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 <= /o:p>

www.cienciadigital.org=

 
                                                 =                             =                                      ISSN: 2602-8085=

                                                 =                       Vol. 3, N°3.4, p. 327-341, septiembre, 2019= <= ![endif]>

 

Evaluación del rendimiento de la transferencia de imágenes en aplicaciones IoT entre l= os lenguajes de programación Java y Python

Evaluation of image transfer performance in IoT applications between the Java and Python programming languages

 

= Paul Xavier Paguay Soxo.[1]= , Rosman José Paucar Córdova.[2], Angel Patricio Flores Orozco.[3] =

Recibido: 14-07-2019 / Revisado: 23-07-2019 /Aceptado: 15-08-2019/ Publicado: 10-09-2019

 

Abstract          =            DOI: https://doi.org/10.33262/cienciadigital.v= 3i3.4.878

The purpose of this research project was to compare the performance of the image transfer process using a reduced plate (comput= er board) between the Java and Python programming languages ​​appl= ied to the video surveillance system implemented within a public institution su= ch as the District Department 07D04 Balsas - Marcabeli - Pińas of the Ministry= of Education. For the evaluation, internal and external measures and metrics w= ere established, with a sample of 375 images of a population of 14400, an error= of 0.05% resulting in the following, for internal metrics response time was obtained 258% in favor of Java, then in terms of processing time 13.88% in favor of Java, likewise, in terms of external metrics, the use of CPU 86% in favor of Python, the use of RAM, 34% in favor of Python and for Java bandwi= dth use 20.56 times less KiloBytes than Python, finally applying multicriteria analysis with the NAIADE method it is obtained that with a positive ranking= of 0.7 “Java is Better than Python”, so it is evident that it exists a signifi= cant difference in the performance of image transfer in Internet-based video surveillance systems of Things between the Java and Python programming languages.

Keywords: Image transfer, Phyton, Java, performance, video surveillance, redu= ced plate.

Resumen

El propósito del presente proyecto de investigación fue realiz= ar la comparativa del rendimiento del proceso de transferencia de imágenes utilizando una placa reducida (placa computadora) entre los lenguajes de programación Java y Python aplicado al sistema de video vigilancia implemen= tado dentro de una institución pública como lo es la Dirección Distrital 07D04 Balsas – Marcabeli – Pińas del Ministerio de Educación. Para la evaluación = se establecieron medidas y métricas tanto internas como externas, con una mues= tra de 375 imágenes de una población de 14400, un error de 0.05% dando como resultado los siguiente,  para las métricas internas tiempo de respuesta se obtuvo un 258% a favor de Java, lu= ego en cuanto al tiempo de procesamiento 13.88% a favor de Java, así mismo, en cuanto a las métricas externas, el uso de CPU un 86% a favor de Python, el = uso de RAM, un  34% a favor de Python y= para el ancho de banda Java usa 20.56 veces menos KiloBytes que Python, finalmen= te aplicando el análisis multicriterio con el método de NAIADE se obtiene que = con un ranking positivo de 0.7 “Java es Mejor que Python”, por lo cual se evide= ncia que existe una diferencia significativa del rendimiento de la transferencia= de imágenes en sistemas de videovigilancia basada en Internet de las Cosas ent= re los lenguajes de programación Java y Python.

= Palabras Clave: Tr= ansferencia de imágenes, Phyton, Java, rendimento, video vigilancia, placa reducida.

INTRODUCCIÓN

A lo largo de la historia se han desarrollado inventos o tecnologías que han cambiado la vida de los seres humanos. Los sistemas de video vigilancia basados en la “Tecnología Emergen= te” Internet de las Cosas (IoT), son aplicaciones ampliamente utilizadas en la actualidad en las organizaciones de cualquier ámbito, estos sistemas permit= en obtener información como imágenes o videos en tiempo real de un lugar a tra= vés de una computadora personal o un Smartphone que se conecta a una Computador= a de Placa Reducida (en inglés: Single Board Computer o SBC). Una de las opciones que más avances ha tenido en este campo son las placas Raspberry, que para = el 2020 habrá 50.000 millones de dispositivos “inteligentes” conectados (Sala= zar & Silvestre, 2016) y que según  (Wai, Jegatheesan, & Chee, 2015)  Raspberry puede ser el futuro de l= as aplicaciones inteligentes (smart) y las comunicaciones cliente - servidor.<= o:p>

Pero en sí żQué es el Internet = de las Cosas o por sus siglas en inglés IoT? Según (Evans, 2011), del Internet = Business Solutions Group (IBSG) de Cisco, IoT es “sencillamente el punto en el tiemp= o en el que se conectaron a Internet más cosas u objetos que personas”, lo cual ocurrió entre los ańos 2008 y 2009, y según estudios (Evans, 2011).

