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VANET Inherent Capacity forOffloading Wireless Cellular Infrastructure:

An Analytical StudyGhayet el mouna ZHIOUA†∗, Houda LABIOD∗, Nabil TABBANE† and Sami TABBANE∗

† InfRes - Telecom ParisTech - France∗ MEDIATRON - Higher School of Communication of Tunis (Sup’Com) - Tunisia

Email: {zhioua, houda.labiod}@telecom-paristech.fr, {nabil.tabbane, sami.tabbane}@supcom.rnu.tn

Abstract—Due to the high user traffic demand increase, of-floading cellular traffic through other kinds of networks, such asWi-Fi hotspots and femtoCells has been highly studied. In thispaper, we investigate upon the possibility to use Vehicular ad hocNetworks (VANETs) for the offloading the cellular infrastructure.We present an analytical study based on an optimization problemformulation where the aim is to select a maximum target set offlows that could be routed to the destination downloader nodethrough the VANET network. The offloading decision considersthe vehicular link availability, channel load by consideringmedium contention, vehicle to vehicle link quality and the linkconnectivity duration between the VANET node and the road sideunit. Simulation results of our analytical approach show that thedata offloading fraction is closely affected by the data volume ofthe flows, the channel load and by the link quality. Results showalso that the offload of best effort data traffics could reach 100%for low data volumes.

Keywords- Offloading, VANET, Analytic analysis, Cellular Infrastructure

I. INTRODUCTION

Nowadays, mobile devices such as tablets and smartphonesare booming with a significant increasing success. The averagesmartphone usage has grown of 81% in 2012 [1] and mobilePCs and tablets subscriptions are expected to grow from 250million in 2012 to around 850 million in 2018, exceeding thenumber of fixed broadband subscriptions [2]. The growingnumber of these mobile devices goes with the increase ofcellular data traffic demand as applications must be directlypumped and connected to Internet. The blooming is such, thatmobile data traffic demand is expected to be continuously ris-ing with a rate of more than 100% per year. As a matter of fact,operators need to cope with the data demand volume rice andensure sufficient base stations capacities to meet subscribersgrowing requirements. One solution to overcome these issuesis the cellular traffic offloading. Proposed offloading solutionsinclude femtoCells for indoor offloading, Wi-Fi for outdooroffloading and opportunistic offloading. According to Cisco[1], in 2012, globally, 33% of total mobile data traffic wasoffloaded onto the fixed network through Wi-Fi or femtoCell.Numerous works have studied the system of offloading thecellular infrastructure while using the Wi-Fi hotspots andfemtoCells. Authors in [3] present a quantitative study ondelayed and on-the-spot offloading by using Wi-Fi. A gain ofabout 65% is presented for offloading of the traffic of every-day smart-phone users while considering mobility and traffic

patterns. Reference [4] proposes a cost function approach thatevaluates the suitability of a traffic flow to be routed through anIEEE 802.11 hotspot. The cost function considers the occupiedairtime on the channel by evaluating the amount of time thatthe wireless medium has been occupied while transmitting allthe flows packets and the efficiency of resource utilization.

However, only very few works [5], [6] have consideredusing the intelligent transportation system (ITS) for the samepurpose. In fact, mobile ad hoc network, i.e. vehicular network(VANET) networks are widespread. Vehicles are the thirdplace, after homes and offices, where citizens spend more timedaily. Moreover, vehicles become intelligent and connected asthey will be equipped with On Board Units (OBUs) whichare devices that provide Vehicle to Vehicle (V2V) and Vehicleto Infrastructure (V2I), called also road side units (RSU)communications. A dedicated frequency band 5.86–5.92 GHzhas been allocated for IEEE 802.11p vehicular communi-cations. Therefore, considering ITS for offloading cellularinfrastructure represents an attractive solution. In reference[5], authors present an analytical study of content offloadingthrough ITS. The problem formulation is based on maximizinga cost function by considering constraints related to channelaccess and flows conservation. However, the work does notconsider inter-RSUs roaming/handover and the quality of thelink between the infrastructure and the VANET network.

