Abstract Intrusion detection in Fog-Assisted Wireless Sensor Networks (Fog-WSNs) is a critical security measure designed to protect the functionality and integrity of these networks. Fog computing extends the abilities of traditional WSNs by introducing edge devices or fog nodes that can process data and provide computational resources. Intrusion detection in Fog-WSNs is vital to maintain the privacy and security of data generated and transmitted by sensors and processed at the fog node. Machine learning (ML) algorithms are widely employed to detect intrusion. These models can be trained on historical data to detect known attack patterns and can adapt to emerging threats. By implementing effective intrusion detection mechanisms, these networks can alleviate risks and ensure the reliable operation of critical applications in different fields namely IoT, smart cities, and industrial automation. This study designs an Intrusion Detection using Binary Arithmetic Optimization with Sparse Auto encoder (BAOA-SAE) technique for Fog Assisted WSN. The major aim of BAOA-SAE method is to recognize and classify the presence of the intrusions in the network. Primarily, the BAOA-SAE method focuses on the election of prominent features in the network data. Next, the SAE model is implemented to categorize the presence of intrusions. To improve the intrusion rate, the BAOA-SAE technique employs bacterial foraging optimization algorithm (BFOA). A broad range of simulated outcomes were performed on benchmark IDS dataset. The extensive outcomes highlighted the high efficiency of the BAOA-SAE method over other existing techniques. FogAssisted FogWSNs (Fog-WSNs resources node ML (ML threats mechanisms IoT cities automation BAOASAE BAOA (BAOA-SAE WSN Primarily Next rate BFOA. BFOA . (BFOA) dataset techniques (BFOA

BACKGROUND: Public procurement plays a pivotal role in the South African economy and accounts for a significant portion of public expenditure. The effective management of procurement processes is crucial for ensuring that money is spent wisely and there is an efficient service delivery. However, the paper-based procurement systems have been plagued by inefficiencies. OBJECTIVES: To address the challenges of paper-based procurement systems, the study developed a digital procurement framework. The development of a digital procurement framework for the South African public sector represents a strategic response to these challenges and aims to leverage cutting-edge digital technologies to revolutionise the way that the government procures goods and services. METHOD: The study followed a qualitative research methodology in which content analysis was used to identify themes. Subsequently, a thematic analysis was conducted to formulate the components of the proposed framework. The study facilitated an understanding of the Internet of Things (IoT) and cloud computing. RESULTS: The literature revealed that Industry 4.0 technologies are crucial as they present opportunities which are consistent with legislation like scalability, cost efficiency, collaboration, transparency, accountability and process efficiency. CONCLUSION: Implementing procurement reforms effectively will result in a public sector procurement system that is responsible, offers value for money and ensures high-quality service delivery. CONTRIBUTION: The study contributed to the body of knowledge by providing a guide for a digital procurement of South Africa's public sector and impact of technology on public procurement.

Abstract: Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research has been carried out to investigate and apply computer vision techniques to address this problem. One of the most promising approaches for wildfire fighting is the use of drones equipped with visible and infrared cameras for the detection, monitoring, and fire spread assessment in a remote manner but in close proximity to the affected areas. However, implementing effective computer vision algorithms on board is often prohibitive since deploying full-precision deep learning models running on GPU is not a viable option, due to their high power consumption and the limited payload a drone can handle. Thus, in this work, we posit that smart cameras, based on low-power consumption field-programmable gate arrays (FPGAs), in tandem with binarized neural networks (BNNs), represent a cost-effective alternative for implementing onboard computing on the edge. Herein we present the implementation of a segmentation model applied to the Corsican Fire Database. We optimized an existing U-Net model for such a task and ported the model to an edge device (a Xilinx Ultra96-v2 FPGA). By pruning and quantizing the original model, we reduce the number of parameters by 90%. Furthermore, additional optimizations enabled us to increase the throughput of the original model from 8 frames per second (FPS) to 33.63 FPS without loss in the segmentation performance: our model obtained 0.912 in Matthews correlation coefficient (MCC), 0.915 in F1 score and 0.870 in Hafiane quality index (HAF), and comparable qualitative segmentation results when contrasted to the original full-precision model. The final model was integrated into a low-cost FPGA, which was used to implement a neural network accelerator.