Los medios de comunicación para= el envío de información soportados por la Placa Raspberry, dependen de los mód= ulos que se le puedan adaptar, sin embargo, la conexión LAN viene instalada por defecto en su versión Pi B, por otro lado, esta se ve limitada por la neces= idad de instalar conexiones de red, por tal motivo la opción de conexión inalámb= rica Wi-Fi (Estándar 802.11b) ofrece una ventaja en cuanto a la viabilidad de instalación.

Los sistemas de videovigilancia tienen que permitir visualizar la información en tiempo real por lo tanto requieren del envío constante de información, como lo indican (Leir= a, Vega, González, & Aracil, 2014) para los casos= de servidores streaming, en el que se transfieren por la red de datos, grandes cantidades de fotogramas por segundo, que en lo posterior formarán parte del video resultante. Esto por ende resulta en la necesidad de sistemas que en = su estructura apliquen plataformas con un adecuado rendimiento que no declive = el servicio en el tiempo, tal como lo indica (Armonk, NY Paten= te nş 20150220336, 2015), ya que de esto dependerá la calidad del Servicio (Vidal-Silva, Madariaga, & Solís, 2017). En el mismo sentido, según (Medina, Pruebas de rendimiento TIC, 20= 14), para realizar= las pruebas de rendimiento, un criterio de evaluación se basa en métricas exter= nas e internas, entre las métricas externas el mismo establece indicadores como: Número de peticiones atendidas por unidad de tiempo, tiempo medio de respue= sta, número medio de errores, y entre las internas están: Uso medio de recursos = como CPU y RAM.

Como en la mayoría de sistemas informáticos, existen diversas opciones de lenguajes de programación para el desarrollo aplicaciones con placas reducidas, habiendo opciones tanto de software propietario y libre, donde los lenguajes de programación de softwa= re libre proveen una gran ventaja en cuanto a los costos de implementación. Se= gún el sitio web (TIOBE, 2019) encargada de analizar los lenguajes de programación más utilizados a nivel mundial ubica= a los lenguajes de programación Java y Python entre los cuatro primeros puest= os, siendo ambas opciones las más utilizadas en los prototipos para la el desarrollo de sistemas basados en IoT. Algunos proyectos de este tipo que se pueden citar tenemos por ejemplo el de (Llanos, Naula, &= amp; Valdiviezo, 2014), (Ferdoush & L= i, 2014) que utilizan el lenguaje Python mientras en los de = (Valeriu , Florin, & Adrian-Viorel , 2014) y (Kryvyy , Serhiy,= & Ruslan, 2015) ,utiliza el lenguaje Java para la implementación de soluciones IoT con una placa Raspberry. Con este antecedente se plantea la siguiente interrogante: żCuál de los dos lenguajes de programación Java o Pyhton ofrece un mejor rendimiento en la transferencia de imágenes en un Sistema de Videovigilancia utilizando equipamiento de Internet de las Cosas (IoT) como una placa Raspb= erry Pi?

La estructura del presente trab= ajo se detalla a continuación:

ˇ    La introducción, problema, objetivos generales y específicos, justificación e hipótesis planteada.

ˇ    Marco Referencial, donde se describen conceptos fundamentales de seguridad de información, criptografía y algoritmos criptográficos, arquitectura cliente/servidor, plataformas de desarrollo y modelos de calidad de softwar= e

ˇ    Diseńo de la Investigación, donde se describe el diseńo, tipo, métodos y enfoque d= e la investigación, la población, variables e indicadores, matriz de contingenci= as, parámetros y herramientas, el método FURPS, prototipos y software utilizado= .

ˇ    Resultados y discusión, en el cual se detalla la recolección y análisis de datos envas= e a indicadores establecidos, además se determina el rendimiento total efectivo= y la comprobación de la hipótesis.