In this paper, we propose a cooperative traffic transmissionalgorithm in a joint 4G LTE Advanced cellular network and aVANET network where VANET nodes will cooperate with theLTE infrastructure by routing a portion of the cellular traffic. Inthis work, we provide an analytical study by which we quantifyand evaluate how much can the VANET network offloadthe cellular infrastructure while considering the constraintsrelated to the mobile nodes connectivity and the infrastructurefeatures. The originality of this work as compared to reference[5] is the consideration of the impact of the load and the datavolume on channel contention for the I2V link. Moreover weconsider variable flows volume and prioritize the offload offlows with high I2V link connectivity duration.

The remainder of this paper is structured as follows. We firstdescribe our system model. In the third Section, we presentour analytical model. Finally, performances evaluation andconclusions are presented in the Sections IV and V.

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II. SYSTEM MODELING

The system model is based on a hybrid network architec-ture composed of two systems: an LTE infrastructure and aVANET network. The cellular infrastructure is composed ofeNodeBs connected to the LTE Evolved Packet Core (EPC)[7]. The VANET topology is composed of IEEE 802.11p basedvehicular nodes moving over a road. Vehicles have got twointerfaces related to the I2V and V2V communications. Theyuse the IEEE 802.11p based VANET interface to communicatewith neighboring vehicles and the remaining interface tocommunicate with the infrastructure. We assume that I2V andV2V communications occur on different frequency channels.Fixed RSUs are deployed over the road. We propose to calleach vehicular user that wishes to download content fromthe cellular network, e.g., Internet, a “downloader vehicle”.More concretely, the downloader vehicle receives queries froman Application Program Unit (APU) which are requests fordownloading content from the cellular network. The APUcould be an uploaded application in the smartphone of the usertraveling in the vehicle, it could be also installed in the vehicleitself as a service proposed by the automotive manufacturer.APU-OBU communications are out of the scope of this work.

Downloading content from the infrastructure could be per-formed through multiple ways (c.f. Fig. 1): 1) It could be adirect I2V transfer from the RSUs, i.e. RSU-downloader I2Vlink. 2) Downloading the content could to be also assistedby other vehicular nodes that relay it to the destination. Insuch situation, a gateway is elected for interfacing betweenthe RSU and VANET. The elected gateway receives contentfrom the RSU and sends it through multihop V2V links to thedownloader vehicle, i.e. gateway V2V link. 3) Otherwise, thedownloader uses the direct link to the cellular infrastructureto download its content from the eNodeB. A vehicular nodewishing to download a content from the eNodeB starts bygenerating a request to the cellular network. The eNodeBforwards the request to the RSUs in the area where thedownloader is traveling. RSUs are then in charge of making thedecision of even delivering the data to the target downloaderthrough RSU-downloader I2V link, or to a relay vehicledeemed to meet later the downloader (through V2V hops)or to reject the request and the downloader vehicle will havethe content directly from the cellular network. In the two firstcases, the cellular infrastructure will be offloaded through theVANET network as content will be downloaded directly fromthe RSU.

The Region of Interest (ROI) is the geographical regionof an RSU within which vehicles are moving and send andreceive information from this RSU. Each vehicle can beattached to only one RSU. We assume that, RSU is aware ofthe downloaders in its ROI and vehicles send periodically theiridentity and velocity/position to the RSU using the floatingcar data (FCD) transmissions [8]. As our system model isbased on considering vehicles moving in a multiple lane roadwithout intersections, the RSU can determine the I2V andV2V connectivity in its ROI based on FCD information. Thevehicles connectivity graph is periodically updated as FCD arereceived using a graph connectivity manager (GCM) unit. The

