A Review on Cloud and Fog Computing Integration for IoT: Platforms Perspective

Baktyan, Asrar; Zahary, Ammar

doi:10.4108/eai.20-12-2018.156084

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…p r i n t ( average ) ; S e r i a l . p r i n t l n ( " cm " ) ; } delay ( 5 0 0 ) ; } void i n i t i a t e T r i g g e r ( ) { d i g i t a l W r i t e ( T r i g g e r P i n , LOW) ; delayMicroseconds ( 2 ) ; d i g i t a l W r i t e ( T r i g g e r P i n , HIGH ) ; delayMicroseconds ( 1 0 ) ; d i g i t a l W r i t e ( T r i g g e r P i n , LOW) ; } byte deviceAddress [ 8 ] ;…”

Section: Appendix a Sketchs Arduino Ide Codesmentioning

confidence: 99%

“…This practice can yield substantial advantages, including improved energy efficiency by increasing percentages and achieving high-performance benchmarks, which are indicative of a high degree of flexibility, reduced H 2 consumption, and cost-effective operations for both hydrogen advanced mobility and hydrogen fueling infrastructure systems. In addition, the utilization of the Internet of Things (IoT) in the pursuit of sustainable development is focused on the creation of engineered systems [8], promoting environmental protection and preservation of natural ecosystems [9]. By interconnecting various systems through sensor and communication technologies, IoT for sustainability strives to establish a paradigm that harmonizes the community's objectives of ecological and environmental conservation while ensuring economic stability and security [10].…”

Section: Introductionmentioning

confidence: 99%

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Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

Paredes-Baños,

Molina-Garcia,

Mateo-Aroca

et al. 2024

Electronics

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This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.

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Section: Appendix a Sketchs Arduino Ide Codesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

Paredes-Baños,

Molina-Garcia,

Mateo-Aroca

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…De acuerdo a (Baktyan & Zahary, 2018) y considerando las ventajas discutidas, un enfoque de fog computing podría beneficiar a los tipos de aplicación siguientes:…”

Section: Tipos De Aplicaciones Beneficiarias De Fog Computingunclassified

Un acercamiento a fog computing

Fernández¹

2020

SISTEMAS

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Los sensores y otros dispositivos de Internet de las Cosas (IoT) generan voluminosos, variados y veloces datos primarios que pueden incluir ruido, irrelevancia y poca sensibilidad al contexto, además de crearse, con bastante frecuencia, con una alta distribución geográfica. La transferencia de tales datos directamente a la nube genera incremento de errores, pérdida de datos y alta probabilidad de congestión de tráfico en la red, además de un gasto considerable de recursos, sin una ganancia asociada. Con el fin de disminuir la latencia y el tráfico innecesario de datos en Internet, mediante el aprovechamiento de recursos distribuidos geográficamente más cerca de la fuente, surge el paradigma Fog computing, conformando un ecosistema IoT-Fog-Cloud. El presente artículo ofrece un acercamiento conceptual a fog computing, expone sus ventajas y los tipos de aplicaciones IoT para las cuales es más apropiado su uso. Finalmente, se resumen los desafíos más importantes asociados a tal enfoque emergente.

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“…Execution and load management in edge architectures has been tended to in the past [5,22], yet frequently the structures alluded to do not mirror the regularly geographically distributed nature of edge computing. We extend here on works like [9,34] that have considered single autonomous clusters as it were.…”

Section: Introductionmentioning

confidence: 99%

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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Recently distributed computing capacities are brought to the edge of the Internet, permitting Internet-of-Things applications to process calculation all the more locally and subsequently more productively and this has brought a totally different scope of apparatuses and usefulness. This instrument can The most significant characterizing highlights of edge processing are low latency, location awareness, wide geographic distribution, versatility, support for countless nodes, etc. We want to likely limit the latency and delay in edge-based structures. We center around a progressed compositional setting that considers communication and processing delays and the management effort notwithstanding a real request execution time in an operational efficiency situation. Our design is based on multi-cluster edge layer with nearby autonomous edge node clusters. We will contend that particle swarm optimization as a bio-motivated optimization approach is a perfect candidate for distributed IoT load handling in self-managed edge clusters. By designing a controller and utilizing a particle swarm optimization algorithm, we show that delay and end-to-end latency can be reduced.

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A Review on Cloud and Fog Computing Integration for IoT: Platforms Perspective

Cited by 11 publications

References 10 publications

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

Un acercamiento a fog computing

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

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