Abstract This work evaluates the feasibility of integrating lowcost Raspberry pi boards and ESP8266 microcontrollers with industrial equipment of Programmable Logic Controllers (PLC) in a decentralized network. These devices will be the nodes that participate in the exchange of data between the manufacturing floor and business services in a simple, reliable and economical way. The network nodes produce and consume data that is exchanged by an open-source protocol manager node called Node-RED. The protocol manager is a server with a Linux kernel on a RISC (Reduced instruction set computing) microprocessor. The sensors in the manufacturing nodes use SoC (System On Chip) microcontrollers and through the concept of Edge computing they acquire, process and send their data to the protocol manager. Thus, using the Iperf3, Wireshark and MTR tools, we will measure the performance of the communications link and the status of the participating nodes in their data production/consumption processes.

Resumen Este trabajo evalúa la viabilidad de integrar en una red descentralizada las placas de bajo costo Raspberry pi, microcontroladores ESP8266 con equipos industriales de Controladores Lógicos Programables (PLC). Estos dispositivos serán los nodos que participan en el intercambio de datos entre el piso de manufactura y los servicios empresariales de manera simple, fiable y económica. Los nodos de la red producen y consumen datos que son intercambiados por un nodo gestor de protocolos open source llamado Node-RED. El gestor de protocolos es un servidor con núcleo de Linux sobre un microprocesador RISC (Reduced instruction set computing). Los sensores en los nodos de manufactura utilizan microcontroladores SoC (System On Chip) y mediante el concepto de Edge computing adquieren, procesan y envían sus datos al gestor de protocolos. Así, mediante la herramienta Iperf3, Wireshark y MTR, mediremos el rendimiento del enlace de comunicaciones y el estado que guardan los nodos participantes en sus procesos producción/consumo de datos.

ABSTRACT In this work we propose a new technique to verify if a graph corresponds to some edge pairing. For that, we propose the use of what we call parallel graph; a graph where each edge is divided into two oriented half-edges. This graph associates a word with a given pairing, so that operations on the words can be used to determine the equivalence between two pairings. The relationship between the word and the vertices of the pairing is determined by a proposed algorithm called vertex rain. Finally, we determine the relationship between the vertex length and the associated word. halfedges. halfedges half edges. edges half-edges pairings rain Finally

RESUMO Neste trabalho propomos uma nova técnica para verificar se um grafo corresponde a algum emparelhamento de arestas. Para isso propomos o uso do que intitulamos grafo paralelo; um grafo onde cada aresta é dividida em duas semi-arestas orientadas. Este grafo associa uma palavra a um dado emparelhamento, de forma que operações sobre as palavras podem ser usadas para determinar a equivalência entre dois emparelhamentos. A relação entre a palavra e os vértices do emparelhamento é determinada por um algoritmo proposto chamado chuva de vértices. Por fim, determinamos a relação entre a extensão de vértices e a palavra associada. arestas paralelo semiarestas semi orientadas emparelhamentos fim associada

ABSTRACT Pollination using Unmanned Aerial Vehicles (UAVs) has emerged as a promising solution to the current pollination crisis. The dwindling number of natural pollinators forces the production of cutting-edge pollination technologies. This work proposes a module to optimize path planning for UAVs to travel in a minimum time. This study suggests a novel approach to maximize cross-pollination and minimize travel time with a highly efficient meta-heuristic optimization algorithm. This paper briefly describes a process we previously developed for flower insights that includes flower gender and gene identification and classification. With an insight into flowers, the proposed algorithm aims to achieve efficient and accurate pollination while minimizing energy consumption and convergence time. The Versatile Flower Pollination Algorithm’s (VFPA) approach is superior because it significantly reduces the amount of computing required while maintaining almost optimal performance. The proposed algorithm was successfully implemented to compute the distance between the male and female flowers and transfer nectar with a difference in the nectar value. The proposed approach shows promise for addressing the pollination crisis and reducing the reliance on traditional methods. (UAVs cuttingedge cutting edge technologies crosspollination cross metaheuristic meta heuristic classification Algorithms Algorithm s VFPA (VFPA performance value methods

