# available bandwidth estimation in gpsr for vanets

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Available Bandwidth Estimation in GPSR for VANETs

Carolina Tripp-BarbaTelematic Engineering Dept.

Universitat Politècnica deCatalunya (UPC)Barcelona, Spain

Mónica Aguilar IgartuaTelematic Engineering Dept.

Universitat Politècnica deCatalunya (UPC)Barcelona, Spain

Luis Urquiza AguiarTelematic Engineering Dept.

Universitat Politècnica deCatalunya (UPC)Barcelona, Spain

[email protected] Mohamad

MezherTelematic Engineering Dept.

Universitat Politècnica deCatalunya (UPC)Barcelona, Spain

Aníbal Zaldívar-ColadoFaculty of Computer Science

University of SinaloaMazatlan, Mexico

Isabelle Guérin LassousUniversité Claude Bernard

Lyon 1, LIPLyon, France

ABSTRACTThis paper proposes an adaptation of the collision probabil-ity used in the measure of the available bandwidth designedfor Mobile Ad hoc Networks (MANETs) and which is de-scribed in ABE [12]. Instead, we propose a new ABE+ thatincludes a new function to estimate the probability of losses.This new function has been specially designed for VehicularAd hoc Networks, to be suited to the high mobility and vari-able density in vehicular environments. In this analysis wedo not only consider the packet size, but also other metrics,such as, density and speed of the nodes. We include theABE+ algorithm in the forwarding decisions of the GBSR-B protocol [14], which is an improvement of the well-knownGPSR protocol. Finally through simulations, we comparethe performance of our new ABE+ compared to the originalABE. These results show that ABE+ coupled with GBSR-B achieves a good trade-off in terms of packet losses andpacket end-to-end delay.

Categories and Subject DescriptorsC.2.1 [Computer-Communication Networks]: NetworkArchitecture and Design - Wireless Communication; C.4[Performance of Systems]: Design Studies, MeasurementTechniques

General TermsDesign, Performance, Measurement

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KeywordsAvailable bandwidth estimation, VANET, forwarding deci-sion, linear regression, QoS

1. INTRODUCTIONOver the last years, network connectivity support in trans-

portation systems has been one of the main research topicsin computer networks engineering [4]. Most of the researchhas been focused on routing mechanisms [7] and on networkmobility support, but there are still additional aspects tocover.

Network support in transportation systems has two dif-ferent objectives: first, to provide connectivity to passen-gers for both business and infotainment; Secondly, to allowthe exchange of information between vehicles and the in-frastructure, in order to improve road safety and efficientdriving. The problems and challenges of each of these usesare different, and solutions, adapted to each of them, mustbe provided.

Safety, comfort driving and infotainment applications forpassengers [9] are therefore some of the main services pro-posed for Vehicular Ad hoc Networks (VANETs). Someof these services are critical, such as, for instance, colli-sion warning; other services require bandwidth, such as, forinstance, video-streaming services. For these latter, QoS(Quality of Service) provisioning is a clear challenge in VANETsdue to the intrinsic mobility of these networks.

Available bandwidth is a network parameter that is con-sidered in many QoS solutions. The available bandwidthbetween two nodes is defined as the maximum through-put at which packets can be transmitted between these twopeers without disrupting any already ongoing flow in thenetwork [12]. The estimation of this value in a wireless net-work is a difficult issue, due to some phenomena related tothe wireless environment as mobility and topology changes.This estimation is even more complicated, if the wireless net-work is a VANET, which presents special features, speciallyhigh speed of the nodes, which results in frequent routingpath disruptions.

