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https://doi.org/10.37815/rt= e.v33n2.829

Artículos originales=

 

Análisis del rendimiento de los protocolos OLSR, DSDV y AODV en una Red MANET= <= /o:p>

Performance analysis of OLSR, DSDV and AODV protocols in a MANET Net= work=

 

Paúl= Astudillo1 https://orcid.org/0000-0001-8380-6103, Santiago González1 https://orcid.org/0000-0001-6604-889X, Christian Quinde1 https://orcid.org/0000-0002-9863-6941<= /p>

 

1Departam= ento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de Cuenca, Cuenca, E= cuador

paul.astudillop@ucuenc= a.edu.ec, = santiago.gonzalezm@ucu= enca.edu.ec, christian.quinde@ucuen= ca.edu.ec

 

Enviado:    &nbs= p;    2021/06/24

Aceptado:    &nb= sp;  2021/09/28

Publicado:      = 2021/11/30        =             &nb= sp;    

Resumen

3D"Cuadro

Este artículo presenta una propuesta de análisis y comparación entre los protocolos de enrutamiento proactivo OLSR y DSDV, y reactivo AODV en una red MANET. Se de= finen dos escenarios: el primero representando un equipo de fútbol atacand= o y el segundo defendiendo. Se varía el área de movimiento de los nodos para simular cada escenario. Para ello, se realiza una investigación sobre los problemas que presentan estos algoritmos de enrutamiento para redes Ad Hoc en diferentes ámbitos. De esta forma, obtener un panorama de las estrategias y soluciones que existen actualmente= . A partir de la información recopilada, se propone realizar una simulación utilizando NS3, para obtener resultados que se asemejen a= la realidad. La comparación se realiza a base de las métricas de throughput, PDR y Delay. En cada simulación, la velocidad de los nod= os y la posición inicial se ajustan según el comportamiento de un jugador de fútbol real. Los nodos se mueven en una trayectoria aleat= oria dentro del área específica, y uno de éstos envía datos a un nodo fijo situado en el borde del campo. El tráfico transmitido está caracterizado para simular datos biométricos realistas. A partir de los resultados, se concluye que AODV y DSDV tienen un mejor rendimiento que OLSR y el intervalo óptimo de transmisió= ;n es de 20 segundos. Además, con una potencia de transmisión de= 10 dBm se garantiza el 100% del throughput en el receptor.

 

Palabras clave: delay, NS3, mapa de calor, = PDR, throughput.

Abstract

This paper prese= nts an analysis and comparison between the proactive routing protocols OLSR and DSDV and the reactive routing protocol AODV in a MANET network. Two scenarios were defin= ed, the first one representing a soccer team attacking and the second one defending. To simulate each scenario the area of movement of the nodes (pla= yers) was varied. For this purpose, an investigation was carried out on the probl= ems presented by these routing algorithms for Ad Hoc networks in different environments. In this way, an overview of the strategies and solutions that currently exist is obtained. Based on the information gathered, it is propo= sed to perform a simulation using NS3 in order to obtain results that resemble reality. The comparison is made based on throughput, PDR and Delay metrics.= In each simulation, parameters such as node velocity and initial position are adjusted according to the behaviour of a real soccer player. The nodes move= in a random trajectory within the specific area and one of them sends data to a fixed server node located at the edge of the area where the nodes move, simulating the bench where the receiver is located. The transmitted traffic= is characterized to simulate realistic biometric data. From the results, it is concluded that AODV and DSDV have better performance than OLSR and the opti= mum transmission interval is 20 seconds. In addition, with a transmit power of = 10 dBm, 100% throughput is guaranteed at the receiver.

 

Keywords: delay, heatmap, NS3, PDR, throughput.

 

Introducci&oa= cute;n

Una red ad = hoc móvil (MANET) es un tipo de sistema distribuido complejo que compren= de de nodos móviles inalámbricos que puedan autoorganizarse dinámicamente en topologías de red ad hoc temporales. Esto permite a las personas o dispositivos trabajar en Internet en áreas = sin infraestructura de comunicación preexistente (Kurn= iawan et al., 2020). En las redes ad hoc, los nodos no están familiarizados con la topología de sus redes, por el contrario, tienen que descubrirlo. Normalmente, un nuevo nodo anuncia su presencia y escucha los mensajes transmitidos por sus vecinos. Cada nodo aprende acerca de otros cercanos y cómo llegar a ellos. Para esto existen tres tipos de protocolos: Proactivos, Reactivos e Híbridos. = Este artículo se centra únicamente en los dos primeros. DSDV y OLSR son protocolos proactivos basados en tablas de ruteo que se actualizan de f= orma regular. Por el contrario AODV es un protocolo reactivo que trabaja a petición para crear nuevas rutas (Bai = et al., 2017).

