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https://doi.org/10.37815/rt= e.v33n2.824

Artículos originales=

 

Comparación de Modelos Empíricos de pérdida LPWAN por Propagación en el campus Prosperi= na de la ESPOL: Resultados derivados de un estudio empírico<= /o:p>

LPWAN Empirical Propagation Path Loss Models comparison at Prosperina campus in ESPOL: Results based on an empir= ical study

 

Geovanny Arguello1 https://orcid.org/0000-0002-3486-8444, Edison Del Rosario1  https://orcid.org/0000-0003-4573-2048, Edison Boris Ramos1 https://orcid.org/0000-0002-0469-6135=

 

1Escuela Superior Politécnica del Litoral - ESPOL, Guayaquil, Ecuador

geomaarg@espol.edu.ec, , bramos@espol.ed= u.ec

 

Enviado:         <= /span>2021/06/11

Aceptado:       2021/09/28

Publicado:      2021/11/30                          <= span class=3Deop>

Resumen

La tecnología de largo alcance LoRa, como parte de tecnologías de bajo consumo energético LPWAN, permite estable= cer comunicaciones inalámbricas a kilómetros de distancia con bajas tasas de transmisión. Sus características, inducen a la creación de nuevas líneas de investigación para comprender su comportamiento en diversos ambientes y diferenciarlas de otras tecnologías similares. En esta investigación, se caracteriza a la tecnología LoRa en contraste c= on Sigfox y NBIoT. Se evalúa= el efecto de pérdidas en la propagación que tiene la tecnología LoRa, con mediciones realizadas en ambientes tanto ab= iertos como cubiertos por vegetación, dentro de las instalaciones de la Escuela Superior Politécnica del Litoral (ESPOL), campus Gustavo Galindo Velasco o Prosperina. Se establece un modelo de pérdida por gradient= e, utilizando regresión de mínimos cuadrados (LS). Se registra el parámetro RS= SI en paquetes enviados a través de una topología punto a punto entre nodos, y= se obtiene un valor de potencia promedio recibida en cada nodo. El modelo obtenido, es de gradiente múltiple y se lo compara con otros modelos de propagación como el modelo de pérdida en espacio libre (FSL), y tres modelos empíricos como son, modelo COST-231 Hata, model= o de la ciudad de Dortmund (Alemania) y el modelo de la ciudad de Oulu (Finlandi= a), siendo este último el que presenta menor error de dispersión con el modelo obtenido.

3D"Cuadro

Palabras clave: gradiente, LoR= a, LPWAN, modelo de propagación, regresión de mínimos cuadrados (LS).

Abstract

Long-range technology LoRa, as part of low power consumption technologies LPWAN, allows wireless communications within kilometers of di= stance using low data rates.  Its characteristics induce the creation of new lines of research to have a bett= er understanding of its behavior in different environments and differentiate it from other similar technologies. In this research, LoRa technology was characterized by the contrast of Sigfox and <= span class=3DSpellE>NBIoT. It was evaluated the path loss effect in LoRa technology, using measurements in open environments and forest environments= , at Prosperina Campus - ESPOL.  A path loss gradient model using Least Squares Regression (LS) was found. RSSI parameter was registered, from send= ing packages through point-to-point node’s topology and getting a mean power fr= om each node. The multi-gradient model obtained was compared with other propagation models such as the Free Space Loss (FSL) model and three empiric models: COST-231 Hata model, model of Dortmund = city (Germany), and model from Oulu city (Finland). Among these models, the latt= er presented the lowest dispersion error regarding the obtained model.

 

Keywords: gradient, LoRa, LPWAN, propagation model, least square regression.

 

I= ntroducción

En la actualidad, junto con el desarrollo del Internet de las cosas (IoT), existen cada vez más dispositivos intercomunica= dos entre sí (Kais Mekki, 2018). Una de las tecnologías de comunicación que ha ganado popularidad en los últimos tiempo= s, son aquellas conocidas como LPWAN (Low Power Wi= de Area Network), debido a su largo alcance y bajo consu= mo energético. Lora, Sigfox y NBIoT son tres de las tecnologías LPWAN más utilizadas para el desarrollo de aplicaciones IoT dependiendo las característica= s que se requieran.