In [13], we show that the available bandwidth estimationABE, proposed in [12] and dedicated to multihop wireless

networks (MANETs), can be of interest for VANETs. How-ever, this study also shows that using ABE directly, withoutfine-tuning it to VANETs, can improve the routing perfor-mance, but only in case of moderate mobility. In this paper,we adapt ABE to VANETs’ features. More specifically, wefocus our research on the improvement of the collision prob-ability on which ABE is based, by adapting the functionf(m), used in ABE, to vehicular scenarios. We propose anew solution for ABE, denoted ABE+. We evaluate ABE+with the routing protocol GBSR-B (Greedy Buffer StatelessRouting Building-aware) [14] and we also compare it withGPSR (Greedy Perimeter Stateless Routing) [6], as it is awell-known geographic routing protocol specially designedfor VANETs.

The paper is organized as follows: in Section 2, we re-view the main solutions for available bandwidth estimationin multihop wireless networks. We also give a brief summaryof the GPSR and GBSR-B routing. Then in Section 3, weremind the main principles of the ABE solution. In Sec-tion 4, we show how to adapt ABE to VANETs. Finally,in Section 5, we evaluate, by simulation, the new solutionABE+. We also compare it to the initial solution ABE.

2. RELATED WORK, GPSR AND GBSR-BQoS mechanisms often require an estimation of available

resources to offer services over networks with good perfor-mance. Available bandwidth is a key network parameterthat is often used in QoS solutions. In multihop wirelessnetworks, the estimation of the available bandwidth is adifficult task. Several proposals have been presented tocompute the available bandwidth on IEEE 802.11 wirelesslinks in MANETs. ABE [12] combines channel monitor-ing to estimate each node’s medium occupancy includingdistant emissions, probabilistic combination of these val-ues to account for synchronization between nodes, estima-tion of the collision probability between each pair of nodes,and variable overhead’s impact estimation. This mechanismonly requires one hop information communication and maybe applied without generating a too high additional over-head. In this proposal, the collision probability is a functionon the packet size only. RABE [11] (Retransmission-basedAvailable Bandwidth Estimation) also considers, in its es-timation, the bandwidth wasted by extra waiting time andmedium occupancy due to retransmission. This estimationrequires to compute the collision probability and the meannumber of retransmission attempts. IAB [16] takes into ac-count the common medium occupation periods between thetwo end nodes of each link and the independent occupationsperiods. They are computed thanks to the sensing busy stateduring which one end node senses the medium busy whileits neighbor senses the medium idle. This computation as-sumes a uniform distribution of nodes in the network. Mostof the previous proposals are dedicated to general multihopwireless networks and are not optimized to specific applica-tions of MANETs, like, for instance, VANETs. In [13], weshow that ABE can be of interest for VANETs and can im-prove the routing performance, but only in case of moderatemobility.

We end this section with a short description of GPSR andGBSR-B as we use them in order to evaluate our new solu-tion ABE+. GPSR forwards packets to the node which isclosest to destination following a hop-by-hop scheme. GPSRuses two different techniques to forward packets: greedy for-

warding, which is used by default, and perimeter forward-ing, which is used whenever greedy forwarding cannot beused. Since nodes require knowing their neighbors’ posi-tions, each node periodically transmits a beacon containingits own identifier (e.g. IP address) and position. An impor-tant drawback of GPSR is the implementation of perime-ter forwarding, because it is not clear when the algorithmswitches its mode to greedy forwarding again. In addition,mobility can induce routing loops while using the perimetermode [8]. To avoid these problems caused by perimeter for-warding, GBSR-B stores packets in a local buffer of the cur-rent relaying node when there is no neighbor that satisfies allthe requirements needed to be a next forwarding node. If atleast one of the two conditions (i.e., being actually a reach-able neighbor and being closer than the current carrier nodeto destination) required to be the next forwarding hop isnot satisfied, then packets are stored according to the FIFOscheme in a local buffer instead of being discarded. If thebuffer gets full, packets will be dropped. The node periodi-cally looks for a neighbor that can satisfy the requirementsto be the next forwarding node. Every period of time (1s) the node looks for a new node candidate. This periodof time is frequent enough to quickly detect any topologychange. If a candidate fulfills the requirements, the storedpackets are forwarded to that node.