=  

= DESARROLLO

= Marco Teórico Referencial

Como antecedentes a la presente investigación se pueden mencionar los siguientes trabajos:

Artículo Científico: “Utilización de la Microcomputadora Raspberry Pi con Capacidad de Comunicación Wi-Fi para la captura de Imágenes mediante Cámara y Almacenamiento de Información en Base= de Datos Externa”, presentado por los autores (Naula, Llanos, & Valdiviezo, 2014)= en la que conc= luyen que el prototipo desarrollado puede capturar imágenes de una cámara y envia= r a un gestor de base de datos.

La siguiente investigación se titula “Wireless Sensor Network System Design using Raspberry Pi and Arduino for Environmental Monitoring Applications”, propuesta por los autores (Li, Ferdoush, 2014), entre los resultados obtenidos es que se desarrolló un sistema de sensores para el en= vío de información mediante la utilización de la placa Raspberry PI, en la que = se concluye que este tipo de sistemas son atractivos debido a que tienen características como:  bajo costo, compacto, escalable, fácil de personalizar, fácil de desplegar y mantener.<= o:p>

El artículo titulado “Raspberry Pi as Internet of Things hardware: Performances and Constraints”, de los autores = (Maksi= mović, Vujović, Davidović, Milošević , & Perišić, 2014), en la cual concluyen que “La placa de Raspberry Pi es una opción muy económica, con soporte con un extenso número de periféricos de entrada y salida. La red de comunicación.” Así también que: “Ańadiendo el módulo Wi-Fi y proveyendo de acceso a Internet, es posible configurarlo para una comunicación con control remoto, lo cual Raspberry Pi, se hace muy adecuado para aplicaciones con concepto de IoT”.

Por otro lado (Prechelt, 2000)<= !--[if supportFields]>en su artículo = “An empirical comparison of C, C++, Java, Perl, Python, Rexx, and Tcl for a search/string-processing program” concluye que existen varias ventajas en l= as diferentes tareas entre los lenguajes analizados, resaltando la velocidad d= el lenguaje Perl entre lenguajes de scripts,  así también (Cunningham, Subrahmanian, & Wester= berg, 2010) en su artículo “User-centered evolutionary software development using Python and Java”, resaltan características como: “Java es un lenguaje de tipo está= tico verificado que ofrece rendimiento, robustez y modularidad como tal, mientras que Python es un lenguaje de tiempo de ejecución controlado que ofrece prototipado rápido, modificación dinámica en tiempo de ejecución y evaluaci= ón retardada”

Con estos antecedentes cabe mencionar = que una comparación entre los dos lenguajes materia de la investigación, no se = ha encontrado en el ámbito de la transferencia de imágenes utilizando una placa reducida, por lo que es necesario analizar este tema.

<= ![if !supportLists]>ˇ&nb= sp;        Raspberry Pi

Según (Jipsion, 2017) Raspberry Pi,  “es un ordenador de placa reducida o (p= laca única) (SBC) de bajo costo, desarrollado en Reino Unido por la Fundación Raspberry Pi, con el objetivo de estimular la enseńanza de ciencias de la computación en las escuelas.”, este ordenador entre varias ventajas  se tiene: el reducido tamańo, lo cual le permite una portabilidad así como el menor consumo de energía necesario par= a su funcionamiento, en la actualidad la utilización de este dispositivo se ha masificado sobre todo en aplicaciones de domótica, así como en el área del internet de las cosas.

<= ![if !supportLists]>ˇ&nb= sp;        Raspbian

(Raspbian) define a su si= stema operativo como: “Es un sistema operativo con licencia libre basado en Debia= n, optimizado para hardware Raspberry Pi. Un sistema operativo es el conjunto = de programas básicos y utilidades que hacen al Raspberry Pi iniciar.”

ˇ&nb= sp;   Sockets

Según (Fúquene, 2= 011)<= span lang=3DES-EC style=3D'font-size:12.0pt;font-family:"Times New Roman",serif'= >, un socket es “un método para la comunicación entre un programa del cliente y un programa del servidor en una red. Un socket se define como el punto final en una conexión. Los sockets se crean y se utilizan con un sistema de peticion= es o de llamadas de función a veces llamados interfaz de programación de aplicac= ión de sockets (Application Programming Interface - API)”.

La mayoría de los lenguajes de programación implementan en sus bibliotecas de clases, métodos aplicando los conceptos de los sockets, hay que recalcar que este método es de nivel más = bajo de programación, y sirve de base para las demás implementaciones de framewo= rks de nivel superior.