 =

Hoy en d&ia= cute;a en el deporte, la tecnología ha tomado un papel muy importante y el fútbol no es la excepción. La introducción de la tecnología dentro del deporte ha permitido una mejora en la formaci&= oacute;n de los atletas. Dentro de los entrenamientos, existen ejercicios que demand= an un mayor esfuerzo físico, es el caso del sprint, que es un ejercicio= de alta intensidad donde los atletas llegan a su máxima capacidad. Por = lo tanto, resulta indispensable realizar una medición de los datos físicos del atleta o jugador (Lama= arti et al., 2019).&nbs= p; En este contexto es necesario tener en cuenta métricas como el throughput, el delay end-to-end, el jitter y el PDR. Y se debe priorizar al= guna para mejorar la calidad de servicio (QoS). Aquí es donde toma importancia el protocolo de enrutamiento utilizado en la red. Dentro de los inconvenientes que surgen en las redes MANET, están el área de interferencia entre nodos, la distancia entre los mismos y la velocidad de desplazamiento de los nodos. Todos estos factores influyen directamente en = las métricas mencionadas anteriormente. Por tal razón, en este artículo se utilizan protocolos que tienen una base sólida, q= ue han sido probados y se ha demostrado que son robustos y eficientes.

 =

La simulación sigue desempeñando un papel vital en términ= os de escalabilidad tanto en tamaño como en velocidad experimental. Con estudios basados en simulación, el enfoque se puede analizar en deta= lle a diferentes escalas, con diversas aplicaciones de datos, diferentes condiciones de campo, con el fin de obtener resultados reproducibles y anal= izables. De ahí que, se propone el uso de NS3 junto con el módulo Flow Monitor para realizar la simulación. NS3 es un software ampliamente empleado para investigación, ya que es un programa muy realista, los modelos que usa están apegados a la realidad, se puede utilizar semi= llas para generar aleatoriedad y obtener resultados estadísticos aceptabl= es (Camp= anile et al., 2020). Además, al trabajar con un gr= an número de nodos, diferentes tipos de movilidad, entre otros parámetros, el software de simulación es una herramienta que = proporciona ventajas, debido a las limitaciones que implica trabajar en escenarios con = una gran cantidad de nodos en la realidad.

 =

 En este artículo se presenta= una evaluación de los protocolos de enrutamiento OLSR, AODV y DSDV para = el despliegue de una red MANET enfocada en la transmisión de datos biométricos. En particular, se ha seleccionado el caso de un escenar= io deportivo con nodos móviles. En la sección 2 se presentan los trabajos relacionados a los protocolos de enrutamiento en redes Ad-Hoc, así como de sensores biométricos en ambientes deportivos. Adicionalmente, en la sección 3 se encuentra la metodología empleada y las herramientas utilizadas. En la sección 4 se presentan= los resultados obtenidos y finalmente, en la sección 5 se encuentran las conclusiones.

 =

Trabajos relacionados

De acuerdo a (Sharma & Shruti Thapar, n.d.), se deben tomar en cuen= ta las características específicas de cada aplicación para escoger un protocolo sobre otro. También se detalla que los protocol= os reactivos tienen un mejor rendimiento que los proactivos en un escenario con movilidad, no así para un escenario fijo o estático. En (Gamess & Russoniello, 2018; Singh & Verma, 2015) se señala que el protocolo de enrutamiento proactivo OLSR presenta un menor delay end-to-end= y menor número de saltos comparado con DSDV. En (Yefa Mai et al., 2017) se demuestra que AODV es mejor en cuanto al throughput y delay end-to-end, pero OLSR es mejor en la entrega de paquetes. Al aumentar el área de movilidad, el protocolo = AODV se vuelve dominante en todas las métricas. De manera similar en (Singh & Verma, 2015), se comprueba que AODV = es mejor que OLSR y DSDV en condiciones de nodos móviles, pero con nodos fijos OLSR supera a AODV y a DSDV en las métricas de throughput y PD= R. Además en (Al-Dhief et al., 2018) se establece que AODV s= upera a DSDV en términos de throughput.