 

Los modelos de propagación ayudan en la planificación y despliegue de redes, particularmente para realizar estudios durante el despliegue inicial y la viabilidad del proyecto. También se los utiliza en estudios para calcular la medida de la interferencia conforme avanza el despliegue de la red. En las redes inalámbricas, se pueden caracterizar tres tipos de modelos; estos son: empíricos, determinísticos y estocásticos (V.S. Abhayawardhana, 2005). Los modelos empíricos son los que se basan únicamente en observaciones y mediciones. Es= tos modelos se utilizan principalmente para predecir la medida de pérdida por trayectoria, pero también existen modelos que predicen el desvanecimiento p= or lluvia y la trayectoria múltiple. Los modelos deterministas hacen uso de las leyes que gobierna la propagación de ondas electromagnéticas para determina= r la potencia de la señal recibida en una ubicación particular.

 

Los modelos deterministas, a menudo requieren un mapa completo en 3D de la propagación ambiente. Un ejemplo de modelo determinista es un modelo de tra= zado de rayos. Los modelos estocásticos, por otro lado, modelan el medio ambiente como una serie de variables aleatorias. Estos modelos son los menos preciso= s, pero requieren la menor información sobre el medio ambiente y utilizan menos potencia de procesamiento para la generación de predicciones. Los modelos empíricos se pueden dividir en dos subcategorías, disperso en el tiempo y no disperso en el tiempo. El primero, está diseñado para proporcionar informac= ión relacionada con las características temporales que sufre el canal de propagación usado; es decir, el retardo por múltiples trayectorias en la propagación del canal. Un ejemplo de este tipo son los que se desarrollaron= en la Universidad de Stanford (SUI), con un grupo de trabajo conformado por personal del Instituto de Ingenieros Eléctricos y Electrónicos (IEEE) 802.1= 6. Ejemplos de modelos empíricos no dispersivos en el tiempo son ITU-R, Hata y el modelo COST-231 Hata (Hendrik Linka, 2018). Todos estos m= odelos predicen la media de la pérdida por trayectoria en función de varios parámetros, por ejemplo, distancia, alturas de antena, etc.

       Aunque los modelos de propagación empíri= cos para sistemas móviles se han validado de forma exhaustiva, su idoneidad para ser considerados estándares, no ha sido completa= mente establecida. Este artículo propone un modelo empírico de propagación basado= en mediciones realizadas en el campus de la E= SPOL, ayudado de análisis estadístico para calcular el gradiente-pendiente resultante, tanto en ambientes cubiertos con vegetación, como en ambientes abiertos. Luego, se compara con varios estudios empíricos presentados y expuestos en la comunidad científica. Se analiza el efecto de la línea de v= ista en la tecnología LoRa.

 

Di= ferencias técnicas y factores IoT: L= oRa, Sigfox y NBIoT=

A pesar de que LoRa, Sigfox<= /span> y NBIoT son consideradas tecnologías LPWAN, cada = una de ellas presenta características propias dadas por el fabricante:<= /span>

 

LoRa

Es una tecnología de capa física, que utiliza una técnica de ensanchamiento del espectro, de origen propietario por la compañía SEMTECH, para la transmisió= n o recepción de datos. El rango de frecuencias para la modulación está compren= dido entre 860-960 MHz para Europa y América, y frecuencias de 400 MHz para Asia. Admite comunicación bidireccional a través de la modulación por “chirp” chirrido de ensanchamiento del espectro (CSS), metiendo una señal mensaje en un ancho de banda angosto (Ayanle I. Ali, 2019).

 

Para el mejor entendimiento del CSS, se utilizan conceptos como chip y chirp. Un chip equivale a una medida de cantidad dent= ro de un tiempo fijo, equivalente al ancho de banda utilizado para transmitir símbolos; es decir, si se tiene un ancho de banda de 250khz, entonces, se t= iene 250000 chips por segundo. La cantidad de chips que pueden ser utilizados pa= ra la creación de un símbolo viene dada por el factor de esparcimiento (SF), y= se rige a la siguiente fórmula: =  , el SF también indica la cantidad de bi= ts que se utilizarán para un símbolo. El SF va de 7 a 12, mientras mayor el SF, ma= yor es la distancia de cobertura y menor la tasa de transmisión; este efecto es debido a que aumenta la cantidad de chips por símbolo.  Por otro lado, un = chirp no es nada más que la rampa utilizada para la creación de símbolos, que pue= de ser ‘up-chirp’ o ‘down-chi= rp’.