LTE Advanced

EPC

S1

RSU

VA

NET

Ne

two

rk

ROI

D2

D1, D2, D3 : Downloaders

G : Gateway

D1

G

I2V link Gateway V2V link Direct link

D3

Infr

astr

uct

ure

Figure 1. System Model

GCM module is installed in each RSU and its role is to builtand update the connectivity graph of vehicles belonging to itsROI using location information. The graph is composed ofvertices and edges where a vertex represents one vehicularnode. The edge is between two vertices and has a weightrelated to its V2V link quality, called hereafter Pc, formulas (4)and (8). Pc is computed using the V2V distance. We notice thatthe memory capacity at RSUs and vehicles is not consideredto be an issue thanks to their good storage capabilities.

III. OPTIMIZATION PROBLEM FORMULATION

The goal is to define a solution which determines data flowsthat could be retrieved via the VANET network through eithera direct I2V link or via multihop V2V links to the downloadervehicles. Thus, we formulate a multi-constraints optimizationproblem to quantify the maximum data content that could bedownloaded through the ITS where each RSU takes decisionthat maximizes the fraction data flow have to be prefetched.

A. Max-Flow Formulation

We first define symbols and assumptions used in our study:R is the IEEE 802.11p wireless transmission range, RI is thecoverage of the RSU within which vehicles receives down-loaded content in the ROI, MI , respectively Mv , is the numberof contenting nodes in the I2V and V2I channel, respectivelythe V2V channel, at the reference time t. Moreover, we assumethat vehicles follow a Poisson arrival process with exponentλ. We call data flow, the basic unit to offload. Let be t thereference time at which a snapshot of the network is done.Given a snapshot of vehicles’ position, trajectory and velocity,the RSU maximizes the following objective function:

max∑i

∑j

ϕi,j (1)

where ϕi,j is the flow j of the downloader vehicle i. The MaxFlow problem formulation is solved by considering constraintsrelated to the I2V and V2V paths availability and I2V contentdownloading duration, as detailed below.

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B. Fairness Constraints

1) Path Availability: This constraint aims to evaluate thelink between the RSU and the downloader vehicle vi. It is theprobability that a path exists between the vehicle i and theRSU which could be a single-hop or a multihop path:

PAi ≥ PAth � ϕi,j , ∀ j ∈ Fi (2)

where Fi is the set of the flows of vehicle i and PAth > 0.An estimation of a suitable PAth will be handled SectionIV.The PAi between the vehicle i and the RSU is:

PAi = LAIi + LAV 2Vi (3)

The link availability LAIi parameter is computed if a single-hop exists between the RSU and vi, otherwise if a multihoppath is used to connect vi to the RSU, LAV 2V

i is computed.

LAIi = Pr(dI2vi ≤ RI) ∗ Pci ∗ PMAI(4)

where, Pci is the link quality probability between the RSU,and the vehicle vi. Pci is related to the distance be-tween the RSU and vi and to the received signal strength.Pr(dI2vi ≤ RI) is the probability that vi is positioned underthe radio transmitting range of the RSU. As vehicles follow aPoisson arrival process with parameter λ, it is expressed as:

Pr(dI2vi ≤ RI) = 1− e−λRI (5)

We consider IEEE 802.11p-based MAC scheme that usescontention for medium access where the contention windowsize increases if the node fails to transmit, with a probabilityPb. If the channel is idle, with a probability P̄b, the vehicleaccesses the medium and the packet is sent with a probabilityPcoll to collide with another data packet. Thus, the probabilitythat the vehicle expresses a collision free medium access is:

PMAI= 1− Pcoll =

(CW − 1

CW

)MI−1

(6)

where CW is the contention window size. As we consider onlydata traffic, the CW value is specified by the IEEE 802.11pstandard [9] for one SCH channel.

