Avances en arquitectura y tecnología decomputadores

Actas de las Jornadas SARTECO 2017

Málaga, 19 a 22 de Septiembre de 2017

Avances en arquitectura y tecnología de computadoresActas de las Jornadas SARTECO 2017

Editores: Rafael Asenjo, Ángeles Navarro, Arturo González-Escribano, Diego R. Llanos,Sergio Cuenca Asensi, Jesús González Peñalver

(c) 2017, Jornadas SARTECO

ISBN-13: 978-84-697-4835-0

Málaga, 2017

7483507884699

ISBN 978-84-697-4835-0

Comités de coordinación

Comité de Dirección de las Jornadas SARTECOPresidente de Honor:Francisco Tirado Fernández (Universidad Complutense de Madrid)Presidenta:Inmaculada García Fernández (Universidad de Málaga)Vicepresidente:Victor Viñals Yufera (Universidad de Zaragoza)Secretaria:Katzalin Olcoz Herrero (Universidad Complutense de Madrid)

Comité de Organización

Rafael Asenjo Plaza (Universidad de Málaga)Francisco Corbera Peña (Universidad de Málaga)Mł Ángeles González Navarro (Universidad de Málaga)Sonia González Navarro (Universidad de Málaga)Oscar Plata González (Universidad de Málaga)Andrés Rodríguez Moreno (Universidad de Málaga)Luis Felipe Romero Gómez (Universidad de Málaga)Manuel Ujaldón Martínez (Universidad de Málaga)Diego R. Llanos Ferraris (Universiadd de Valladolid)Arturo González Escribano (Universiadd de Valladolid)

Comité de Coordinación JP 2017Ramón Beivide Palacios (Universidad de Cantabria)Jesús Carretero Pérez (Universidad Carlos III de Madrid)José Duato Marín (Universidad Politécnica de Valencia)Inmaculada García Fernández (Universidad de Málaga)Antonio Garrido Del Solo (Universidad de Castilla la Mancha)Emilio López Zapata (Universidad de Málaga)Emilio Luque Fadón (Universitat Autònoma de Barcelona)Alberto Prieto Espinosa (Universidad de Granada)Francisco José Quiles Flor (Universidad de Castilla la Mancha)Ana Ripoll Aracil (Universitat Autònoma de Barcelona)Francisco Tirado Fernández (Universidad Complutense de Madrid)Mateo Valero Cortés (Universidad Politècnica de Catalunya)Victor Viñals Yúferas (Universidad de Zaragoza)

Comité de Coordinación JCER 2017Jesús González Peñalver (Universidad de Granada)Sergio Cuenca Asensi (Universidad Alicante)Miguel A. Vega Rodríguez (Universidad de Extremadura)Miguel Damas Hermoso (Universidad de Granada)Antonio Martínez Alvarez (Universidad Alicante)Gustavo Sutter (Universidad Autónoma de Madrid)Ignacio Bravo (Universidad de Alcalá)José Torres (Universidad Valencia)Jordi Carrabina (Universitat Autònoma de Barcelona)Juan Suardíaz (Universidad Politécnica de Cartagena)Jesús Barba Romero (Universidad de Castilla la Mancha)Goiuria Sagardui Mendieta (Mondragon Unibertsitatea)Jorge Portilla Berrueco (Universidad Politénica de Madrid)