 

En el ámbito deportivo existen varios estudios = del movimiento en atletas, por ejemplo, en (Massard et al., 2018) se realiza un aná= ;lisis de la velocidad máxima de jugadores de fútbol. Asimismo en ADDIN CSL_CITAT= ION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"DOI":"10.1016/j.heliyon.2020.e05427","I= SSN":"24058440","abstract":"Health sciences; Physiology; Physical activity; Occupational health; Musculoskelet= al system; Evidence-based medicine; Aerobic threshold; Anaerobic threshold; Maximal oxygen uptake; InStat index; Soccer.","author":[{"dropping-particle":"&quo= t;,"family":"Modric","given":"Toni"= ,"non-dropping-particle":"","parse-names":fal= se,"suffix":""},{"dropping-particle":"&q= uot;,"family":"Versic","given":"Sime&quo= t;,"non-dropping-particle":"","parse-names":f= alse,"suffix":""},{"dropping-particle":"= ","family":"Sekulic","given":"Damir= ","non-dropping-particle":"","parse-names&quo= t;:false,"suffix":""}],"container-title":&quo= t;Heliyon","id":"ITEM-1","issue":"1= 1","issued":{"date-parts":[["2020","= ;11","1"]]},"page":"e05427","publis= her":"Elsevier Ltd","title":"Aerobic fitness and game performance indicators in professional football players; playing position specifics and associations","type":"article-journal","volum= e":"6"},"uris":["http://www.mendeley.com/docu= ments/?uuid=3D3682f044-e963-325d-88b1-882d903387c7"]}],"mendeley&= quot;:{"formattedCitation":"(Modric et al., 2020)","plainTextFormattedCitation":"(Modric et al., 2020)","previouslyFormattedCitation":"(Modric et a= l., 2020)"},"properties":{"noteIndex":0},"schema&= quot;:"https://github.com/citation-style-language/schema/raw/master/cs= l-citation.json"}(Modric et al., 2020) se obtienen valores para la velocidad de desplazamiento dependiendo de la posición de juego, así como = del estado físico de cada jugador. De igual manera, en el estudio que realiza (Wehbe et al., 2014), se obtiene el tiempo para cada movimiento q= ue efectúa un jugador de acuerdo a su posición. Los perfiles de actividad física de los jugadores revelan una información val= iosa para conocer el esfuerzo físico que realiza el jugador y así mantenerlo en un estado óptimo, evitar lesiones y desarrollar nuevas estrategias de acuerdo al posicionamiento que se observa en los mapas de ca= lor. Los entrenadores están interesados en herramientas analíticas para apoyar el análisis táctico y controlar la calidad de sus jugadores durante partidos individuales o temporadas enteras. Debido a esto= , existen varios estudios donde se analiza la posición de cada jugador en el c= ampo de juego de acuerdo a su rol (Defensa, Medio-campista, Delantero, etc.) ADDIN CSL_CITAT= ION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"DOI":"10.1145/3343172","ISSN":&quo= t;21576912","abstract":"The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a ma= tch (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidat= ed and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluat= ion of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes = the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibi= lity and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.","author":[{"dropping-particle":"&= quot;,"family":"Pappalardo","given":"Luc= a","non-dropping-particle":"","parse-names&qu= ot;:false,"suffix":""},{"dropping-particle":&= quot;","family":"Cintia","given":"P= aolo","non-dropping-particle":"","parse-names= ":false,"suffix":""},{"dropping-particle"= ;:"","family":"Ferragina","given":&= quot;Paolo","non-dropping-particle":"","parse= -names":false,"suffix":""},{"dropping-particl= e":"","family":"Massucco","given&qu= ot;:"Emanuele","non-dropping-particle":"",&qu= ot;parse-names":false,"suffix":""},{"dropping= -particle":"","family":"Pedreschi","= ;given":"Dino","non-dropping-particle":""= ;,"parse-names":false,"suffix":""},{"dro= pping-particle":"","family":"Giannotti",= "given":"Fosca","non-dropping-particle":"= ;","parse-names":false,"suffix":""}],&qu= ot;container-title":"ACM Transactions on Intelligent Systems and Technology","id":"ITEM-1","issue":"= 5","issued":{"date-parts":[["2019","= ;9","1"]]},"page":"59","publisher&q= uot;:"Association for Computing Machinery","title":"PlayeRank: Data-driven performance evaluation and player ranking in soccer via a machine learning approach","type":"article-journal","volume&qu= ot;:"10"},"uris":["http://www.mendeley.com/documen= ts/?uuid=3De55619aa-01b4-3cd4-a498-fa7e1257040a"]}],"mendeley&quo= t;:{"formattedCitation":"(Pappalardo et al., 2019)","plainTextFormattedCitation":"(Pappalard= o et al., 2019)","previouslyFormattedCitation":"(Pappalardo = et al., 2019)"},"properties":{"noteIndex":0},"schema&= quot;:"https://github.com/citation-style-language/schema/raw/master/cs= l-citation.json"}(Pappalardo et al., 2019).  Al tener un perfil de actividad física de cada jugador se puede limitar su movimiento con la ayuda de los mapas de calor de acuerdo a su posición. De esto depende la formación usada por el equipo.