3. ABE: AVAILABLE BANDWIDTH ESTI-MATION

We consider the solution ABE [12] as it is one of the firstsolutions to propose an available bandwidth estimation fea-tured for MANETs based on IEEE 802.11. In this section,we remind the main principles of ABE.

3.1 Synchronization of idle periodsIn ABE, each node estimates its idle time period by sens-

ing the medium. The available bandwidth estimation of awireless link in ABE uses the idle time periods of the emitterand the receiver of the link. However, for a communicationto take place, emitter and receiver must be both idle. Asthere is no reason that emitters and receivers are always idleat the same time, ABE includes, in its estimation, the prob-ability that two end nodes of a link be both idle at the sametime. To this end, a uniform random distribution of themedium occupancy over an observation period is assumed.The basic analytical expression to estimate the available ex-pected bandwidth E(b(s,r)) in the wireless link formed bynodes s and r considering the overlapped synchronizationperiods is:

E(b(s,r)) = Ts · Tr · C (1)

where Ts is the idle time period at the sender side, Tr is theidle time period at the receiver side and C is the maximummedium capacity.

3.2 Collision and backoff mechanismAs the estimation of the idle periods synchronization is

only probabilistic, collisions can still arise. This happenswhen a packet is emitted while the medium is not idle atthe receiver’s side. Such a collision triggers the binary ex-ponential backoff mechanism of the 802.11 DCF [1]. Colli-sions and a backoff increase impact the available bandwidth.ABE computes the collision probability from hello messages

often used in routing protocols and periodically exchangedbetween neighbor nodes. Then, the packet collision prob-ability expected when sending, on the link, packets of mbits, pm, is derived from the collision probability of HelloMessages, phello computed on line, in the following way:

pm = f(m) · phello (2)

where f(m) is a Lagrange interpolating polynomial ob-tained, off line, from simulations. The additional overheadintroduced by the binary exponential backoff mechanism iscomputed as:

K =DIFS + backoff

Tm(3)

where Tm (in sec.) is the time separating the emission oftwo consecutive frames, DIFS is a fixed interval and backoffis the mean backoff used to transmit a single frame.

Finally, by merging the different mechanisms that impactthe available bandwidth, ABE estimates the available band-width on a wireless link as follows:

ABE(s,r) = (1 −K) · (1 − pm) · Ts · Tr · C (4)

4. ADAPTATION OF ABE TO VANETS

4.1 Collision probability of ABEIn ABE, the function f(m) is used to estimate the packet

collision probability if a flow with packets of size m wouldbe transmitted on the wireless link on which the estimationis processed (Equation 2). f(m) is obtained by computingthe Lagrange interpolating polynomial from simulation re-sults computed off line. The simulated scenario is a fixedscenario of 4 nodes with 2 hidden sources and 2 destinations(asymmetrical hidden nodes). The collision probability isestimated on one link by sending packets of a given size onthis link while varying the workload of the other link, hiddento the source of the first link. This estimation is processedfor different packet sizes. The interpolation considers pairsof values (packet losses for a given packet size m (pm) andlosses of hello messages (phello)) for a given workload onthe hidden link. The f(m) function used in [12] is given inEquation 5:

f(m) = −5.65·10−9·m3+11.27·10−6·m2−5.58·10−3·m+2.19(5)

This fixed and simple scenario is more likely to be foundin MANETs. Moreover, in [12], the authors only considerpacket losses due to collisions. In VANET scenarios, vehiclesfollow streets in cities with buildings and respect traffic signsand many other aspects should also be considered. There-fore, we propose to consider packet losses in a more generalextent. Indeed, packet losses may come from multiple rea-sons. First, some packets can be lost due to the forwardingalgorithm of the routing protocol. That is, when the currentforwarding node does not find a proper next hop to forwardthe packet, it may drop the packet or store it temporaryin the local buffer, depending on the particular algorithm.Packets stored during a too long time might be discardedat destination due to late arrival. This kind of loss maybe important in VANETs due to high mobility. Second, we