 

La Figura 1= muestra diferentes símbolos de una trama LoRa, detallando características intrínsecas de esta tecnología.

 

Figura <= /span>1<= /span>

Diferentes símbolos LoRa, la prime= ra mitad con ‘up-chirp’ y el restante con ‘down-chirp

3D"Tata

 

Los conjuntos de todas estas características de la señal resultante permiten reducir las interferencias y llegar a largas distancias. Lora adicionalmente utiliza un método de corrección de errores (FEC) con tasas de codificación = 4/5, 4/6, 4/7 y 4/8; para mejorar la sensibilidad en la recepción (Usman Raza, 2017).

 

Sigfox

Tecnología que usa modulación por desplazamiento de fase binaria (BPSK) sobre una portadora muy angosta (UNB) de aproximadamente 100 Hz. El uso de UNB mejora= la sensibilidad en el receptor ya que se utiliza de manera eficiente el espect= ro, generando poco consumo energético. Todo esto suma para tener una tasa de da= tos máxima de 100 bits por segundo. Una de las desventajas que presenta Sigfox es la limitación en el tamaño del mensaje a transmitir y la cantidad de veces por día tanto para subida como para bajad= a (Usman Raza, 2017).

 

NBIoT

Tecnología que coexiste con la tecnología LTE, por lo tanto, depende de una infraestructura presente. Utiliza modulación por desplazamiento de fase en cuadratura (QPSK) junto con acceso múltiple por división de frecuencia (FDM= A), ortogonal para descargas (DL) y única portadora para cargas (UL). Puede ope= rar en 3 modos diferentes, único portador, en banda (compartiendo ancho de banda con portadora LTE) y banda de guarda (en una portadora ya existente). Puede alcanzar tramas hasta 1600 bytes con una tasa de transmisión de 20 kbps para subida y 200 kbps para bajada (Massimo Ballerini, 2020).

 

La Tabla 1 muestra un resumen d= e las características tecnológicas existentes en LPWAN.

 

Tabla 1<= /span>

Características tecnológicas de tecnologías (Oratile Khutsoane, 2017)

 

Lora

Sigfox

NBIoT

Modulación

CSS

UNB DBPSK(UL), GFSK(DL)

QPSK

Frecuencias

430 MHz & 860-960 MHz=

868 MHz & 902 MHz

Frecuencias LTE

Rango

< 20 km

< 40 km

< 10 km

Tasa de transmisión

0.3 – 37.5 kbps (LoRa), 50 kbps (FSK)

100 bps (UL), 600 bps (DL= )

20 kbps (UL), 200 kbps (D= L)

Número de canales

10 en EU, 64+8(UL) y 8 (D= L) en US, diferentes SF

360 canales

Dependiendo el número de bloques LTE

Sincronismo

No

FEC

No

Puede usar

Tamaño de paquetes

Hasta 250 bytes

12 bytes (UL), 8 bytes (D= L)

Hasta 1600 bytes

Topología

Estrella – Multi estrella=

Estrella

SEA

<= o:p> 

<= o:p> 

F= actores IoT

Los factores principales de una tecnología para pertenecer a una red LPWAN son = la distancia en la cobertura y el bajo consumo energético. Sin embargo, para satisfacer las necesidades de una aplicación IoT adicionalmente debe comprender factores como la calidad de servicio (QoS), tiempo de vida de la batería, latencia, escalabilidad, tamaño de la trama, modelo de desarrollo y costo.=

 

Rango y cobertura

A nivel de rangos Sigfox tiene ventaja sobre las demás, debido a que una sola estación base puede ll= egar a cubrir alrededor de 40 km a la redonda; por otro lado, Lora ofrece alcanc= es de hasta 20 km, mientras que NBIoT depende much= o de donde se encuentre una red LTE y sus alcances son menores a 10 km (Elmustafa Sayed Ali Ahmed, 2019).