 

En (Akbar et al., 2016) se mencionan diferentes tipos de sensores biométricos, como por ejemplo un medidor del ritmo cardíaco, = un sensor SpO2, Acelerómetro, Giroscopio, etc. Cada uno de estos sensor= es genera información acerca de la actividad física. Segú= n (Lloret et al., 2016), la cantidad de información út= il (payload) de un paquete con información de salud puede alcanzar un tamañ= ;o promedio de 104 bytes. Asimismo, en (Besher et al., 2020) y (Beitelspacher et al., 2020), se muestra que el tamaño de un paquete de datos biométricos es de 79 bytes y 50 bytes respectivamente, las diferencias se dan debido al número de sensores, tipo de sensores y frecuencia de recolección de datos.

 

Otro aspecto fundamental para caracterizar el tr&aacut= e;fico y mejorar los resultados de la simulación, es el data rate. E= n (Akbar et al., 2016) y (Kos et al., 2019), se muestran los requerimientos para aplicaciones médicas. Donde se encuentran los valores óptimos para el data rate (ver, Tabla 1) además el delay y el consumo de energía para algunos t= ipos de sensores usados en el deporte. Es de vital importancia el monitoreo de la salud dentro del deporte, en (R. T. Li et al., 2016) se señala que los avances en la tecnología han permitido a los atletas de resistencia individuales, equipos deportivos y médicos, monitorear los movimient= os funcionales, cargas de trabajo y marcadores biométricos para maximiz= ar el rendimiento y minimizar las lesiones. Con la misma idea en (S. Li et al., 2020) se señala que los sensores corporales= en el campo deportivo tienen un gran impacto en la eficiencia de la vida, ya q= ue estos se enfocan en medir factores fisiológicos, promover la salud y mejorar la adherencia a la práctica en diferentes poblaciones, desde atletas de élite hasta pacientes.

 

Tabla 1

Valores de Data Rate para Diferen= tes Sensores Biométricos

SENSOR

DATA RATE ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","item= Data":{"DOI":"10.1155/2016/7170943","ISSN&quo= t;:"16877268","abstract":"Recently, increasing demand for remote healthcare monitoring systems poses a specif= ic set of Quality of Services (QoS) requirements to the MAC layer protocols = and standards (IEEE 802.15.6, IEEE 802.15.4, etc.) of Wireless Body Area Sens= or Networks (WBASNs). They mainly include time bounded services (latency), reliable data transmission, fair channel distribution, and specified data rates. The existing MAC protocols of WBASNs are lack of a specific set of QoS. To address this, the paper proposes a QoS profile named delay, reliability, and throughput (DRT). The QoS values computed through DRT profile provide maximum reliability of data transmission within an accept= able latency and data rates. The DRT is based on the carrier sense multiple ac= cess with collision avoidance (CSMA/CA) channel access mechanism and considers IEEE 802.15.4 (low-rate WPAN) and IEEE 802.15.6 (WBASN). Further, a detai= led performance analysis of different frequency bands is done which are standardized for WBASNs, that is, 420 MHz, 868 MHz, 2.4 GHz, and so forth. Finally, a series of experiments are conducted to produce statistical res= ults for DRT profile with respect to delay, reliability, and packet delivery r= atio (PDR). The calculated results are verified through extensive simulations = in the CASTALIA 3.2 framework using the OMNET++ network simulator.","author":[{"dropping-particle":"= ;","family":"Akbar","given":"Muhamm= ad Sajjad","non-dropping-particle":"","parse-n= ames":false,"suffix":""},{"dropping-particle&= quot;:"","family":"Yu","given":&quo= t;Hongnian","non-dropping-particle":"","parse= -names":false,"suffix":""},{"dropping-particl= e":"","family":"Cang","given":= "Shuang","non-dropping-particle":"","par= se-names":false,"suffix":""}],"container-titl= e":"Journal of Sensors","id":"ITEM-1","issued":{&q= uot;date-parts":[["2016"]]},"publisher":"Hind= awi Limited","title":"Delay, Reliability, and Throughput Based QoS Profile: A MAC Layer Performance Optimization Mechanism for Biomedical Applications in Wireless Body Area Sensor Networks","type":"article-journal","volume&= quot;:"2016"},"uris":["http://www.mendeley.com/doc= uments/?uuid=3D1173fed6-f528-357b-987f-e4aed94f27cf"]}],"mendeley= ":{"formattedCitation":"(Akbar et al., 2016)","plainTextFormattedCitation":"(Akbar et al., 2016)","previouslyFormattedCitation":"(Akbar et = al., 2016)"},"properties":{"noteIndex":0},"schem= a":"https://github.com/citation-style-language/schema/raw/master/= csl-citation.json"}(Akbar et al., 2016)(Kos et al., 2019)(Külah & Alemdar, 2020), se propuso la siguient= e división mostrada en la Figura 1 y Figura 2. Donde DFC representó a los defensas centrales, DFI y DFD representó a los defensas izquierdo y derecho respectivamente, MC representó a los medio-campistas, EI y ED representaron a los extrem= os izquierdo y derecho respectivamente y DC representó al delantero cen= tro. Cada nodo se movió dependiendo de la posición del jugador al = que representó, basándose en los mapas de calor de cada jugador según (Wehbe et al., 2014), por lo cual se asignaron diferentes colores= a estas áreas para poder identificarlos, ya que los nodos no pueden cambiar de área, con el motivo de simplificar el escenario de movili= dad de los nodos. Cabe recalcar que existieron posiciones de relevos por lo que= en ciertos momentos los nodos o jugadores compartieron la misma área, es por ello que se ha definido áreas conformadas con barras, es el caso= de los DFI y EI o DFI y MC.