ˇ&nb= sp;   Rendimiento de los sistemas=

Según (Molero, X.; Ruiz, C.; Rodeńo, M., 2004) la tendenc= ia actual dentro del campo de la evaluación de prestaciones se orienta principalmente a utilizar índices que tienen en cuenta el tiempo de respues= ta en una PC de un conjunto programas de prueba o evaluación (benchmarks).

ˇ&nb= sp;   Tiempo de respuesta

Para = , optimizar los tiempos de respuestas tiene un enorme impacto en la experienc= ia de los usuarios, navegar de manera fluida y rápida en los sistemas informát= icos hace que el usuario se sienta cómodo y se quede más tiempo.

Cuando los tiempos de respuesta del sistema caen más allá de un límite, la interac= ción fluida desaparece y la eficiencia decae significativamente.

Para el presente estudio, el tiempo de respuesta es el tiempo medido en milisegundos que se necesita para que la petición de video por parte del cliente sea atendida por el servidor.<= /o:p>

 =

ˇ&nb= sp;   Tiempo de Procesamiento

El tiempo de procesamiento es el tiempo total que se requiere para completar una transacción, este inicia desde el momento que construye la petición, hasta cuando recibe la respuesta y se procesa la información.

ˇ&nb= sp;   Métricas externas

Para el presente trabajo, el consumo de recursos viene incidido por subíndices como: El porcentaje de Us= o de CPU, porcentaje de Uso de RAM, y Por el porcentaje de Uso del Ancho de Band= a, se descarta el Uso de Disco por la baja influencia en el proceso de transmi= sión de los fotogramas.

Marco Metodo= lógico

= El presente tipo de trabajo de investigación es cuasi-experimental. En donde se propone diseńar un sistema de comunicación desarrollado en los lenguajes de programación Java y Python y probar su rendimiento del proceso de transfere= ncia de imágenes utilizando una placa reducida para la implementación de un sist= ema de video vigilancia.

= El presente trabajo investigativo tomará un enfoque cualitativo cuantitativo, = para establecer indicadores que permitan medir a través de valores adjetivos y valores numéricos los resultados obtenidos, para posteriormente analizarlos= con métodos estadísticos

La población en la presente investigación la conforma la cantidad de transferencia de imágenes que se realizarán en una hora, según varias empresas que implementan sistemas de videovigilancia como (Axis Comunications, 2018) o (SecuraMe, 2018), establece= n que la resolución del video dependerá de la cantidad de fotogramas por segundo (FPS) que se transfieran, en la cual los valores van desde 3 FPS hasta los = 25 FPS, para el presente estudio y lue= go de pruebas realizadas se ha establecido el valor de 4 FPS con lo cual se garan= tiza una correcta visualización del video con relación de aspecto(ratio) 16:9, resolución de 360 pixeles y reducción de flicker(parpadeo) de 50Hz, obteniéndose una población de 14.400 imágenes a ser transferidas.<= /o:p>

ˇ&nb= sp;   Selección de la muestra

Para encontrar la muestra se aplicará la siguiente fórmula de muestreo (Aguilar-Barojas, 2005):

Dónde:

N=3DTotal de la población<= /o:p>

 

Luego de los cálculos se establece como muestra para el estudio un total de 375 transferencias de imágenes a realizar en cada escenario.<= /p>

ˇ&nb= sp;   Análisis de Indicadores Individual

Para el proceso de análisis de = la información recolectada se utilizó = el método estadístico de la Prueba Z, con un total de 375 imágenes (poblac= ión desconocida) generando los resultados mediante el software SIAE 2.0, el mis= mo que permitió verificar si existe o no una diferencia significativa en cada = uno de los indicadores.

ˇ&nb= sp;   Análisis Multicriterio

Según (Villacís Cruz, 2= 005), existen algun= os modelos para la elección de un procedimiento de agregación o análisis de los criterios. Entre los básicos se encuentra el método NAIADE (Novel Approach to Imprecise Assessment= and Decisión Environments), el mismo que combina los elementos del modelo de superación, pero agregando funciones continuas y monótonas de credibilidad = de la indiferencia y preferencia, para mejorar la calidad de selecciones y rankeos, evitando que pequeńas variaciones de preferencia cambien el orden original de selección. Esto significa que el modelo permite evaluar alternativas de acuerdo a criterios, tomando en cuenta preferencias de dist= inta intensidad, para evitar que se seleccionen alternativas por diferencias pequeńas en su calificación. El NAIADE, desarrollado por Munda (Munda, 2002) permite también la introducción de datos cualitativos, cuantitativos difuso= s, determinísticos y estocásticos, encajando así de forma idónea para el prese= nte proyecto ya que se cuenta con varios criterios que inciden en el Rendimiento Total. El software genera un ranking de alternativas las cuales son finitas (método discreto) y no permite dar peso a los criterios, es decir otorgando= la igual 'importancia' a los mismos.