Comité de Programa JCER 2017

Egoitz Arruti (Mondragon Unibertsitatea)Marta Beltran (Universidad Rey Juan Carlos)Francisco Bonin-Font (Universitat de les Illes Balears)David Castells Universitat (Universidad Autònoma de Barcelona)Javier Díaz (Universidad de Granada)Juan Carlos Díaz (Universidad de Extremadura)Luis Entrena (Universidad Carlos III de Madrid)Leire Etxeberria (Mondragon Unibertsitatea)Eduard Fernandez-Alonso (Recore Systems)Rodolfo García-Bermúdez (Universidad Técnica de Manabí)Juan A. Gomez-Pulido (Universidad de Extremadura)José María Granado (Universidad de Extremadura)Juan Antonio Holgado (Universidad de Granada)Miren Illarramendi (Mondragon Unibertsitatea)Antonio Jimeno-Morenilla (Universidad de Alicante)Gustavo Marrero (Universidad de Las Palmas de Gran Canaria)Francisco Moya (Universidad de Castilla-La Mancha)Joaquín Olivares (Universidad de Córdoba)Alberto Ortiz (Universitat de les Illes Balears)Fernando Pardo (Universitat de València)Jon Perez (Ikerlan)Francisco Ramos (Schneider Electric)Lluís Ribas-Xirgo (Universitat Autonoma de Barcelona)Fernando Rincón (Universidad de Castilla-La Mancha)Jose Sousa (Instituto de Engenharia de Sistemas e Computadores)Elías Todorovich (Universidad Nacional del Centro, Argentina)Javier Valls (Universitat Politècnica de València)Eugenio Villar (Universidad de Cantabria)

Jornadas SARTECO 2017 IX

Lenguajes, compiladores y herramientas de programación y ejecución paralela

El camino desde la maleabilidad MPI hasta las cargas de trabajos adaptativas 431Sergio Iserte, Rafael Mayo Gual, Enrique S. Quintana-Ortí, Vicenç Beltran, Antonio J. Peña

GrPPI: Una interfaz de patrones paralelos genérica 437David Del Rio Astorga, Manuel F. Dolz, Javier Fernandez, Jose Daniel Garcia

To distribute or not to distribute: the question of load balancing for performance or energy 445Esteban Stafford, Borja Pérez, Jose Luis Bosque, Ramón Beivide, Mateo Valero

Técnicas de implementación de Stencils en multi-GPU distribuidas 453Senmao Ji, Arturo Gonzalez-Escribano, Diego R. Llanos

Redes y Comunicaciones

Arquitectura de Red con Reserva de Anchos de Banda para Sistemas Heterogéneos Basados enFPGAs

463

Tomás Picornell, José Flich, Rafael Tornero, Jose Maria Martínez

Modelado de una Red Fotónica para Computación Exascale 473José Duro, Salvador Petit, Julio Sahuquillo, Maria Gomez

Diseño y evaluación de sistemas de interconexión basados en tecnología óptica integrada en sili-cio

481

Juan-José García-Castro Crespo, Francisco Alfaro, Jose L. Sanchez

Estudio de Protocolos de Descubrimiento de Vecinos en Redes Inalámbricas Ad Hoc 487Jose Vicente Sorribes Diaz, Lourdes Peñalver Herrero

GatcomSUMO: primeros pasos para simulaciones VANET 497P. Pablo Garrido Abenza, Pablo Piñol Peral, Manuel P. Malumbres

Characterization of Vehicular Communications at Urban Intersections 507Seilendria Ardityarama Hadiwardoyo, Enrique Hernández-Orallo, Carlos Calafate, Juan-Carlos Cano, Pietro Manzoni

Gestión de la movilidad de usuarios basada en SDN para aplicaciones multicast en WLANs 513Estefanía Coronado, Roberto Riggio, Jose Villalon, Antonio Garrido

Análisis de Rendimiento de la Transmisión de Flujos de Vídeo en Entornos WLAN 521Alejandro Molina Galán, Manuel Perez Malumbres, Otoniel Lopez Granado, Miguel Martinez-Rach, Pablo Piñol Peral

Evaluación del uso de redes inalámbricas sub-GHz en la difusión de mensajes en redes oportu-nistas

531

Jorge Herrera-Tapia, Enrique Hernández-Orallo, Pietro Manzoni, Oscar Alvear, Carlos Ca-lafate, Juan-Carlos Cano