In the case where vehicle vi is far from the RSU and no I2Vcommunication is possible, we examine the link availabilitybetween vi and the RSU by electing a gateway vp that willhandle the interfacing between the V2V links and the RSU.The vi to RSU connectivity could be performed by meaningof two hops (RSU-vpand vp-vi) or multiple V2V hops. TheV2V link availability is then expressed as:

LAV 2Vi = LAIp ∗

p−1∏k=i

LAV 2V (k, k + 1) (7)

where LAV 2V (i, j) is the link availability between two vehi-cles i and j in the ROI. It is expressed as:

LAV 2V (k, k+ 1) = Pr(dvk2vk+1≤ R) ∗Pc(k, k+ 1) ∗PMAv

(8)

where k and k+1 are two neighboring vehicles composing theRSU to vi path and Pc is the link quality probability betweenvk and vk+1 and Pr(dI2vi ≤ R) is expressed as:

Pr(dI2vi ≤ R) = 1− e−λR (9)

and

PMAv=

(CW − 1

CW

)Mv−1

(10)

where Mv is the number of contenting nodes in the V2Vchannel.

2) Channel occupation duration : Content downloadingduration of flow ϕi,j have to be lower than the sojourn timein the ROI of vi or its gateway (i.e.vp). Channel occupationduration constraint is expressed as:

τ(ϕi,j) < σ {LCDI2i ‖ LCDI2p} (11)

where

τ(ϕi,j) =∑tr

tk(ϕi,j) (12)

τ(ϕi,j) represents the amount of time that the wireless mediumhas been occupied for the transmission of the flow ϕi,j . tr isthe number of trials which represents the transmission attemptsthat have been required to access to the medium to send theconsidered flow. The σ parameter, 0 ≤ σ ≤ 1, is relatedto the fraction of content of the flow ϕi,j that we aim tooffload through the ITS. Thus, for σ = 1, all ϕi,j contentwill be considered for potential downloading via the VANETnetwork. For σ 6= 1, only a fraction of the content will bedownloaded via the VANET network, the remaining contentwill be handled by the cellular infrastructure. In this work, wemake assumption that σ = 1.

tk(ϕi,j) =

K∑k=1

tbk + tIFS + td + tack (13)

where, the duration tk includes the whole transmission du-ration considering the backoff waiting time to attempt toaccess to the medium (tbk), the inter-frame spaces (tIFS),td which is the duration of the complete data frame andthe acknowledgment (tack). K is the number of packetscomposing the data flow. The LCDI2i parameter representsthe link connectivity duration between the RSU and the vehiclevi:

LCDI2i =

√(α2 + γ2)R2

I − (αδ − βγ)2 − (αβ + γδ)

α2 + γ2(14)

where, α = vicosθi, β = xi − xj , γ = visinθi, δ = yi − yj .(xi, yi) is the Cartesian coordinates of vehicle i and (xj , yj)is the Cartesian coordinates of the RSU. Vehicle i has aninclination of θi, (0<θi,<2Π) with respect to the x-axis andmoving at vi speed.

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Parameters ValuesFlows Access Category AC_BE

AIFSN 6SIFS 2Ts

Packet Size 500 bytesContent downloading volume 10Mb

SCH Channel Bandwidth 10 MHzData Rate 3 Mb/s

Time Slot (Ts) duration 16 µsCWmin/CWmax 15 / 1023

RI / R 1000 / 300 m

Table ISIMULATION PARAMETERS SETTINGS

IV. PERFORMANCES EVALUATION

Given the aim of investigating the effect of vehicular nodesdensity, channel access contention and VANET network trafficoffload potential, we have conducted a number of experimentsusing a single contention channel IEEE 802.11p. We based ourperformances evaluation on using variable topology scenarioswhere each scenario is characterized by its number of nodesand maximum number of hops to the infrastructure. In thesescenarios, we varied the number of vehicular nodes ([1 - 40]nodes) and the number of hops from the downloader vehicleto the RSU ([1 - 5] hops) to evaluate the impact of vehicularnodes density and the number of hops on the Max-Flowproblem. Moreover, it is clear that the overhead induced byVANET routing protocol are not an issue because we use adedicated SCH channel for data transmission.