RESUMO A polinização por meio de Veículos Aéreos Não Tripulados (VANTs) tem emergido como uma solução promissora para a atual crise de polinização. A diminuição do número de polinizadores naturais força a produção de tecnologias de polinização de ponta. Este trabalho propõe um módulo para otimizar o planejamento de trajetos para que os VANTs viajem em um tempo mínimo. Este estudo sugere uma nova abordagem para maximizar a polinização cruzada e minimizar o tempo de viagem com um algoritmo de otimização meta-heurística altamente eficiente. Este artigo descreve brevemente um processo que desenvolvemos anteriormente para insights de flores que inclui o gênero da flor e a identificação e classificação de genes. Com uma visão das flores, o algoritmo proposto visa alcançar uma polinização eficiente e precisa, minimizando o consumo de energia e o tempo de convergência. A abordagem do Versátil Algoritmo de Polinização de Flores (VFPA) é superior porque reduz significativamente a quantidade de computação necessária, mantendo um desempenho quase ideal. O algoritmo proposto foi implementado com sucesso para calcular a distância entre as flores masculinas e femininas e transferir néctar com uma diferença no valor do néctar. A abordagem proposta mostra-se promissora para enfrentar a crise de polinização e reduzir a dependência dos métodos tradicionais. (VANTs ponta mínimo metaheurística meta heurística genes precisa convergência VFPA (VFPA necessária ideal mostrase mostra se tradicionais

With the growth of data traffic, demand of huge number of digital devices and their interconnection to establish a reliable communication, the internet has become a potential demand of the society. To develop a system that securely connects the internet to real-world space would aid in the advancement of a human-centered society that balances economic progress with the resolution of social issues. This paper provides a detailed aspect of Society 5.0 with its requirements, architecture, and components. We have proceeded extensively with the state-of-the-art Society 5.0 and its link with Industry 4.0/5.0. Furthermore, the role of Society 5.0 in the sustainable development goals of the United Nations is well elaborated. Several emerging communication and computing technologies such 5G/5G-Internet of Things (IoT), edge computing/ cloud computing/ fog computing, Internet of everything, blockchain, and beyond networks have been also well explored to fulfill the demands of Society 5.0. The potential application of super smart cities (Society 5.0) with some real-time experience of inhabitants is thoroughly discussed. Finally, we highlighted several open research challenges with opportunities. OPEN LICENSE: CC BY-NC-ND

Resumen El objetivo de este trabajo es medir el impacto de la aplicación de técnicas de computación aproximada sobre un caso de estudio de implementación de un medidor de la dinámica de tráfico vehicular en una unidad computacional basada en un sistema embebido. El estudio parte de una implementación inicial del medidor hecha en C++. Para el análisis de la aplicación, la implementación inicial se perfila con un perfilador empotrado en el mismo código, que muestra información detallada de cada una de las partes. Durante el análisis de consumo, se encontraron partes optimizables con paralelismo a nivel de hilos y técnicas de computación aproximada como omisión de recuadros, que es una técnica inspirada en la perforación de lazos. La primer optimización realizada fue la implementación multihilo, que logró acelerar la aplicación 1.32 veces sin introducir errores en los resultados. Posteriormente, la implementación fue optimizada con la omisión de recuadros. Durante el desarrollo de este trabajo, se demuestra que modificar el número de recuadros omitidos de forma dinámica en tiempo de ejecución mejora considerablemente el error introducido comparado a mantener constante el número de recuadros omitidos. La combinación de ambas optimizaciones concluyó en una implementación 1.76 veces más rápida, donde la aplicación de computación aproximada mediante omisión de recuadros contribuyó hasta en un 25% sobre el total de la mejora, acelerando el medidor de 8.7 recuadros por segundo a 15 en el escenario más crítico a cambio de la introducción de errores numéricos.