Evaluando el Consumo de Redes BLE/LoRaWAN para IoT 537Celia Garrido-Hidalgo, Diego Hortelano, Luis Roda-Sánchez, Teresa Olivares, M.CarmenRuiz

Malla Bluetooth Low Energy para la nueva Industria 4.0 545Diego Hortelano, Luis Roda-Sánchez, Celia Garrido-Hidalgo, Teresa Olivares, M.CarmenRuiz

Characterization of Vehicular Communications atUrban Intersections

Seilendria A. Hadiwardoyo, Enrique Hernandez-Orallo, Carlos T. Calafate,Juan-Carlos Cano, Pietro Manzoni

Department of Computer Engineering (DISCA)Universitat Politecnica de Valencia

Camino de Vera s/n, 46022 Valencia, Spain{seiha, ehernandez, calafate, jucano, pmanzoni}@disca.upv.es

Abstract—Delivering safety messages in vehicular commu-nications is crucial as these messages must be delivered in areliable way and having low delay. The challenge comes whendelivering these messages in an urban scenario, where variousobstructions are present and cause line of sight restrictions. Urbanintersection is a clear example of such limitations, obstructionslike buildings affect the communication performance in the 5GHz band. Intersections have their own characteristics based onthe level of obstruction. This paper analyzes the communicationson intersections by characterizing the types of intersection interms of the delivery rate performance. Real experiments areperformed, as well as analytic modeling, to examine the impactof different types of intersection. The results show that thecommunication rate at intersections is influenced by the distanceto the intersection, the line of sight conditions, and the antennalocation. In addition, the degree of obstruction that characterizeseach intersection also affects the success ratio when deliveringmessages in a particular scenario.

Keywords—V2V, Android, GRCBox, ITS, intersections.

I. INTRODUCTION

Vehicle-to-Vehicle (V2V) communication is an ad-hoc net-working paradigm that is a vital part of Intelligent Transporta-tion systems (ITS), a concept typically referred to as VehicularAd-hoc Networks (VANETs) [1]. The diffusion of messagesin such networks should be timely and reliable, especiallyin terms of safety ITS applications [2]. A very low delayis essentially expected in critical safety applications. Thus,when facing emergency situations, relevant messages shouldbe delivered quickly and with reliability [3].

Under such critical situations, several message dissemina-tion schemes have been proposed for effective communicationsby avoiding the broadcast storm problem [4]. It is also impor-tant to consider the density of vehicles, roadmap, as well asintersection patterns, as they were found to be key factors forthe dissemination process [5]. It is also worth mentioning thatthe geolocation system plays an important role in the safetymessage dissemination process in urban scenarios, as the citedschemes depend on GPS information [6].

This paper addresses real experiments in the city of Valen-cia (Spain) to evaluate the impact that intersection characteris-tics will have on vehicular communications in the 5 GHz band.Different types of intersections are considered in this work,which defines the classification of communication restrictions.

Based on the field tests results, we then modeled the successfulprobability of packet sending at each intersection along withthe antenna location. The results obtained can then be trans-parently integrated in simulation platforms to achieve morerepresentative results.

The results obtained showed that the degree of obstructionat intersections affect the performance of packet delivery andits communication range. Also, placing the antenna inside avehicle would cause the delivery ratio to be reduced comparedto putting it at the top of the vehicle.

The paper is organized as follows: in the next sectionwe present the related works. In section III we describe themethodology and procedures followed in our work. Then, insection IV, we detail the intersections chosen for the experi-ments. Experimental results are then presented and discussedin section V, followed by the intersection modeling basedon the obtained results in section VI. Finally, in section VII,conclusions and future works are presented.

II. RELATED WORKS

Intersection management is a relevant issue that has beenaddressed in recent years. In VANETs, and especially at thenetwork layer, some works are proposed with geographicalconditions approaches, for example in [7], road layouts withintersections are considered. Other works involved selectingintersections as points of relay for routing with the aid ofnavigation maps and road IDs [8].