 

Tiempo de batería y latencia

El consumo energético de las tres tecnologías es bajo, sin embargo, al tener una comunicación sincrónica, los picos de consu= mo de la tecnología NBIoT son mayores que las otra= s dos. La compensación para esto, es la latencia. Lora,= a pesar de que posee dispositivos de clase C que son especializados para redu= cir la latencia aumentando el consumo energético, no llega a reducir la latencia tanto como con dispositivos NBIoT. Por lo tanto= , para aplicaciones de poco envío de datos es recomendable usar Lora o Sigfox, pero para aplicaciones donde la latencia jueg= a un papel importante se recomienda NBIoT.

 

Calidad de servicio

Lora y Sigfox utili= zan bandas no licenciadas y protocolos de comunicación asíncronos, los mismos q= ue son utilizados para mitigar los efectos de interferencias como co-canal, caminos múltiples y desvanecimiento. Sin em= bargo, no llegan a brindar la misma calidad de servicio que da NBIoT, la misma que utiliza una banda licenciada y un protocolo síncrono basado en LTE. Para el uso de una banda licenciada, se tiene que = tener en cuenta el costo para la adquisición/alquiler de la misma, que depende de= la región donde se la utilice (Gutierrez, 2019).

 

Escalabilidad y longitud de trama

LoRa, Sigfox y NBIoT soportan una gran cantidad de nodos conectados a una estación base; no obstante, NBIoT permite mayor escalabilidad ya que teóricamente admite hasta 100 mil usuari= os por celda en comparación con LoRa y Sigfox que admiten hasta 50 mil. Así mismo, la longit= ud de trama es mayor para aplicaciones con tecnología NBIoT<= /span>, hasta 1600 bytes, seguidos por LoRa, hasta 243 = bytes y finalmente Sigfox con un máximo de 12 bytes p= ara transmisión, lo que limita mucho en el uso de aplicaciones.

 

Costo y licenciamiento

NBIoT no tiene un modelo d= e red establecido, ya que utiliza o comparte infraestructura de red LTE, mientras= que Sigfox y LoRa son tecnologías actualmente en comercialización y que poseen diferentes aplicaciones alrededor del mundo. Una de las ventajas significativas de los ecosistemas LoRa es la flexibilidad, ya que adm= iten desarrollos locales, utilizando Gateways LoRa, y desarrollos públicos usando estaciones bases.= Este modelo híbrido permite tener mejor acogida en campos industriales. Para proyectar = el costo en una red LPWAN, es necesario entender la necesidad de pagar o no po= r el espectro donde se usa, la estación base a utilizar y el costo de un nodo en particular. NBIoT utiliza el espectro licenciad= o, el mismo que tiene un costo dependiendo la cantidad de ancho de banda a utiliz= ar en su respectivo país. LoRa y Sigfox utilizan una banda no licenciada por lo que no hay costos por la utilización del espectro. Adicionalmente, el costo por estación base o Gateway Sigfox es mayor a un Gateway Lora, ya que existen en = la actualidad gateways de bajo presupuesto para desarrollo que se conectan con hardware ya existente como la Raspberry Pi. Finalmente, están las estaciones base NBIoT, cu= yo costo excede a otras tecnologías debido a la necesidad de establecer una red celular LTE para su uso. En cuanto a los nodos o dispositivos finales, el c= osto entre LoRa o Sigfox= es muy similar, siendo los dispositivos NBIoT los más costosos en el mercado (Luis F. Ugarte, 2019) (Juha Petäjäjärvi, 2015).

    

            Figura 2 muestra una sinopsis= de cada tecnología versus su factor IoT mencionado= . La tecnología LoRa es presentada en color azul, Sigfox en color naranja y NBIoT<= /span> en amarillo. Cada uno de estos parámetros permiten tener una mejor imagen global con respecto a la aplicación que se desee realizar.