Resumo O objetivo deste trabalho é medir o impacto da aplicação de técnicas de computação aproximada em um estudo de caso da implementação de um medidor de dinâmica de tráfego de veículos em uma unidade computacional baseada em um sistema embarcado. O estudo começa a partir de uma implementação inicial do medidor feita em C++. Para a análise da aplicação, a implementação inicial é perfilada com um perfilador incorporado no próprio código, que exibe informações detalhadas sobre cada uma das partes. Durante a análise de consumo, foram encontradas peças otimizáveis com paralelismo em nível de thread e técnicas de computação aproximada, como o salto de quadros, que é uma técnica inspirada na perfuração de laço. A primeira otimização realizada foi a implementação multithread, que conseguiu acelerar a aplicação em 1,32 vezes sem introduzir erros nos resultados. Posteriormente, a implementação foi otimizada com o salto de quadros. Durante o desenvolvimento deste trabalho, mostra-se que modificar a dinâmica do número de salto de quadros em tempo de execução melhora consideravelmente o erro introduzido em comparação com a manutenção constante do número de salto de quadros. A combinação de ambas as otimizações resultou em uma implementação 1,76 vezes mais rápida, onde a aplicação da computação aproximada por meio do salto de quadros contribuiu com até 25% da melhoria total, acelerando o medidor de 8,7 quadros por segundo para 15 no cenário mais crítico, em troca da introdução de erros numéricos.

Abstract This work aims to measure the impact of approximate computing on a case study of traffic dynamics metering on a System-on-Chip edge computing device. Firstly, the study proposes a baseline implementation of the metering system in C++. To analyze the application in detail, study profiled the baseline using a built-in instrumented profiler, presenting the overall performance of each of its parts. During the hotspot analysis, some parts had optimization opportunities exploitable by multi-threading and approximate computing techniques, particularly frame skipping, which is inspired by the loop perforation approximate technique. The first optimization employed was multi-threading, which led to a 1.32x speedup on the application without introducing errors in the metrics. Then, the meter was optimized by using frame skipping. This work demonstrated that adaptatively modifying the number of frames skipped improved the error in the final metrics compared to keeping it fixed. In terms of performance, the frame skipping brought the overall speed up to 1.76x. Approximate computing, in particular, frame skipping, managed to contribute up to 25% of the overall speedup, managing to accelerate the meter from 8.7 frames per second to 15 fps in the most critical case in exchange for some numerical error on the final metrics.

Abstract COVID-19 has caused serious health damage, infecting millions of people and unfortunately causing the death of several ones around the world. The vaccination programs of each government have influenced in declining those rates. Nevertheless, new coronavirus mutations have emerged in different countries, which are highly contagious, causing concern with vaccination effectiveness. So far, wearing facemasks in public continues being the most effective protocol to avoid and prevent COVID-19 spread. In this context, there is a demand of automatic facemask detection services to remind people the importance of wearing them appropriately. In this work, a performance evaluation of an AIoT system to detect correct, inappropriate, and non- facemask wearing, based on two computational models: Cloud and Edge, was conducted. Having as objective to determine which model better suites a real environment (indoor and outdoor), based on: reliability of the detector algorithm, use of computational resources, and response time. Experimental results show that Edge-implementation got better performance in comparison to Cloud-implementation.