Intersections can be classified into different types accordingto their degree of signal obstruction. This depends on differenttype of blockages, such as buildings, vegetation or vehicles.Buildings blocking the line of sight (LOS) clearly affect therange of communications. In [9], the authors find that thiskind of communication is possible up to 155 m range, and itdepends mainly on the street width. If we look the effect ofvegetation, the work by [10] in rural environments showed that,under non-line-of-sight (NLOS) conditions, the delivery ratedepends on types of blockages, such as plants and vegetationtypes, as well as on the season of the year. Later research[11] investigated the impact of the presence of vehicles interms of communications interference, finding that a singlevehicle would affect the whole transmission performance. Acategorization of different types of intersections is proposedin [12]. These authors have found through simulation that

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communication performance depends on the level of obstruc-tions available. Another consideration would be the distancebetween the transmitter and the center of the intersection. Theauthors in [13] show that having the transmitter close to theintersection clearly improves the delivery performance.

Our work aims to investigate the transmission performancedepending on the different types of intersection at differentdistances. The uniqueness of our work is the frequency bandand performance metrics used in the experiment, as well as thevarious types of intersection and antenna locations tested. Inaddition we have introduced a new model for each intersectionthat defines the packet delivery ratio based on the distance tothe intersection.

III. METHODOLOGY

This section explains the methodology used in our experi-ments. We aim to measure the packet delivery ratio on differenttypes of intersections, and for different antenna locations,depending on the distance to the center of the intersection.

A. General Overview

A combination of appropriate hardware and software uti-lization was taken to measure the packet delivery ratio anddata analysis methodology. In our experiments, we have usedan Android mobile phone and a GRCBox [14]. In addition,a custom application has been installed for performing ex-periments by transmitting messages similar to DecentralizedEnvironmental Notification Messages (DENM), described inthe European ETSI standard [15]. The use of smartphoneshave been recently tested in different vehicular communicationresearch works [16], [17]. However, in our case, to achieveVANET communications in the 5 GHz band, we use a GRCBoxas our on board unit, thereby achieving all required V2Vcommunications functionality.

Two vehicles are needed in these experiments for realmobile communications, in which one will be the sender nodeand the other one the receiver node. The vehicle acting asreceiver will be static, and located near the center of theintersection. The one acting as sender will be mobile, it will becrossing the intersection repeatedly following the designatedpath. The communications occur when the sender vehicleis moving while sending packets that contain the sender’sexact location; the receiver vehicle receives those packets andstores them in as a log file in the Android device’s internalstorage. The logfile is then analyzed to extract the locationcoordinates in order to get the distance between the sender andthe receiver, and the ratio of successful packet delivery. Thisinformation will allow us to get an understanding of the effectof different intersection characteristics on message deliveryperformance in vehicular environments, as it can be modeledfor further analysis. Concerning the antenna location, we usedtwo different positions: one inside the vehicle on its dashboardand another one outside the vehicle on its rooftop.

B. GRCBox Overview

Our work require some kind of On Board Unit (OBU)device that provides ad-hoc network connectivity in the 5.8GHz band. This way, as we use Android smartphones togenerate packets, we would not need to root the phone to

Fig. 1: GRCBox Hardware module connected to a VANETwith three different nodes [14].

enable the ad-hoc network, which is practical for the end users.The adopted device, called GRCBox [14], provides ad-hocconnectivity and has multiple interface based on RaspberryPi; it also provides a wider communication range comparedto the one achieved by smartphones. This way, combining anAndroid Smartphone and a GRCBox, V2X communication canbe achieved.

The use of the GRCBox in this work is to forward packetsgenerated by the Android tool. One of these devices is locatedin the sending vehicle, and another one in the receiving one,acting the GRCBox as a gateway for packets dissemination inthe VANET. The application should be compatible with theGRCBox by configuring appropriate rules. The configurationitself is done by a specific Android tool that will automaticallymatch the ports and interfaces. In particular, one interface(inner) will acts as an Access Point (AP), and the other one(outer) will form a VANET with other GRCBoxes; it shouldbe configured separately so as to provide communications inthe 5.8 GHz band.