 

Figura 2

Comparación entre factores IoT

3D"Gráfico,

 

Metodología

Una red basada en dos nodos fue establecida dentro del campus Gustavo Galindo Velasco o Prosperina de la Escuela Superior Politécnica del Litoral. Uno de estos fue colocado de forma estática e= n la parte superior del edificio de la Facultad de Ingeniería Eléctrica de Ingeniería en Electricidad y Computación (FIEC). Para la identificación del nodo estático, se atribuyó un nombre conforme a la ubicación de la facultad donde se encuentra. Nodo FIEC fue el nombre elegido, y su ubicación geográf= ica viene referenciada con -2.144914 de latitud y         -79.967758 de longitud. La Figur= a 3 muestra en vista aérea las instalaciones de la ESPOL, georreferencia la ubicación del nodo estático FIEC y de los puntos donde fueron tomadas las pruebas.

 

La altura del Gateway FIEC, con respecto a la superficie del terreno de la universidad, es de 22 metros.

 

El otro nodo fue utilizado como un nodo móvil, con movimiento dentro del área = de cobertura del nodo estático tanto en ambientes abiertos, como en ambientes cubiertos por vegetación, esto debido al entorno encontrado durante el recorrido y toma de mediciones, el mismo que empezaba en un lugar denso y cubierto por vegetación, hasta un lugar más abierto, en ciertos casos obstr= uido por edificios. La elevación del nodo móvil fue de 0.70 metros sobre la superficie de la universidad, para simular un ambiente real.

 

Para cada punto de medición móvil, se utilizó una unidad de GPS diferencial para establecer la correcta ubicación del punto. Todas las mediciones se realiza= ron a una frecuencia de 915 MHz, con un ancho de canal de 20 kHz y un factor de esparcimiento de 10. Para esta investigación se tomaron mediciones en 22 pu= ntos diversos, diferenciados por circuitos, uno llamado FIEC (dentro de la vegetación) y el otro circuito llamado FCNM, por la Facultad de Ciencias Naturales y Matemáticas de la ESPOL, en campo abierto/suburbano mostrado en= las Figuras 4(a) y 4(b), respectivamente. En cada punto de medición georreferenciado se establecieron 100 muestras, de las cuales se calculó el RSSI promedio y el SNR. Las ganancias utilizadas para las antenas fueron de= 3 dBi para el nodo estático y 0 dBi para el nodo móvil. El valor de la intens= idad de señal recibida (RSSI) fue utilizado para hallar el gradiente (pendiente)= del modelo, mientras que el valor de potencia recibida fue utilizado para la estimación de la pérdida por propagación (pathloss) en cada punto de medición.

 

Figura 33D"Imagen

 

 

Figura 4

 

La pérdida por propagación puede ser definida como la distancia entre la poten= cia transmitida en función de la recibida, usualmente expresada en decibelios. = La ecuación para el análisis de regresión de los mínimos cuadrados (LS) es:

 

 

(1)

 

Donde, =  es el punto de referencia a 1 metro de distancia del nodo, y n es conocido como el exponente de pérdida de propagación. Los valores de PL son expresados en decibelios. Para el cálcul= o de la pérdida en espacio libre (FSL) n es igual a dos. La importancia del valo= r de n es la rapidez con la que decae la potencia con respecto a la distancia. <= o:p>

 

Para la obtención de un modelo estadístico significativo utilizando el método de regresión de los cuadrados mínimos, se juntaron mediciones con ambientes si= milares. No se incluye una discriminación con los puntos que tienen línea de vista y= los que no (LOS & NLOS).

 

Modelos empíricos

Los resultados del modelo gradiente obtenido, utilizando el método de regresión= de los mínimos cuadrados, será comparado con otros modelos empíricos aceptados= en la actualidad. Se evaluarán tres modelos conocidos, modelo COST-231 Hata, modelo Oulu y el modelo Dortmund (V.S. Abhayawardhana, 2005). Adicionalment= e se tomará como guía la pérdida en espacio libre (FSL).

 

M= odelo por pérdidas en espacio libre (FSL)

Modelo que sirve como guía para nuestro análisis, basado en un cálculo puramente analítico, el FSL (ecuación de Friis) se calcul= a:

 

 

<= span style=3D'font-size:11.0pt;mso-bidi-font-size:12.0pt;font-family:"Times Ne= w Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC;mso-far= east-language: ES-TRAD;mso-bidi-language:AR-SA'>

(2)

 

Donde d es la distancia en km, f es la frecuencia de operación del sistema expres= ada en MHz, y las unidades del modelo son en decibelios (dB).

