Two major problems are limitations in terms of network latency and bandwidth. While cloud-based infrastructures give us access to scalable, on-demand storage and processing services that can scale to the requirements of the Internet of Things (IoT), these centralized resources can create unacceptable delays and performance problems for devices that have latency-sensitive applications, such as health monitoring and emergency response applications. This article has been created for the PhD thesis that aims to create a deep tech architecture for intelligent IoT systems.

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Agents and Computer Vision for Processing Stereoscopic Images

Cited by 35 publications

References 10 publications

GECA: A Global Edge Computing Architecture

GECA: A Global Edge Computing Architecture

Distribution of quantum keys over commercial networks

A deep tech architecture for intelligent IoT systems

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