C. Android Tool

A specific Android-based application was created for thepurpose of measuring the packet delivery ratio at differentlocations. The tool contains libraries and plugins to connectto the GRCBox module making it fully compatible with theGRCBox platform. The user would not need to configure theconnection, as the tool allows automatic connectivity to theouter interface performing VANET communications.

The application initially checks the connection betweenthe smartphone and the GRCBox device. Inputs are requiredfrom users, such as the log file name, packet transmissionrate, and the packet size. The application will then generatemessages similar to typical CAM/DENM messages [15], [18].The packets have a size of about 300 bytes, and will be sentat a rate of 30 packets per second. GRCBox connectivity willbe checked on the receiver’s side as well, where the userintroduces the log file name and activates the receiving process.The whole process is started when the user that sends packetsstarts transmitting, and the user receiving packet starts gettingthem. The process ends when both users (sender and receiver)press the stop button, which will make the application storethe log file in each device’s internal storage.

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D. Data Analysis

The log files, which contain geographic information storedin the Android device’s storage, were used for data analysis.They contain coordinates of the sender and the receiver ve-hicles, which can be used to calculate the distance betweenthe center of the intersection and the sending vehicle. Thiswas done by using Geographiclib [19]. The log files were alsoanalyzed to obtain the delivery ratio by comparing the numberof packets sent and received at the receiver’s side.

IV. SELECTION OF TARGET INTERSECTIONS

Three different types of intersections having different char-acteristics are selected for the experiments. The characteristicsare defined by the degrees of obstruction. The location of theexperiments is shown in figures 2, 3, and 4. The trajectoryand the direction of the sending vehicle is indicated with ayellow line and the corresponding arrow. The receiving vehicleis presented with a yellow point in each figure.

For intersection 1, we have chosen an open space with veryhigh levels of LOS. It was located in the outskirts of the city ofValencia where there are more fields than residential buildings(latitude 39.483920, longitude -0.333793). In an aerial view(see figure 2) we can see that the only blockages are twobuildings, meaning that the degree of obstruction is very lowwhile the receiving vehicle is located near the center of theintersection, mostly surrounded by open fields.

For the second intersection, the experiment was done in aresidential area (latitude 39.473695, and longitude -0.332307),where buildings massively present forming an urban canyon(see Figure 3). In this case, the LOS is minimum even if welocate the receiving vehicle near the center of the intersectionsince it is surrounded by buildings. In this intersection, theworst-case conditions are expected, meaning that the deliveryratio should be minimum compared to the other scenarios.

Intersection 3 is an intersection having a moderate degreeof obstruction when compared to the previous ones. Thisintersection is located near the university campus area withsome residential buildings, meaning that blockages includeboth buildings and vegetation (latitude 39.473848, and lon-gitude -0.341330). In this intersection, as we can see in figure4, the environment has buildings, trees, and some open space

Direction of theSending Vehicle

Receiver

Fig. 2: Location and the trajectory for Scenario 1 (Open)

Direction of theSending Vehicle

Receiver

Fig. 3: Location and the trajectory for Scenario 2 (Buidings)

area surrounding the trajectory. It is expected that the outcomeof the communication in this scenario would have a rate inbetween the first and the second intersection scenarios.

V. EXPERIMENTAL RESULTS

The coordinates of the vehicles when packets are eithertransmitted or successfully received can be extracted thanksto the log files. Based on this real experiment, we created aheatmap that highlights those locations where the sending ve-hicle has successfully delivered packets. The data was gatheredby five vehicle runs at each intersection, and also by varyingthe antenna location (located either on the dashboard or therooftop).