Table 1: Simulation settings in urban scenarios.Parameter ValueArea 1500 m x 1500 mNumber of vehicles 40 to 180Maximum vehicle speed 30, 50, 70, 90 km/hTransmission range 250 mSensing range 300 mMobility model ManhattanMobility generator CitymobMAC specification IEEE 802.11bPhysical rate 11 MbpsTraffic rate CBR, 4 KbpsSimulation time 1000 sPacket size (m) 1000, 750 and 500 bytesRouting protocol GPSR and GBSR-B

consider urban environment, therefore buildings in the citymay affect packet losses. Finally, packets may collide whenmore than one vehicle in the transmission range attempts totransmit a packet at the same time. For all these reasons,we decide to consider these three kinds of packet losses, in-stead of considering only packet collisions in order to derivea new f function. To this end, we start with an analysis ofdata obtained from simulations in VANET urban scenarios.

4.2 Data analysis from VANETs scenariosTo obtain information about the probabilities of packet

losses, in general, in vehicular urban scenarios and to de-velop our new proposal, we carry out several simulations toanalyse the performance of a vehicular environment. We usethe open source network simulator NCTUns 6.0 [15] wherewe implemented GPSR. The simulation city area is 1500mx 1500m, each street has a length of 100 m and intersectionsof 30 m. We also use the presence of buildings that attenu-ate the transmission signal, in order to give more realism tothe simulations. The mobility model is Manhattan, whichis generated by Citymob [10]. In each scenario, 50% of thenodes are sending data to one fixed destination during 1000sec. using the GPSR routing protocol. This destinationis common for all the sources. The traffic profile of eachsource is a CBR at 4 Kbps. Notice that this rate can of-fer low quality video services, which are suited for warningservices (e.g. send a video clip of an accident). The mainsimulation settings of each scenario are shown in Table 1

We perform several simulations by varying the packet sizem=1000, 750 and 500 bytes. Likewise, we vary the densityof the scenario by using a number of vehicles N between 40and 180 nodes in each simulation. Finally, we use an averagespeed of the vehicles s between 30 km/h and 90 km/h, whichare the common speeds in cities.

First, we analyze the results on packet losses from simula-tions using the previous combination of variables (N,m, s).The results are depicted in Fig. 1. Fig. 1(a) shows thepercentage of packet losses in an urban scenario where thepacket size is m=1000 bytes. We can see that the percentageof packet losses decreases as the number of nodes increases.This can be explained by the fact that when the numberof neighbors increases, each node has more candidates tochoose a better next hop to forward the packets. But whenthe number of neighbors is low, each forwarding node has

(a) Scenario with packet size of m=1000 bytes.

(b) Scenario with packet size of m=750 bytes.

(c) Scenario with packet size of m=500 bytes.

Figure 1: Percentage of packet losses from simula-tions with different packet sizes in urban scenarios.

more difficulties to find a proper next hop to forward thepackets that are consequently discarded.

We can also see that, with few nodes, the percentage ofpacket losses decreases slightly when the speed increases(i.e., 40 node at 90 km/h). Indeed, for a low number ofnodes, at higher speeds the current forwarding node canfind new vehicles more often to choose a next forwardingnode or can get destination quickly to deliver the packet.On the other hand, we can notice that when the numberof nodes increases, the speed has less effect on the packetlosses. The reason is that there already are enough nodesto make a good choice of the next forwarding node inde-pendently of the nodes’ speed. However, for much higherspeeds we can already see that for more than 160 nodes,packet losses increase with speeds higher than 70 km/h. InFigs. 1(b) and 1(c) the same analysis can be done, but withpacket sizes of 750 and 500 bytes respectively.