&n= bsp;

Otro aspecto importante tomado en cuenta fue la veloci= dad de los jugadores (nodos). Por lo cual se consideró velocidades promedio= s y velocidades máximas donde el entorno cambiaba cada 5 segundos (Antonio Durá Vaquera, n.d.), siendo este el tiempo = de movilidad. Después de cumplir este tiempo se cambió la dirección y la velocidad de los nodos utilizando una variable aleato= ria uniforme. En (Wehbe et al., 2014) se detallaron las velocidades promedio en Km= /h y en (Mendez-Villanueva, 2012) y las velocidades máximas, para las distintas posiciones, es decir para defensas, medio-campistas y delanteros. En la Tabla 2 se resumen estos valores.

 

Figura <= /span>1

Escenario 1 "Equipo Defendiendo"

3D"Interfaz

 

 

Figura <= /span>2

Escenario 2 "Equipo Atacando= "

3D"Interfaz

 

Tabla 2

Velocidades Promedio y Máx= imas para cada Posición

 

DFC

DFI/DFD

MC

DC

Vel. Promedio

6,279

6,279

6,965

6,711

Vel. Max.

28,1

26,7

27

29,1

La metodología utilizada para comparar el desempeño de cada uno = de los protocolos de enrutamiento dentro de estos dos escenarios se muestra en= la Figura = 3Tabla 1. De la misma forma se estableció el tamaño del paquete en 100 bytes, debido a que es un valor que estuvo en el rango descrito por los aut= ores (Beitelspacher et al., 2020; Besher et al., 2020; Lloret et al., 2016)= . Por último, se escogió UDP como protocolo de transporte debido a que comparado con = TCP éste es más rápido, simple y eficiente. Esta caracterización permitió generar un tráfico realista e= ntre los nodos.

 

El cuarto bloque permitió definir las ár= eas de movilidad de los nodos, así como las velocidades de cada uno dependi= endo de la posición que ocupó dentro del campo de juego y el tipo = de escenario, para esto se utilizó el módulo Random Walk = de NS3 el cual es un modelo de movilidad aleatorio 2D. Hay que tener en cuenta= que una de las limitaciones de la simulación es la modelación de = la movilidad, modelar el movimiento humano es otro campo de investigació= ;n específico (Conti & Giordano, 2014).

 

A continuación, se especifican las condiciones = de cada escenario estudiado, es decir, el comportamiento de los nodos cuando representan al equipo de fútbol atacando o defendiendo, para lo cual= se hizo uso de dos scripts, ya que se definen diferentes áreas en cada = uno. Una vez configurados los dos escenarios, se ejecutó la simulaci&oacu= te;n utilizando los protocolos de enrutamiento propuestos y los parámetro= s de las tarjetas inalámbricas o módulos Wi-fi que trasmiten la información de los sensores biométricos según el tipo = de análisis (tres análisis). Algunos de los sensores más comunes utilizaron el estándar IEEE 802.11n como lo hizo en (Hu et al., 2020), por lo que la simulación emple&oacut= e; el mismo. Para generalizar los resultados se hizo uso de una semilla de aleatoriedad y se repitió la simulación utilizando un script = de Linux, variando así las condiciones de la simulación. Después, se realizó un análisis estadístico de = los resultados obtenidos para cada escenario con cada uno de los protocolos empleados. Finalmente, se establecieron las conclusiones respectivas y se definió el mejor protocolo dependiendo del tipo de métrica. A= ntes de continuar, se muestran en la Tabla 3 los parámetros utilizados en las simulaciones.