A heatmap of messages received in the first intersection(open scenario) is shown in figure 5a. It has the lowest degreeof obstruction, and the antenna is located inside the vehicle inthis case. The map shows that packets are successfully sentfrom locations with a high level of LOS in the trajectory.Regarding the delivery ratio, near the center we obtain thehighest values, as expected. On the other hand, it gets lowerwhen moving away until about 300 meters.

When we put the antenna on the rooftop (see figure 5b) weobserve that the delivery ratio significantly improved comparedto the previous scenario. Now, we find that the physical

Direction of theSending Vehicle

Receiver

Fig. 4: Location and the trajectory for Scenario 3 (Trees)

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Fig. 5: Heat Maps for the different scenarios. Each plot shows the packet delivery ratio depending on the sender position, scenario(open, building and trees) and antenna location (dashboard, rooftop).

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obstructions that are present still affect the communication,but only by reducing the delivery ratio. In particular, a slightdecrease in this ratio is detected at the points where LOS isobstructed by the two available buildings.

For the second intersection (building scenario), when weput the antenna in the dashboard, the results are the worstamong all. Figure 5c shows that, being in an urban canyon,communications for this kind of intersection become quitechallenging, and the maximum range achieved is about 50 me-ters. In terms of delivery ratio, sharply descending values occurwhen the sending vehicle moves away from the intersection.

Figure 5d shows the results when we put the antenna onthe rooftop. Compared to the previous scenario, we can seethat the delivery ratio, as well as the coverage range, becomehigher. In particular, communications reach about two blocksaway with about 100 meters of range, having a delivery ratiothat we find acceptable for this scenario.

The results obtained in the third intersection (trees sce-nario), having the antenna in the dashboard, showed bettercommunication performances compared to the previous one.Figure 5e shows that the transmission range is higher than forintersection 2, and that the delivery ratio is also higher. Thisoccurs thanks to the open space present in the location, byhaving a street that is wider, and because fewer obstacles arepresent compared to the previous case (buildings scenario).However, compared to the first intersection (open scenario),the range achieved by communications in this intersection islower.

Finally, figure 5f shows the results obtained when havingthe antenna on the rooftop. Despite the results are betterthan putting the antenna in the dashboard, we find that thedistance covered is mostly maintained (up to 150 meters) asthe delivery ratio drops when a blockage is present (in thiscase the buildings located on the east side of the trajectory),although more packets are received than for the dashboard casefor a same distance.

VI. INTERSECTION MODELING

The results obtained from each intersection can be usedfor creating an intersection model. We aim at a generic modelthat can be parametrized so as to represent each possibleintersection, thereby being easily integrated in simulation tools.

To derive the model of the different types of intersectionswe have obtained the number of packets transmitted andreceived for each position registered. Next, having a distancerange distribution, we can determine the packet delivery ratio.Finally, a curve fitting process was done to derive optimalparameters along with its corresponding plots (see figure 6).

A common model was expected to be suitable for thedifferent types of intersections and antenna positions. Weintended to have only one parameter that varies within themodel while still representing all available scenarios. For thedifferent types of intersections and antenna locations, we havetried to find the fitting function, and evaluated several ones likepolynomial and power functions. According to our analysis,the best fitting was achieved using a Gaussian function:

f(x) = ae�(x�b)2

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#1 dash (c=56.57)#2 dash (c=30.92)#3 dash (c=38.98)#1 roof (c=240.72)#2 roof (c=71.82)

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Fig. 6: Delivery Ratio at Intersections with Both AntennaPositions

TABLE I: c parameter and �2 error values for each scenarioand antenna position.

Antenna on Antenna ondashboard rooftopc �2 c �2

Intersection 1 56.57 15.35 240.72 30.15Intersection 2 30.92 12.84 71.82 21.67Intersection 3 38.98 6.18 136.15 20.14

We have fixed the delivery ratio to be one at the intersec-tion, or distance zero, meaning that a is one and b is zero.So, the parameter used for fitting is c, that is, the standarddeviation �. Thus, the following function is obtained:

f(x) = e�x2

2c2 (2)

The exponential function calculates the delivery ratio withdistance x. The probability becomes 0 as the distance valuegets higher. The value of the constant c reflects the variationof data values and depends on the actual scenario.