ˇ&nb= sp;   Evaluación del rendimiento<= /span>

La evaluación de rendimiento de sistemas informáticos propuesta por (Medina, 2014) en su artículo “Pruebas = de rendimiento”, establece dos tipos de pruebas: internas y externas, las inte= rnas que corresponden a tiempos de respuesta y procesamiento, mientras que las externas que guardan relación con el porcentaje de uso de CPU, RAM, y ancho= de banda. Esta información se puede observar en la tabla 1.

<= span lang=3DES-EC style=3D'font-size:12.0pt;font-family:"Times New Roman",serif; mso-ansi-language:ES-EC;font-weight:normal'> 

Tabla <= /span>1. Indicadores del rendimiento

Métrica

Indicador

Unidad de Medida

Internas

Tiempo de Respuesta

Milisegundos

Tiempo de Procesamiento

Milisegundos

Externas

%Uso de CPU

Porcentaje

%Uso de RAM

Porcentaje

Ancho de Banda Utilizado

KiloBytes

=  

En varias investigaciones como = por ejemplo (Alonso Forcelledo & Cańete González, 2006) y la de (Murillo Morera &= amp; Caamańo Plini, 2010) se analizan los indicadores del rendimiento de sistemas como, el uso de CPU= y RAM pertenecientes a las métricas externas, sin especificar su pesos de ponderación, así como en la investigación de (Ghodsi, y otros) donde se hace = un análisis por separado de tres indicadores, Uso de CPU (M. Externa), Uso de = RAM (M. Externa) y Tiempo de Respuesta (M. Interna), y de igual manera no se de= fine ponderaciones. Para la presente investigación de la misma manera no se establece que uno u otro tenga más peso dado el escenario donde se lleva a = cabo el experimento, ya que por un lado las métricas internas como las externas = son de elevada importancia, la primera por los tiempos que se utiliza para llev= ar a cabo las tareas y las métricas externas por la cantidad de recursos limitad= os con los que cuenta una computadora de placa reducida (Raspberry). Para la consolidación de la información se utilizará entonces un método discreto multicriterio con una matriz de impacto (o evaluación) que puede incluir medidas nítidas, estocásticas o difusas del rendimiento de una alternativa = con respecto a un criterio de juicio.

<= ![if !supportLists]>ˇ&nb= sp;        Escenario

El escenario propuesto refleja el ento= rno en producción donde se ejecutará el sistema de videovigilancia, que es en la Dirección Distrital 07D04 Balsas – Marcabeli – Pińas del Ministerio de Educ= ación, donde se cuenta con una red Wireless (IEEE 802.11b); por lo que la incorporación de uno o varios nuevos dispositivos “inteligentes” como la pl= aca de Raspberry (haciendo uso de virtu= des como tamańo, versatilidad, robustez y conexión inalámbrica), se une a la red existente sin inconvenientes, evitando así la incorporación de conexion= es por medio de cables, lo cual a más de alterar la estética existente del lug= ar, la incorporación de conexiones y cableado nuevo, canaletas, patch cord, pat= ch panels, ordenadores, etc., garantizándose la trasmisión normal por medio inalámbrico, debido a que se cuenta con oficinas separadas por modulares, paredes y techos falsos y ancho de banda de 10 MB por fibra óptica de plan corporativo con compartición 1:1.

Para realizar las pruebas comparativas del rendimiento, se ha diseńado un escena= rio en donde van a interactuar dos procesos, un Servidor (Raspberry Pi) y un cliente (PC), los mismos que van a estar conectados mediante una red Wirele= ss segmentada, con una velocidad de 4MB, el cual se puede observar gráficament= e en la figura 1.