 

M= odelo Cost-231 Hata

Modelo altamente usado para predecir pérdidas por propagación en sistemas inalámbricos. Es un modelo basado en el modelo Okumura= -Hata (Commission), 1999), pero con un f= actor de corrección indicado para trabajar en un rango de frecuencias entre 500 M= Hz y 2000 MHz. Este factor de corrección se aplica para ambientes urbanos, sub-urbanos y rurales, dependiendo el caso. La ecuaci= ón básica para la pérdida de propagación con este modelo,<= /span> viene dada por:

 

 

<= o:p>

(3)

 

Donde, f es la frecuencia en MHz, d es la distancia en km y =  es la altura de la antena estática sobre= el nivel del suelo, en metros. El parámetro cm es igual a 0 dB para ambientes sub-urbanos o abiertos; mientras que toma el valor de 3 para ambientes urba= nos. El parámetro = , viene definido para ambientes urbanos (Hata, 1980), como:

 

 

(4)

 

Y para ambientes abiertos o sub-urbanos,

 

 

(5)

 

Donde, =  es la altura de la antena del nodo móvil= sobre el nivel del suelo (Anderson, 2003).

 

C= oeficiente de Regresión

Basados en la ecuación 1, se derivan dos modelos que son el modelo de la ciudad de = Oulu (Finlandia) y el modelo de la ciudad de Dortmund. Para el modelo Oulu se ti= ene un =  dB con un valor de = . Para el modelo de Dortmund se tiene  =  dB con un valor de = , donde n corresponde= al gradiente distancia-potencia.

 

Resultados y discusión

Figura 5 muestra cada punto m= edido dentro de las instalaciones de la ESPOL, se los ha referenciado con nombres pertenecientes a circuitos tomados en las facultades correspondientes como = son FIEC (color verde) y FCNM (color rojo). Se ha propuesto un modelo de pendie= ntes múltiples debido a los diferentes ambientes que presenta el área de medició= n. Los puntos FIEC representan un ambiente cubierto por vegetación, mientras q= ue los puntos FCNM representan un ambiente abierto/suburbano. Basado en estos resultados, el valor de la pendiente de los puntos FIEC es -45.623 dBm/m; mientras que, la pendiente de los puntos FCNM es -58.264 dBm/m.<= /span>

 

Figura <= /span>5<= /span><= /p>

Mediciones del RSSI

3D"Gráfico,

 

Figura 6 muestra la dispersión entre los datos, a través de un diagrama de cajas, donde se observa la variabilidad debido a los ambientes donde fueron tomados. Se observa que la media del RSSI de los datos FIEC se encuentra en   -98 dBm, con una predominancia de valo= res en el rango de -93 a -103 dBm. Para los datos FCNM se tiene una media del RSSI igual a -120 dBm, con un rango de datos comprendidos entre -114 a -125 dBm.= Con este antecedente, se opta por la obtención de un modelo de gradiente múltip= le para las pérdidas por propagación del modelo LoRa.

Figura <= /span>6<= /span>

Dispersión de datos según el ambiente

3D"Gráfico,

 <= /p>

Basado en la intensidad de la señal recibida (RSSI), el cálculo de la pérdida por propagación viene dada por la fórmula:

 

(6)

 

Donde, SNR representa la medida señal-ruido captada por el dispositivo,  =  es la potencia efectiva isotrópica irrad= iada, =  representa la ganancia de la antena usad= a en la recepción (Juha Petäjäjärvi, 2015). Figura 7 muestra el modelo de pérdida por propagación obtenido de las mediciones (línea cian), junto con = sus dos gradientes = =3D 4.56 para zonas c= ubiertas con vegetación y = =3D 5.83 para espacios abiertos y/o suburbanos. Adicionalmente, se presenta en línea entre cortada roja el modelo de espacio libre, para tenerlo como referencia. Es importante recalcar que los resultados mostrados corresponden a la conexión de bajada = (downlink) del nodo móvil, dada por el intercambio de paquetes entre nodos. También se muestran los modelos de Cost-231 (línea ro= sa), modelo Oulu (línea azul) y modelo Dortmund (línea verde).