Looking deeper into the fitting results and its fitting errors,equation 2 has parameter c, which is the standard deviationof the Gaussian function. This parameter allows adapting thefitting curve for each type of intersection and antenna location.The values of the parameter for the different conditions aredefined in table I. The parameter decreases for lower radioranges at intersections, being also related to the packet deliveryratio. In particular, we find that the packet delivery ratio for acertain distance attains a higher value when this parameter isalso high.

We can see in detail that the first intersection has the high-est c values, being the lowest values achieved by the secondintersection. On the other hand, the c values for intersection 3are as expected, in between the other two intersections. If wefocus on the antenna locations, the value of parameter c forthe rooftop in each intersection achieves much higher valuesthan those for the dashboard case. In fact, for intersection1, it achieves quite significant differences, being about four

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times greater for the rooftop case compared to the dashboardcase. The reason behind this phenomenon is that the signalpower drops not only with an increasing distance, but also bythe materials in between sender and receiver; in this case theantenna located in the rooftop would not suffer interference bythe vehicle’s materials, such as its dashboard and windshield.

The fitting error, represented as �2, is available in tableI. It is obtained as the sum of the squares of the differencesbetween the model function and the real delivery ratios fromthe experiments. For the dashboard scenarios, the model fittingis seen as the most accurate. The reason why this behavioroccurs is that the range does not reach the lower values, as therange is not large enough.

VII. CONCLUSIONS AND FUTURE WORKS

In this paper we studied the packet delivery ratio atintersections when facing different degrees of obstructions anddifferent antenna locations. Experimental results showed thatthe levels of blockage and the positioning of antenna clearlyaffects the transmission range and delivery probability. Havingthe lowest degree of obstruction. In addition, by positioningthe antenna outside the vehicle, we achieve the best results.The obtained results are also analyzed to derive a modelthrough curve fitting and regression. Our study finds that,by varying only one parameter, a modified Gaussian functioncan represent different types of intersection conditions. In thefuture, these results can be brought to simulation experimentsfor further validation. We also plan to do further experimentswith more advanced geolocation devices.

ACKNOWLEDGMENT

This work was partially supported by the ”Ministerio deEconomıa y Competividad, Programa Estatal de Investigacion,Desarollo e Innovacion Orientada a los Retos de la Sociedad,Proyectos I+D+I 2014”, Spain, under Grants TEC2014-52690-R and BES-2015-075988.

REFERENCES

[1] P. Papadimitratos, A. De La Fortelle, K. Evenssen, R. Brignolo, andS. Cosenza, “Vehicular communication systems: Enabling technologies,applications, and future outlook on intelligent transportation,” IEEECommunications Magazine, vol. 47, no. 11, pp. 84–95, 2009.

[2] M. Sepulcre and J. Gozalvez, “Experimental evaluation of cooperativeactive safety applications based on v2v communications,” in Proceed-ings of the ninth ACM international workshop on Vehicular inter-networking, systems, and applications. ACM, 2012, pp. 13–20.

[3] X. Ma, X. Chen, and H. H. Refai, “Performance and reliability of dsrcvehicular safety communication: a formal analysis,” EURASIP Journalon Wireless Communications and Networking, vol. 2009, no. 1, p. 1,2009.

[4] F. J. Martinez, C.-K. Toh, J.-C. Cano, C. T. Calafate, and P. Manzoni,“A street broadcast reduction scheme (sbr) to mitigate the broadcaststorm problem in vanets,” Wireless personal communications, vol. 56,no. 3, pp. 559–572, 2011.