Figura 1. Escenario de pruebas

<= ![if !supportLists]>ˇ&nb= sp;        Prototipo

Los prototip= os desarrollados tanto en Java y Python que serán instalados en el Servidor Raspberry Pi contienen las mismas funcionalidades:

= ˇ      =    Capturar imágenes mediante una cámara integrada al Raspberry

= ˇ      =    Enviar los fotogramas capturados media= nte la utilización de sockets a través de la red de datos Wireless.<= /span>

La aparienci= a del hardware del sistema será como el de la figura 2.

3D"Image

Figura 2. Apariencia del sistema de videovigilancia

= En la tabla 2 se detalla el hardware utilizado en el experimento.

&n= bsp;

Tabla 2. Hardware del sistema

Características

Cliente

Servidor

(Raspberry Pi)

Marca=

Dell

Raspberry Pi+

Procesador=

Intel i7

Broadcom BCM2835, Single-Core a 700MHz

RAM

16GB

512MB

Disco Duro=

1TB

Micro SD 4GB

Internet

Intel Dual B= and Wireless AC 3160

150Mbps Wireless N Nano USB Adapter

=  

= En la tabla 3 se observa el software utilizado para las pruebas.

&n= bsp;

Tabla 3. Software del sistema

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>Características

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>Cliente

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>Servidor

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>(Raspberry Pi)

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'> 

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>Java

<= span lang=3DES-EC style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f'>Python

Sistema Operativo<= /b>

Win10

Raspbian Kernel 4.14

Entorno de Desarrollo

Netbeans

Netbeans

BeeWare

Plataforma

Java Se 8

Oracle JDK 1.7

Python  3.0

=  

=  

=  

=  

=  

=  

=  

=  

=  

= Análisis de Resultados

A continuación, se realiza el análisis correspondiente al indicador “Métricas Internas”, el mismo que se calcula a partir de los índices Tiempo de Respue= sta y Tiempo de Procesamiento que se realizó para el envío de 375 imágenes tant= o en Java como en Python.

En la figura= 3, se visualiza de manera gráfica la media del tiempo de respuesta en milisegundo= s de cada proceso.

Figura 3. Tiempo de respuesta

En la figura 4, se visualiza de manera gráfica la media del tiempo de procesamiento en milisegundos de cada proceso.

Figura 4. Tiempo de Procesamiento

 

En la figura 5, se visualiza de manera gráfica la media del porcentaje de uso de CPU de cada proceso.

Figura 5. Porcentaje de uso de CPU=

En la figura 6, se visualiza de manera gráfica la media del porcentaje de uso de RAM de cada proceso.

 

Figura 6. Porcentaje de uso de RAM

=  

En la figura 7, se visualiza de manera gráfica la media del Ancho de Banda de cada proceso.

Figura 7. Uso de ancho de banda

 <= /o:p>

Para comprob= ar si existe o no diferencia entre los dos lenguajes de programación a la hora= de ejecutar el trabajo de transferencia de imágenes entre una placa computador= a y un servidor, se establecieron dos hipótesis, la hipótesis alternativa y nul= a.

ˇ      =    Ha=3D Existe una diferencia significativa del rendimiento de la transferencia de imágene= s en sistemas de videovigilancia basada en Internet de las Cosas entre los lengu= ajes de programación Java y Python.

Utilizando el método NAIADE de Análisis Multicriterio, se establece la siguiente Matriz de Impacto que se observa e= n la tabla 4.

<= span lang=3DES-EC style=3D'font-size:12.0pt;font-family:"Times New Roman",serif; mso-ansi-language:ES-EC;font-weight:normal'> 

Tabla 4. Matriz de impacto

Dimensiones y Varia= bles

Unidad de Medida

Tipo

Objetivo=

Java

Python

Tiempo de Respuesta

Milisegundos

Cuantitativa

Minimizar

74,565

267,435<= /span>

Tiempo de Procesamiento

Milisegundos

Cuantitativa

Minimizar

249,229<= /span>

283,832<= /span>

%Uso de CPU

Porcentaje

Cuantitativa

Minimizar

69,197

37,202

%Uso de RAM

Porcentaje

Cuantitativa

Minimizar

10,814

8,066

Ancho de Banda

Kilobytes

Cuantitativa

Minimizar

417,301<= /span>

8579,52<= /span>

Fuente: Trabajo de campo

Elaborado por: Los autores

 

La matriz de impacto se ingresó en el software NAIADE como se observa en la fi= gura 8.