 

Figura <= /span>7<= /span>

Comparación de modelos por pérdidas en propagación

 

3D"Gráfico

Descripción

El modelo resultante obtenido viene dado por la ecuación:

 

(7)

 

Donde, =  corresponde a la pérdida de propagación = de la señal recibida, dada por (6).   =  y =  son los gradientes obtenidos, d es la distancia en metros y =  =3D 178.5 metros.

 

Basado en el resultado del modelo obtenido, se calculó la desviación estándar =  que existe, debido al debilitamiento por sombra (SF) para cada ambiente.

 

La Tabla 2 muestra el valor del gradiente obtenido para cada ambiente junto co= n su desviación estándar por debilitamiento de sombra.

 

Tabla 2

(dB)

Con vegetación (FIEC)

4.56

5.1046

Abierto (FCNM)

5.83

6.5246

 

Se puede observar que el modelo empírico adoptado para comunicaciones inalámbr= icas COST-231 posee un mayor gradiente que el resto de model= os empíricos. El modelo obtenido en la ESPOL posee una divergencia con respect= o a los modelos Oulu y Dortmund, debido a las condiciones físicas en que fueron tomados, ya que los dispositivos móviles se encontraban a diferentes altura= s; adicionalmente, la presencia de edificios, zonas verdes que provocaban la ausencia de línea de vista en la mayoría de las mediciones y la variación d= el terreno.

 

Para la obtención de una métrica de comparación de las mediciones adquiridas, se calculó el error porcentual promedio “e”, de los métodos en estudio de este artículo, mostrados en la Tabla 3.

 

Tabla 3 

FSL

52.90 %

Oulu

6.02 %

Dortmund

6.85 %

Cost-231

21.03 %

 

Conclusiones

Este artículo propone un modelo empírico de propagación que se evalúa con medici= ones realizadas dentro del campus Prosperina de la ES= POL. Se obtiene un modelo de gradiente múltiple debido a la presencia de zonas c= on vegetación y zonas abiertas.

 

Con los resultados obtenidos a través de mediciones y comparación de modelos, el modelo Oulu presenta menor error porcentual medio “e”, con respecto al mode= lo de gradiente múltiple obtenido, como se muestra en la Tabla 3, en un rango = de distancias comprendido entre 10 a 5000 metros.

 

La variación en las mediciones con los demás modelos se atribuye a la irregularidad que se tiene en el campus de la ESPOL y a la línea de vista existente o no al momento de realizarlas. Para futuras investigaciones se recomienda la utilización de circuitos en otras direcciones para tener un modelo más preciso sobre la pérdida de propagación dentro de este campus, y complementar el modelo gradiente para un ambiente cubierto de vegetación y = otro al aire libre.

El ambiente cubierto por vegetación presenta un gradiente (pendiente) menor a = las mediciones realizadas en espacio abierto en la FCNM, esto es debido a que existe mayor distancia, y la mayoría de puntos no poseen línea de vista.

 

Los modelos propuestos se desarrollan empleando una frecuencia de 915 MHz, que = es la que utiliza el estándar LoRa, para futuras investigaciones se puede establecer un modelo con otra tecnología LPWAN com= o Sigfox, ya que trabaja en la misma banda no licenciad= a, para establecer diferencias y comparación entre modelos.<= /p>

 

Otro aspecto importante de esta investigación es que se realiza en la estación climática de invierno, con días soleados, por lo que posiblemente otros factores como la temperatura, horario e incluso estación de verano, podrían afectar el modelo propuesto.  =

 

Reconocimientos

Este trabajo ha sido financiado parcialm= ente por CEDIA, a través del Grupo de Trabajo de IoT= y Ciudades Inteligentes.

Los autores desean expresar su agradecimiento a la Escu= ela Superior Politécnica del Litoral (ESPOL) por la prestación y pronta gestión= de permisos para acceder y hacer uso de sus instalaciones.

 

Referencias

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