 

Basándose en la metodología mencionada, = se han propuesto tres análisis estadísticos utilizando intervalos de confianza del 95%, el primero consistió en ejecutar la simulaci&oacu= te;n con un tiempo de 3 minutos o 180 segundos con la configuración de la Tabla 3, usando una potencia fija de 3dBm y los protocolos de enrutamiento con sus valores por defecto. Se realizaron cinco simulaciones para cada escenario con cada uno de los protocolos de enrutamiento. El segundo análisis consistió en aumentar el número de simulaciones para cada protocolo a 20, así como el tiempo de cada simulación a 5 minutos o 300 segundos, también= se aumentó el intervalo de transmisión a 5, 10, 20, 30 y 60 segu= ndos y se modificaron los archivos de configuración de cada protocolo, pa= ra mejorar el desempeño de la red. Por lo tanto, para el protocolo OLSR= se acortó el valor del parámetro Hello de 2 segundos a 0.5 segundos, y el parámetro TC se disminuyó de 3 a 1 segu= ndo. Para el protocolo DSDV se cambió el parámetro PeriodicUpda= teInterval de 15 a 2 segundos, con esto se redujo el periodo de intercambio de tab= las de enrutamiento completas entre nodos.

 

El siguiente parámetro modificado fue el Set= tlingTime, reduciendo el tiempo mínimo de almacenamiento de una actualización de 5 a 3 segundos. Ahora, para AODV el primer parámetro que se modificó fue el intervalo Hello, el c= ual cambió de 1 a 0.5 segundos. También se estableció al parámetro ActiveRouteTimeout en 2 segundos en lugar del valor original de 3 segundos, este parámetro determinó el periodo de tiempo durante el cual una ruta es considerada como válida, por lo tanto, al tener un escenario con alta movilidad se disminuyó este periodo.

 

En el tercer análisis se escogieron los dos pro= tocolos con mejor rendimiento que en el segundo análisis y con el intervalo = de tiempo más óptimo. Además, varió la potencia del transmisor en el rango mostrado en la Tabla 33

 Resumen de la Metodología Ap= licada

3D"Diagrama Descripción

 

Tabla 3

Parámetros de la Simulaci&= oacute;n

PARÁMETROS

ANÁLISIS 1

ANÁLISIS 2

ANÁLISIS 3

Está= ndar WI-FI

IEEE 802.11= n (2.4 GHz)

Capa Física

ErpOfdmRate= 48Mbps

Protocolo de transporte

UDP

Tipo de tráfico

CBR

Tamaño del paquete

100 bytes

Data Rate

100 kbps

Tipo de Movilidad

Random Walk

Tiempo de Movilidad

5 seg

Tiempo de Simulación

180 seg

300 seg

300 seg

Intervalo de Transmisión

1 seg

5, 10, 20, = 30 y 60 seg

20 seg

Número de Simulaciones por Protocolo

5

20

20

Potencia del Transmisor

3 dBm

3 dBm

0 - 10 dBm

Cabe señalar= que la cantidad de nodos y el tipo de escenario demandó un tiempo considera= ble para realizar múltiples simulaciones, debido a la alta carga computacional requerida. Con el objetivo de realizar el estudio con varias pruebas, 230 en total, se utilizaron tiempos de 3 y 5 minutos, ya que incrementar estos tiempos es una limitación debido a las demoras que requieren cada uno de los estudios. Entonces, se prefirió realizar múltiples simulaciones de corta duración para obtener resulta= dos que se puedan valorar estadísticamente, en lugar de una sola simulación de larga duración.

&n= bsp;

Resultados y Discusión

Primer an&aac= ute;lisis

En la Figura 4 se presentan los resultados obtenidos = para el PDR en el escenario 1 y escenario 2. Es evidente que la mejor tasa de recepción de paquete se logra con el protocolo de enrutamiento react= ivo AODV, llegando hasta un 49% de paquetes recibidos. Mientras que el peor desempeño lo obtiene DSDV con tan solo un 27%. Sin embargo, los intervalos de confianza son mayores en AODV, lo que implica que hay m&aacut= e;s variaciones entre simulaciones. En cuanto al delay promedio obtenido para c= ada protocolo, se pueden observar los resultados logrados en la Figura 5, que el protocolo OLSR alcanza los mej= ores resultados ya que tienen el menor delay. En cambio, los otros dos protocolo= s no varían mucho, pero se tienen que tomar en cuenta que para el escenar= io 2 el protocolo DSDV tiene intervalos de confianza muy amplios.  Finalmente, se puede observar en l= a Figura 6, que AODV tiene el mejor throughput pr= omedio en los dos escenarios, le sigue el protocolo OLSR y el protocolo AODV en es= te caso es el peor.