Resumen La COVID-19 ha provocado graves daños a la salud: centenas de millones de personas infectadas y varios millones de fallecidos en el mundo. Los programas de vacunación de cada Gobierno han influido en el decaimiento de estos índices, pero con la aparición de nuevas mutaciones del coronavirus más contagiosas, la preocupación sobre la efectividad de las vacunas se hace presente. Frente a esta situación el uso de mascarillas sigue siendo eficaz para prevenir la transmisión y contagio de la COVID-19. Lo que ha generado una creciente demanda de servicios de detección automática de mascarillas, que permita recordar a las personas la importancia del empleo de estas. En este trabajo se plantea un análisis del rendimiento de un sistema AIoT para la detección del uso correcto, incorrecto y sin mascarilla basado en dos modelos computacionales de Cloud y Edge, con la finalidad de determinar qué modelo se adecua mejor en un entorno real (interior y exterior) sobre la base de la confiabilidad del algoritmo, uso de recursos computacionales y tiempo de respuesta. Los resultados experimentales demuestran que el modelo computacional Edge presentó un mejor desempeño en comparación con el Cloud.

ABSTRACT With the development of the Internet of Things, the number of devices connected to the Internet grows exponentially, and this brings with it a huge increase in the amount of data to be processed in the cloud, increasing traffic, to the detriment of the quality of service. This, together with the low latency and mobility requirements of more than a few applications associated with the Web, has led to a rethinking of the cloud computing paradigm, with decentralized variants appearing that are made closer to the network devices. Fog computing emerges as a new paradigm to solve the problems raised, extending the traditional architecture of cloud computing to the edge of the network. Despite the boom that fog computing has been gaining, there are challenges concerning its implementation in real scenarios, such as smart cities, where the heterogeneity and mobility of devices, as well as the scalability and interoperability required, are aspects that must be considered. The main objective of this paper is the presentation of a guide that guides how to implement this technology in smart cities. The proposed procedure is partially validated for a garbage collection use case in Old Havana, within the framework of a project of the National Program of Science, Technology and Innovation of Informatization.

RESUMEN Con el desarrollo de la Internet de las Cosas, el número de dispositivos conectados a Internet aumenta de forma exponencial, y ello trae consigo que crezcan enormemente la cantidad de datos a procesar en la nube, con un incremento considerable del tráfico, en detrimento de la calidad del servicio. Esto, unido a los requerimientos de baja latencia y movilidad de no pocas aplicaciones Web, ha provocado que aparezcan variantes de computación que se encuentren más cercanas a los dispositivos de la red. La Computación en la niebla (Fog computing) surge como un nuevo paradigma para dar solución a las problemáticas planteadas, con una extensión de la arquitectura tradicional de la computación en la nube hasta el borde de la red. A pesar del auge que ha cobrado la computación en la niebla, existen desafíos en relación a su implementación en escenarios reales, como las ciudades inteligentes, donde la heterogeneidad y movilidad de los dispositivos, así como la escalabilidad e interoperabilidad requerida, son aspectos que deben ser considerados. En función de ello, en este artículo se presenta un procedimiento que orienta cómo implementar esta tecnología en las ciudades inteligentes. A modo de validación parcial, el procedimiento propuesto se aplica al caso de uso de recogida de basura en un escenario de La Habana Vieja, como parte de un proyecto del Programa Nacional de Ciencia, Tecnología e Innovación de Informatización.

Resumen Los algoritmos clásicos de segmentación de imágenes explotan la detección de similitudes y discontinuidades en diferentes señales visuales, para definir regiones de interés en imágenes. Sin embargo, debido a la alta variabilidad e incertidumbre en los datos de imagen, se dificulta generar resultados acertados. En otras palabras, la segmentación basada solo en color a menudo no es suficiente para un gran porcentaje de escenas reales. Este trabajo presenta una nueva estrategia de segmentación multi-modal que integra señales de profundidad y apariencia desde imágenes RGB-D, por medio de una representación jerárquica basada en regiones, es decir, un árbol de segmentación multi-modal (MM-tree). Para ello, la imagen RGB-D es descrita de manera complementaria por diferentes mapas de segmentación. A partir de la imagen de color, se implementa un árbol de segmentación de color (C-tree) para obtener mapas de segmentación y sobre-segmentación. Desde de la imagen de profundidad, se derivan dos mapas de segmentación independientes, los cuales se basan en el cálculo de primitivas de planos y de bordes 3D. Seguidamente, un proceso de fusión jerárquico de regiones permite agrupar de manera local los mapas obtenidos anteriormente en el MM-tree. Por último, el nivel superior emergente del MM-tree integra coherentemente la información disponible en los mapas de profundidad y apariencia. Los experimentos se realizaron con el conjunto de imágenes RGB-D del NYU-Depth V2, evidenciando resultados competitivos, con respecto a los métodos de segmentación del estado del arte. Específicamente, en las imágenes de prueba, se obtuvieron puntajes promedio de 0.56 en la medida de Segmentation Covering y 2.13 en Variation of Information.