From these results, we can conclude that the metric thathas more impact on packet losses in these scenarios, is thedensity of nodes. Even though the nodes’ speed also im-pacts packet losses, this impact is less important. In thesescenarios, the packet size has not a noticeable effect on theresults. This can be explained by the application rate (i.e.,4 Kbps) that we use and that generates moderate loads withwhich the packet size is almost impactless.

Taking into account the three variables of interest (num-ber of nodes N , speed of the nodes s and packet size m)for our scenarios, and having observed the effect of each pa-rameter in the packet collisions, the next step is to find anexpression f(m,N, s), that considers packet losses in generaland that fits better to vehicular scenarios, to substitute theformer f(m) of Equation (5) of ABE, that considers packetcollisions only and that is deduced from a simple MANETscenario.

4.3 Statistical analysisOur goal is to obtain a function f(m,N, s) to be included

in the equation of ABE to assist the routing protocol to takethe best forwarding decision that minimizes packet losses.From the analysis of the previous section, we do not onlyconsider the packet size (m) as a variable, but we also takeinto consideration the nodes’ density (N) and the averagespeed of nodes (s).

To this end, in addition to the packet losses results previ-sously obtained by simulation, denoted p hereafter, we alsocompute the collision probability suffered by hello messagesphello, by measuring the number of packets that should havearrived compared to the ones that actually arrived in a pe-riod of time. Note that phello is a value that also dependson N , m and s. We then make a multiple linear regres-sion [5] [3] with the observed values and obtained from sim-ulations p/phello, by varying the three variables N , s and min a wide range of the values of interest. The model of mul-tiple linear regression is given by Y = α+ β1X1 + β2X2...+βnXn + ε, where X’s are the variables and β are the param-eters to estimate and measure the influence of the variables.The β parameters are also called regression coefficients. Theα is the intercept (or constant). Finally, ε is called the er-ror term, which captures all other factors which influencethe dependent variable Y (assumed Eεi = 0). The softwareIBM SPSS 201 [2] is used.

1IBM SPSS Statistics provides univariate and multivariatemodeling techniques to help users reach the most accurate

Table 2: Model summaryb.R R2 Adjusted R2 Std. error of the

estimation0.811a 0.658 0,646 0.3045352

a. Variables: Constant, number of nodes (N), speed (s),packet size (m).

b. Observed values: p/phello.

The summary of the results are shown in Table 2. Thevalues in Table 2 are defined as follows:

• R: Correlation coefficient: It shows the grade of re-lation between the observed values (p/phello) and thevariables (N, s,m). It is a value between 0 and 1. Lowvalues indicate weak or none relation between the ob-served values and the variables.

• R2: Determination coefficient: Measure of goodnessof fit of the model of multiple linear regression. It isthe proportion of the variation of the observed valuesexplained by the regression model. R2 takes valuesbetween 0 and 1. A small value indicates that themodel does not fit the data well.

• Adjusted R2: It is an optimistic estimation of the fit ofthe model to the measures.

• Std. error of the estimation: Standard deviation ofthe observed values.

From the second column of Table 2 that gives the fit good-ness of a linear model, we can conclude that our three vari-ables (nodes’ density N , speed s and packet size m) explainwell 65.8% of the probability of packet losses (R2=0.658).

An analysis of variance of the regression (ANOVA) ismade to determine if the model has statistical validity. Theresults are summarized in Table 3, which includes sum ofsquares (the sum of the squared deviations of each valuewith respect to the mean; this value measures the maxi-mum dispersion of the data), df (freedom degree of the sam-ple size, which is n=96 in our case), mean squares (sum ofsquares/df) and F (ratio of two mean squares; this valuehelps to measure the variability of the data).

The results in Table 3 confirm that our model of multiplelinear regression for p/phello has statistical validity, sincethe significance (Sig.) is lower than 0.05. A low value ofsignificance (Sig.) indicates that the values are not random.This way, we can conclude that the model fits well with thesimulation results using the parameters N , s and m.