 <= /o:p>

Figura 4=

Comparativa del PDR para cada Protocolo de Enrutamiento

3D"Gráfico,

 <= /o:p>

Al analizar l= as métricas se observa= que el protocolo AODV tiene una alta variación en los intervalos de confianza. Por lo que depende mucho de las condiciones de movilidad, velocidad y del canal, para tener un buen desempeño. En ciertos instantes el tráfico cae prácticamente a cero, debido a la perdida de paquetes. Por este moti= vo se verifica las tablas de enrutamiento y las posiciones de los nodos en est= os instantes y se verifica que, debido al movimiento de los nodos, existen instantes en los que un nodo sale del rango de cobertura con algún n= odo vecino pero las tablas de enrutamiento tardan un determinado tiempo en converger nuevamente, en el caso de OLSR, se escoge un intervalo de 3 segun= dos para intercambiar mensajes TC. Además, se puede notar que debido a la naturaleza del protocolo AODV, este resulta ser más eficiente debido= a que actualiza sus tablas de enrutamiento únicamente cuando va a transmitir tráfico.

 <= /o:p>

Figura 5=

Comparativa del Delay para cada Protocolo de Enrutamiento

3D"Gráfico,

=  

 

Figura 6=

Comparativa del Throughput Promedio para cada Protocolo de Enrutamiento

3D"Gráfico,

 

 

 

Segundo análisis

Los resultad= os muestran que, al aumentar el número de simulaciones y ajustar los parámetros de los protocolos, las métricas mejoraron notablem= ente con respecto a los resultados anteriores. Esto se debe a que los protocolos= de enrutamiento tienen más tiempo para converger y por lo tanto existe = una menor pérdida de paquetes, además debido a la movilidad de los nodos y al canal inalámbrico existen interferencias, pérdidas= de paquetes y por lo tanto el PDR no supera el 70%. Además, los interva= los de confianza se redujeron considerablemente, lo cual indica que existe una menor desviación entre los valores. En la Figura 7, se observa que el PDR tiene una tende= ncia creciente conforme se incrementa el intervalo, sin embargo, este crecimient= o no es tan acelerado, por lo que no hay gran diferencia a partir de los 20 segundos. Con respecto a los protocolos se puede notar que AODV presenta mejores resultados en el escenario 1 y DSDV para el escenario 2.

 

 

 

Figura 7=

Comparativa del PDR para cada Protocolo de Enrutamiento

3D"Gráfico,

 

 

Figura 8=

Comparativa del Delay para cada Protocolo de Enrutamiento

3D"Gráfico,

 

 

En cuanto al delay, la  REF _Ref87022791 \h Figura 8 muestra que el delay aumenta rápidamente conforme se incrementa el intervalo de transmisión, pero al comparar los protocolos de enrutamiento no se evidencia una diferencia muy marcada entre cada uno. Se puede observar que a partir de los 20 segundos los intervalos de confianza crecen debido a que al separar las transmisiones disminuye la cantidad de información recibida y por lo tanto un error tendrá un mayor = impacto en los resultados. Con respecto a la última métrica, el throughput, en la Figura 9 indica que el mejor protocolo es AODV p= ara el escenario 1 y el protocolo DSDV para el escenario 2. Además, se puedo observar que el throughput no alcanza los 100 kbps que es la tasa fijada en cada uno de los trasmisores, por el contrario tienen un valor promedio de 6= 7.82 kbps y 58.96 kbps para el escenario 1 y 2 respectivamente, esto ocurre debi= do a las características propias de una red ad-hoc multi-salto, ya que el throughput disminuye con cada salto según (González et al., 2016).

 

 

Figura 9=

Comparativa del Throughput para cada Protocolo de Enrutamiento

3D"Imagen

 = ;

Tercer análisis

De acuerdo a los resultados anteriores,= se descarta el protocolo OLSR debido a que tiene el peor rendimiento comparado= con los otros dos protocolos en la mayoría de métricas y escenari= os. Además, se establece el intervalo de transmisión en 20 segund= os ya que se obtienen mejores resultados y permite la convergencia de los protocolos de enrutamiento. También se incrementa la potencia del transmisor, y se puede notar que las métricas mejoran su rendimiento= con respecto a los resultados de la sección anterior. Sin embargo, a par= tir de los 4 dBm la red Ad Hoc deja de ser multi-salto y pasa a conformar una r= ed MESH. En la = Figura 10, conforme aumenta la potencia, también aumenta el PDR debido a que se disminuye la cantidad de salt= os, sin embargo, también aumentan las interferencias por lo que todavía existen pérdidas de paquetes. Por otro lado, en el escenario 1 el protocolo AODV es mejor que DSDV hasta los 4 dBm o, en otras palabras, AODV tiene un mejor rendimiento que DSDV en la red multi-salto, p= ero DSDV es mejor cuando la red pasa a ser de tipo MESH. Al analizar el escenar= io 2, se observa que DSDV es mejor que AODV sin importar el valor de la potenc= ia del transmisor.