Abstract Classical image segmentation algorithms exploit the detection of similarities and discontinuities of different visual cues to define and differentiate multiple regions of interest in images. However, due to the high variability and uncertainty of image data, producing accurate results is difficult. In other words, segmentation based just on color is often insufficient for a large percentage of real-life scenes. This work presents a novel multi-modal segmentation strategy that integrates depth and appearance cues from RGB-D images by building a hierarchical region-based representation, i.e., a multi-modal segmentation tree (MM-tree). For this purpose, RGB-D image pairs are represented in a complementary fashion by different segmentation maps. Based on color images, a color segmentation tree (C-tree) is created to obtain segmented and over-segmented maps. From depth images, two independent segmentation maps are derived by computing planar and 3D edge primitives. Then, an iterative region merging process can be used to locally group the previously obtained maps into the MM-tree. Finally, the top emerging MM-tree level coherently integrates the available information from depth and appearance maps. The experiments were conducted using the NYU-Depth V2 RGB-D dataset, which demonstrated the competitive results of our strategy compared to state-of-the-art segmentation methods. Specifically, using test images, our method reached average scores of 0.56 in Segmentation Covering and 2.13 in Variation of Information.

This paper examines the feasibility of using commercial out-of-the-box reconfigurable field programmable gate array (FPGA) technology and the open computing language (OpenCL) framework to create an efficient Sobel edge-detection implementation, which is considered a fundamental aspect of image and video processing. This implementation enhances speedup and energy consumption attributes when compared to general single-core processors. We created the proposed approach at a high level of abstraction and executed it on a high commodity Intel FPGA platform (an Intel De5-net device was used). This approach was designed in a manner that allows the high-level compiler/synthesis tool to manipulate a task-parallelism model. The most promising FPGA and conventional implementations were compared to their single-core CPU software equivalents. For these comparisons, local-memory, pipelining, loop unrolling, vectorization, internal channel mechanisms, and memory coalescing were manipulated to provide a much more effective hardware design. The run-time and power consumption attributes were estimated for each implementation, resulting in up to 37-fold improvement of the execution/transfer time and up to a 53-fold improvement in energy consumption when compared to a specific single-core CPU-based implementation.CATEGORIES: • Hardware ~ Reconfigurable logic applications

A evolução das redes de telecomunicações tem promovido o desenvolvimento de novas aplicações para dispositivos móveis. Algumas destas aplicações exigem requisitos computacionais e energéticos que vão para além das capacidades dos dispositivos móveis. Neste contexto, pode ser utilizada a arquitetura Mobile Cloud Computing (MCC), que permite executar as aplicações em datacenters na cloud e aliviar o processamento nos dispositivos móveis. No entanto, algumas aplicações mais exigentes, e.g. interativas e de tempo real, são mais sensíveis ao atraso no processamento e comunicação da informação. Para estas aplicações, a arquitetura Mobile Edge Computing (MEC) pode ser utilizada como uma tecnologia intermédia que disponibiliza recursos computacionais e de armazenamento a partir da periferia da rede. Este artigo apresenta um estudo que avalia o desempenho das arquiteturas MCC e MEC na execução de duas aplicações tomadas como representativas do espectro das aplicações interativas, de tempo real e de processamento intensivo: o Fluid e o FaceSwap. São apresentados resultados que permitem quantificar o desempenho destas arquiteturas em diferentes circunstâncias.