The values of this regression (α and β parameters) aresummarized in Table 4. Table 4 includes the values to beimplemented in the model (column called B, in red). We seethe value of the constant is 1,9846 (that is α in the model)and then the values of the β’s (-0.00007475, -0.008983 and-0.001428) respectively for each X’s (i.e. m, N and s).

Also, in Table 4 we can see the value of significance (Sig.,last column) of each value to be used (constant α, m, N ,s). We can confirm that the constant α and the number

conclusions when working with data describing complex re-lationships. These sophisticated analytical techniques arefrequently applied to gain deeper insights from data used indisciplines such as medical research, manufacturing, phar-maceutical and market research.

(a) Scenario with packet size of m=1000 bytes.

(b) Scenario with packet size of m=750 bytes.

(c) Scenario with packet size of m=500 bytes.

Figure 2: Percentage of packet losses using the newfunction p(m,N, s) = f(m,N, s) ·phello(m,N, s) with dif-ferent packet sizes m.

of nodes N (number of vehicles in the scenario) have thehigher influence, because both have obtained a significancelower than 0.05 (see last column of Table 4). Nevertheless,we still consider m and s in our expression for f(m,N, s) incase they have a higher impact for other application rates(specially m) or other scenarios.

4.4 Adapting ABE to VANETs: ABE+After the analysis we obtain the final expression for f(m,N, s),

shown in Equation (6).

f(m,N, s) = −7.47 · 10−5 ·m− 8.98 · 10−3 ·N−1.42 · 10−3 · s+ 1, 98 (6)

The function f(m,N, s), which considers packet losses,substitutes the former function f(m) in Equation (2) ob-tained in the original ABE [12] that relates the packet colli-sion probability (pm) with the collision probability of hellomessages (phello). In our study, the packet losses probability,p, denoted p(m,N, s) hereafter, becomes:

p(m,N, s) = f(m,N, s) · phello(m,N, s) (7)

In Fig. 2 we depict the packet losses obtained with ournew model (Equation (7)) for packets with m=1000, 750and 500 bytes, respectively.

Finally, we compare the percentage of packet losses us-ing our expression f(m,N, s) (Equation (7)), the percent-age of packet collisions using the former expression f(m)(Equation (5)) and the percentage of packet losses obtainedfrom simulations. Fig. 3 shows these results for different val-ues with the average speed s=50 km/h and three differentpackets sizes m. It clearly shows that our new expressionf(m,N, s) gives results closer to simulation results than theexpression f(m). We can conclude that f(m,N, s) gives agood approximation on the packet losses in urban scenariosof vehicular networks.

Thus, the new solution ABE+ is:

ABE+(s,r) = (1 −K) · (1 − p(m,N, s)) · Ts · Tr · C (8)

5. SIMULATION RESULTSWe include an available bandwidth estimation into GBSR-

B to the take forwarding decisions. The original GPSR takesthe forwarding decisions based on the distance, selecting asnext forwarding node, the nearest node to destination amongthe neighbors. We adapt GBSR-B to take the forward-ing decision considering the node with the highest availablebandwidth computed with ABE and ABE+. This meansthat each node selects as next hop the node which offers thehighest bandwidth among all the neighbors in transmissionrange, instead of considering as next hop the closest nodeto destination. We analyze the performance of the originalGPSR [6] compared to GBSR-B, GBSR-BABE (f(m)) andGBSR-BABE+ (f(m,N, s)).

We use a manhattan scenario to model a common urbanscenario formed by streets and crossroads. In order to sim-ulate a realistic scenario, we use Citymob [10] to generatethe movements of vehicles that follow streets and respect thepresence of other vehicles and traffic lights. The simulationarea is 1500 m x 1500 m. Each street is 100 m long withintersections of 40 m according to the area of the Eixample

(a) Scenario with packet size of m=1000 bytes.