[5] M. Fogue, P. Garrido, F. J. Martinez, J.-C. Cano, C. T. Calafate, andP. Manzoni, “Pawds: A roadmap profile-driven adaptive system foralert dissemination in vanets,” in Network Computing and Applications(NCA), 2011 10th IEEE International Symposium on. IEEE, 2011, pp.1–8.

[6] J. A. Sanguesa, M. Fogue, P. Garrido, F. J. Martinez, J.-C. Cano, andC. T. Calafate, “A survey and comparative study of broadcast warningmessage dissemination schemes for vanets,” Mobile Information Sys-tems, vol. 2016, 2016.

[7] H. Saleet, R. Langar, K. Naik, R. Boutaba, A. Nayak, and N. Goel,“Intersection-based geographical routing protocol for vanets: a proposaland analysis,” IEEE Transactions on Vehicular Technology, vol. 60,no. 9, pp. 4560–4574, 2011.

[8] T. Acarman, C. Yaman, Y. Peksen, and A. U. Peker, “Intersection basedrouting in urban vanets,” in 2015 IEEE 18th International Conferenceon Intelligent Transportation Systems. IEEE, 2015, pp. 1087–1092.

[9] H. Schumacher, H. Tchouankem, J. Nuckelt, T. Kurner, T. Zinchenko,A. Leschke, and L. Wolf, “Vehicle-to-vehicle ieee 802.11 p performancemeasurements at urban intersections,” in 2012 IEEE InternationalConference on Communications (ICC). IEEE, 2012, pp. 7131–7135.

[10] H. Tchouankem, T. Zinchenko, H. Schumacher, and L. Wolf, “Effectsof vegetation on vehicle-to-vehicle communication performance atintersections,” in Vehicular Technology Conference (VTC Fall), 2013IEEE 78th. IEEE, 2013, pp. 1–6.

[11] H. Tchouankem and T. Lorenzen, “Measurement-based evaluation ofinterference in vehicular ad-hoc networks at urban intersections,” in2015 IEEE International Conference on Communication Workshop(ICCW). IEEE, 2015, pp. 2381–2386.

[12] K. Mahler, P. Paschalidis, M. Wisotzki, A. Kortke, and W. Keusgen,“Evaluation of vehicular communication performance at street intersec-tions,” in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). IEEE, 2014, pp. 1–5.

[13] T. Mangel, M. Michl, O. Klemp, and H. Hartenstein, “Real-worldmeasurements of non-line-of-sight reception quality for 5.9 ghz ieee802.11 p at intersections,” in International Workshop on CommunicationTechnologies for Vehicles. Springer, 2011, pp. 189–202.

[14] S. M. Tornell, S. Patra, C. T. Calafate, J.-C. Cano, and P. Manzoni,“Grcbox: extending smartphone connectivity in vehicular networks,”International Journal of Distributed Sensor Networks, vol. 2015, p. 5,2015.

[15] Intelligent Transport Systems (ITS); Vehicular Communications; BasicSet of Applications; Part 3: Specification of decentralized environmentalnotification basic service, ETSI TS 102 637-3, 2010.

[16] W. Vandenberghe, I. Moerman, and P. Demeester, “On the feasibilityof utilizing smartphones for vehicular ad hoc networking,” in ITSTelecommunications (ITST), 2011 11th International Conference on.IEEE, 2011, pp. 246–251.

[17] R. Frank, W. Bronzi, G. Castignani, and T. Engel, “Bluetooth lowenergy: An alternative technology for vanet applications,” in Wirelesson-Demand Network Systems and Services (WONS), 2014 11th AnnualConference On. IEEE, 2014, pp. 104–107.

[18] Intelligent Transport Systems (ITS); Vehicular Communications; BasicSet of Applications; Part 3: Specification of decentralized environmentalnotification basic service, ETSI TS 102 637-2, 2010.

[19] C. F. Karney, “Transverse mercator with an accuracy of a few nanome-ters,” Journal of Geodesy, vol. 85, no. 8, pp. 475–485, 2011.

512 Jornadas SARTECO 2017