Telecommunication networks evolution is driving the development of new applications for mobile devices. Some of these applications are resource-intensive and push computational and energy demands of mobile devices beyond the mobile hardware capabilities. In this context, Mobile Cloud Computing (MCC) architecture emerges as a solution for offloading mobile devices that allows to execute these applications in cloud datacenters thus reducing the processing demand in mobile devices. However, more demanding applications, e.g. interactive and real-time applications, are sensitive to processing and communications delay. For these applications, Mobile Edge Computing (MEC) can be used as an intermediary technology, providing computing and storage resources in the network edge. This paper presents a study carried out to evaluate the performance of MEC and MCC architectures when executing two applications, Fluid and FaceSwap, representative of real time and computing intensive applications. A set of scenarios were designed to quantify the performance of these architectures in different settings.

Abstract: Increasingly growing Next-generation sequencing requires large-scale computing resources to handle the huge amount of data produced. The Cloud computing paradigm readily handles huge data but the core issue with this paradigm is transfer of enormous data to and from cloud computers due to limited bandwidth which lies in the centralized nature of a Cloud computing architecture that is located far away from users. An architecture where computing power is distributed more evenly throughout the network is the way to combat this problem. The architecture should drive the processing capacity towards the edge of the network, closer to the source of the data. For this propose Fog computing offers a promising solution to move computational capabilities closer to the data generated and will be the solution to gain traction in genomics research. We propose a novel Collaborative-Fog (Co-Fog) model that adopts the Fog and Cloud computing paradigms to manage huge genomic data sets and to enable understanding of how key stakeholders can manage the interaction and collaboration. The present work describes the Co-Fog model that promises increased performance, energy efficiency, reduced latency, faster response time, scalability, and better localized accuracy for future large-scale collaborations in genomics.

Resumen: La secuenciación de la próxima generación es cada vez más creciente y requiere recursos informáticos a gran escala para manejar la enorme cantidad de datos producidos. El paradigma Cloud computing fácilmente maneja datos enormes, pero el problema central con este paradigma es la transferencia de datos enormes hacia y desde las computadoras en cloud debido al ancho de banda limitado que radica en la naturaleza centralizada de la arquitectura Cloud computing la cual está localizada lejos de los usuarios. Una arquitectura donde la potencia de computación se distribuya de manera más uniforme en toda la red es una forma de combatir este problema. La arquitectura debe llevar la capacidad de procesamiento hacia el borde de la red, más cerca de la fuente de los datos. Para esta propuesta, Fog computing ofrece una solución prometedora para acercar las capacidades computacionales a los datos generados y será la solución para ganar fuerza en la investigación genómica. Proponemos un nuevo modelo llamado Collaborative-Fog (Co-Fog) que adopta los paradigmas Fog y Cloud computing para administrar grandes conjuntos de datos genómicos y para permitir la comprensión de cómo las partes interesadas pueden gestionar la interacción y la colaboración. El presente trabajo describe el modelo Co-Fog que promete un mayor rendimiento, eficiencia energética, menor latencia, tiempo de respuesta más rápido, escalabilidad y una mejor precisión localizada para futuras colaboraciones a gran escala en la genómica.

Abstract This paper presents an edge-based parallel code for the data computation that arises when applying one of the most popular electromagnetic methods in geophysics, namely, the controlled-source electromagnetic method (CSEM). The computational implementation is based on the linear Edge Finite Element Method in 3D isotropic domains because it has the ability to eliminate spurious solutions and is claimed to yield accurate results. The framework structure is able to exploit the embarrassingly-parallel tasks and the advantages of the geometric flexibility as well as to work with three different orientations for the dipole, or excitation source, on unstructured tetrahedral meshes in order to represent complex geological bodies through a local refinement technique. We demonstrate the performance and accuracy of our tool on the Marenostrum supercomputer (Barcelona Supercomputing Center) through scaling tests and canonical tests, respectively.