Figura 8. Matriz de impacto en el Software NAIADE

<= o:p> 

Una vez ejecutado el ejercicio, lo resultados que arroja NAIADE con un alfa (α) de 0,5; se obtiene un ran= king Φ+ de 0.7 selecciona al lenguaje Java como mejor que Python. En la fig= ura 9 se puede observar el resultado de la matriz de impacto.=

 

Figura 9. Resultado de la Matriz de Impacto

 

Se puede observar que con un grado de confianza de 0.6744 (Ver figura 10) Java es “Mejor que Python en cuanto al rendimiento en la transferencia de imágenes”

 

Figura 10. Grados de verdad

El resultado concuerda con la hipótesis alternativa (Ha) del estudio “Existe una diferencia significativa del rendimiento de la transferencia de imágenes en sistemas de videovigilancia basada en Internet de las Cosas entre los lenguajes de programación Java y Python.”, en referencia a que, de acuerdo con los indicadores introducidos y bajo los parámetros del modelo, la evaluación dice que en términos de rendimiento en la transferencia de imágenes en el sistema de videovigilanci= a el lenguaje Java tuvo mejor desempeńo que el lenguaje Python.

 

= CONCLUSIONES

ˇ      =    Los lenguajes de programación Java y Python por varios ańos se han encontrado en el listado de los más utilizado= s a nivel mundial (BV, 2016),  gracias a sus características como uso libre, seguridad, robustez, escalabilidad, multiplataforma, lo cual los hace atractivos para los desarrolladores de sistemas informáticos, no solo en el campo aplicaciones = web, escritorio o móvil sino también como en el presente trabajo en el ámbito de= la videovigilancia por medio del uso de dispositivos inteligentes basados en el Internet de las Cosas, a su vez se encontró una diversa documentación y recursos como las librerías sarxos, webcam, Webcam (En Java) y Pygame (En Python) que permitieron llevar a cabo el desarrollo de los prototipos.=

<= span lang=3DES-EC style=3D'font-size:12.0pt;font-family:Symbol;mso-fareast-font-= family: Symbol;mso-bidi-font-family:Symbol'>ˇ      =    Luego del análisis correspondiente se obtuvo que, para las métricas internas tiempo de respuesta se obtuvo una diferencia porcentual de 258.6% a favor de Java, luego en cuanto a la tiemp= o de procesamiento 13.88% a favor de Java, así mismo, en cuanto a las métricas externas, el uso de CPU un 86% a favor de Python, el uso de RAM, un  34% a favor de Python  y para el ancho de banda un  Java ocupa 20.56 veces menos KB que Pyt= hon, finalmente aplicando el análisis multicriterio con el método de NAIADE se obtiene que con un ranking positivo de 0.7 “Java es Mej= or que Python”.

ˇ&nb= sp;        Es importante recalcar que hubo difere= ntes resultados en la evaluación de cada indicador, por lo cual será indispensab= le definir adecuadamente las prioridades que permitan determinar la mejor opci= ón a la hora de decidir por uno u otro lenguaje para la implementación

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 = ;

3Deditorial1.png<= /span>

 

 

 

PARA CITAR EL ARTÍCULO INDEXADO.=

 

 <= /o:p>

Paguay Soxo, P., Paucar Córdova,= R., & Flores Orozco, A. (2019). Evaluación del rendimiento de la transferen= cia de imágenes en aplicaciones IoT entre los lenguajes de programación Java y Python. Ciencia Digital3(3.4.), 327-341= . https://doi.org= /10.33262/cienciadigital.v3i3.4.878

 

 <= /o:p>

3Deditorial1.png

El artículo que se publica es de exclu= siva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Ciencia Digital.

El artículo qu= eda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Ciencia Digital.

 

3D"logo_catalog= 3D"Logo_cicncia_digital
 

 

 <= /o:p>

 

 



[1] Escuela Superior Politécnica de Chimborazo, Facultad de Informática y Electrónica. Riobamba, Ecuador, ppaguay@espoch.edu.ec

[2]= Universidad Técnica de Machala, Machala, Ecuador. rpaucar@utmachala.edu.ec<= /span>

[3]Escuela Superior Politécnica de Chimborazo, Sede Morona Santiago, Macas, Ecuador, aflores@es= poch.edu.ec

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                                                 =                                                                  ISSN: 2602-8085=

                                                 =                       Vol. 3, N°3.4, p. 327-341, septiembre, 2019

 

Desarrollo &= amp; Crecimiento (Volumen Especial)                                             Página 239

 

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