 

Figura 10

Comparativa del PDR para cada Protocolo de Enrutamiento

3D"Imagen

&nbs= p;

En la = Figura 11, se puede notar que el protocolo AODV = tiene un mayor delay que DSDV en los dos escenarios, esto se debe a que AODV al s= er un protocolo reactivo le toma mayor tiempo obtener la información de ruteo de los demás nodos. También se observa que el menor del= ay en una red MESH se obtiene con una potencia de 10 dBm, mientras que en una = red multi-salto el mejor resultado se logra con 3 dBm, de lo cual se puede notar que en ambos tipos de red el mejor resultado se obtiene con el máxim= o de potencia. Con respecto al throughput, la Figura 12 indica que el valor incrementa conforme aumenta el nivel de potencia de los transmisores. También se nota que en el escenario 1, AODV es mejor q= ue DSDV hasta los 4 dBm, y además se observa que con 10 dBm no existe u= na diferencia notable, teniendo además un error menor al 1%. Ahora, en = el escenario 2 DSDV siempre es mejor que AODV a excepción de los 10 dBm, donde nuevamente no hay una diferencia significativa. Finalmente, se puede notar que con 10 dBm el throughput coincide con el data rate de transmisión configurado inicialmente, siendo así el mejor resultado posible para esta métrica.

 

Figura 11

Comparativa del Delay para cada Protocolo de Enrutamiento

3D"Tabla Descripción

 

 

Figura 12

Comparativa del Throughput para cada Protocolo de Enrutamiento

3D"Gráfico,<= span style=3D'mso-bidi-font-size:12.0pt;mso-ansi-language:ES-EC;font-weight:norm= al; mso-bidi-font-weight:bold'>

 

Conclusiones

En el primer análisis, se puede concluir que el protocolo de enrutamiento reactivo AODV tiene un mejor desempeño en = las tres métricas analizadas (PDR, delay y throughput), pero con unos intervalos de confianza muy amplios. Se comprueba que el número máximo de saltos generados fue de tres, cuando el nodo se encontraba= muy alejado del nodo receptor. Mientras que cuando el nodo transmisor se encont= raba más central o cerca del nodo receptor el número de saltos generado era de uno. De acuerdo al perfil del tráfico obtenido y el = PDR para cada uno de los protocolos, se puede notar que existe una gran pérdida de paquetes. Estas pérdidas ocurren debido al comportamiento aleatorio de los nodos, lo cual genera cambios en la topología de la red. Es decir, las tablas de enrutamiento se actuali= zan constantemente, pero los movimientos bruscos de los nodos (jugadores) no permiten que converjan en ciertos instantes. Además, se comprueba qu= e el crecimiento del throughput afecta la efectividad del envío de paquet= es debido a las limitaciones del ancho de banda.

 

En el segundo análisis las tres métricas mejoran y existe una reducción de los intervalos de confianza debido= al cambio en los parámetros de cada protocolo, el espaciamiento entre transmisiones y por el incremento del número de simulaciones. Por ejemplo, con un espaciamiento de 20 segundos el delay se reduce un 64.58% p= ara OLSR, 89.36% para AODV y 88.31% para DSDV, realizando un promedio entre los= dos escenarios. Por otra parte, al aumentar el intervalo de transmisión = se incrementa el delay de cada paquete, por lo que se escoge 20 segundos como un valor óptimo. Con este valor el throughput aumenta un 27.33% para OLSR, un 105% para DSDV realizando el promedio entre los dos escenarios. Mientras que para AODV el throughput aumenta un 44.33% en el escenario 1. Además,= se evidencia que en el escenario 1 (Defensa) el protocolo con mejor rendimient= o es AODV y para el escenario 2 (Ataque) es DSDV. Finalmente, en el análi= sis tres, mejoran los resultados en los dos escenarios. Sin embargo, al superar= los 4 dBm en la potencia del transmisor, la red se convierte en una red MESH. Y= , en definitiva, se comprueba que los dos protocolos no tienen gran diferencia e= n su desempeño en las tres métricas con una potencia de 10 dBm, po= r lo que se puede escoger cualquiera de los dos para este tipo de escenarios. 

 

Reconocimientos

Los autores de este trabajo agradecen al Departamento = de Eléctrica, Electrónica y Telecomunicaciones de la Universidad= de Cuenca por todo el apoyo brindado y herramientas proporcionadas para la elaboración de este artículo.

 

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