(b) Scenario with packet size of m=750 bytes.

(c) Scenario with packet size of m=500 bytes.

Figure 3: Percentage of packet losses using func-tion f(m,N, s), probability of packet collisions us-ing function f(m), compared to simulation results.s = 50km/h

(a) Average packet losses. (b) Average packet delay.

Figure 4: Performance comparison of GPSR, GPSR-ABE and GPSR-ABE+.

Table 3: Analysis of variance ANOVAa.Sum of squares df Mean square F Sig.

Regression 16.391 3 5.464 58.912 .000b

Residual 8.532 92 .093Total 24.923 95

a. Observed values: p/phello.b. Variables: Constant, speed (s), number of nodes (N), packet size (m).

Table 4: Coefficientsa.Unstandardized Standardized

coefficients Std. coefficientsB error Beta t Sig.

Constant 1,9846 .163 12.185 .000m -0,00007475 .000 -0.030 -,491 .625N -0,008983 .001 -.808 13,245 .000s -0,001428 .001 .063 -1,028 .307

a. Observed values: p/phello.

in Barcelona. The maximum average speed of the vehicles is50 km/h. There is one fixed destination which is an accesspoint (AP). Half of the nodes sent 1000-bytes packets each2 seconds to the unique destination during 1000 sec. Thereis an interferent traffic of 800 kbps, from a random node tothe fixed access point. The transmission range of the nodesis 250 m and the sensing range is 300 m, either for vehiclesand for the destination node. Table 1 summarizes the mainsimulation settings, but, in these simulations, the vehicledensity is only 60 vehicles which are randomly positionedand m is only 1000 bytes. Simulation results are performedusing the NCTUns 6.0 simulator [15].

Fig. 4(a) shows the average packet losses of the four eval-uated protocols. We can see that GBSR-BABE+ obtainsthe best performance in terms of packet losses, obtainingthe lower value compared to the other solutions. GBSR-BABE is the second best protocol, but our improvement ofthe function f(m) produces better results as we can see withthe results of GBSR-BABE+. GPSR obtains higher packetlosses as it only considers the nearest node to destinationand due to the lack of a local buffer as in GBSR-B. Finally,in Fig. 4(b), we can see that GPSR obtains the lower end-to-end delay. But this good result is at the price of a high

number of packet losses. On the other hand, GBSR-BABE+

and GBSR-BABE decrease both end-to-end delay and packetlosses, with better performance for GBSR-BABE+.

6. CONCLUSIONIn this paper, we derive a new function f(m,N, s) to esti-

mate the available bandwidth in VANETs. This new func-tion enables to estimate the probability of packet losses andis based on the packet size m, the number of vehicles N inthe scenario and the average speed of the vehicles s. Theaim was to improve the original f(m) used in ABE that onlyconsiders the probability of collisions and is only based onthe packet size.

We present simulations that evaluate the performance ofthe routing protocol GBSR-B using ABE and ABE+. Theresults show that, for the tested scenarios, GBSR-BABE+

achieves a good trade-off in terms of packet losses and packetdelay compared to the original GPSR and GBSR-B as GBSR-BABE . We can conclude that integrating an accurate avail-able bandwidth estimation in routing protocols is of interestfor VANETs.

7. ACKNOWLEDGMENTSThis work was partly supported by the Spanish Govern-

ment through TEC2010-20572-C02-02“Consequence”project.Carolina Tripp and Ahmad Mezher are the recipient of aFI-AGAUR grant of the “Comissionat per a Universitats iRecerca del DIUE” from the Generalitat de Catalunya andthe Social European Budget. Carolina is also granted by theAutonomous University of Sinaloa (Mexico). Luis Urquizais the recipient of a grant from Secretaria Nacional de Edu-cacion Superior, Ciencia y Tecnologıa SENESCYT and theEscuela Politecnica Nacional (Ecuador).

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