Network performances simulation are performed using theNetwork Simulator NS2.33 [10]. We implement an IEEE802.11p package in order to enable VANET communicationsamong high-speed vehicles. Offloading problem resolution hasbeen carried out using the Matlab optimization tool box.The table I presents the simulation parameters. We madeassumption that all the flows under consideration of offloadingthrough the VANET network belong to the best effort accesscategory. Remaining traffic of the base station are sent throughthe cellular network. We suppose that MI = Mv = M .

The offloading technique is broken down into two ways:direct content download from the RSUs and indirect contentdownload, i.e. received through V2V multihop links. In Fig. 2-(a), we plot the direct and V2V link availability while varyingthe number of mobile vehicles contenting to access to themedium for different values of λ and with Pci = 1, ∀ i.For both situations, the link availability decreases considerablywhile the number of nodes increases. For M ≥ 20, the linkavailability becomes very low (LA ≤ 0.1) specially when λ =0.0001. Based on Fig. 2-(a), we can estimate the range of bestvalues of PAth. In fact, for high PAth, the link availabilityis possible only for a low number of contenting nodes (≤ 5),which is not a realistic scenario. For PAth ≤ 0.1, the directand V2V link availability is possible even for a very low λ andwith contenting nodes that could reach 40 vehicles. In Fig. 2-(b), we plot the V2V link availability parameter while varyingthe number of hops used for downloading the content from theRSU. The LA is constant if there is only one contenting node.It decreases considerably when the number of hops increases.

0

0,2

0,4

0,6

0,8

1

1,2

0 20 40

Lin

k A

vaila

bili

ty

M

λ=0,001, I2V λ=0,001, V2V

λ=0,01, I2V λ=0,01, V2V

0

0,2

0,4

0,6

0,8

1

1,2

0 2 4 6 8 10 12

Lin

k A

vaila

bili

ty

Nb Hops

M=1 M=3 M=7 M=12 M=20

(a) (b)

Figure 2. Link Availability features

0

0,2

0,4

0,6

0,8

1

1,2

0 0,02 0,04

Lin

k A

vaila

bili

ty

λ

I2V LA (M=1)

I2V LA (M=10)

V2V LA (M=1)

V2V LA (M=10)

Figure 3. Impact of vehicles density

To have a LA higher than 0.1, the number of hops must beat most 3 for 12 contenting nodes and 5 for 7 contentingnodes. According to Fig. 3, for λ ≤ 0.01, the link availabilityconstraint presents an exponential increase and it convergesfor λ ≥ 0.01. This represents an interesting feature of the LAconstraint. In fact, if λ exceeds a threshold of 0.01, it is certainthat a link availability exists and it doesn’t depend no more onthe number of vehicles in the ROI. The LA is indeed stable.

In Fig. 4, we plot the traffic flows offloading fractionthat could be reached while varying the ratio of incomingvehicles in the ROI, for a PAth = 0.1 and a PAth = 0.5.According to this figure, the percentage of offloading directlink flows is higher than offloading traffics that needs V2Vcommunications to be routed to the destination node. This isdue to the fact that direct link flows experience better LAthan V2V link flows, c.f. Fig.s 2-(a) and (b). Thus, it wouldbe better to prioritize the direct link to the V2V link to routetraffic from the RSU to the vehicle if the latter experiencea good link to the RSU. Moreover, as the PAth increases,the offloading fraction decreases. The offloading percentagecould reach 80% of the considered traffic. Thus, to ensurehigh offloading potential, PAth parameter need to be set to alow value. Finally, we notice that for λ ≥ 0.1, the offloadingfraction remains constant, i.e. the model converges. This is avery interesting feature of our proposed model. In fact, theoffloading fraction does not depend no more of the ratio ofincoming vehicles in the ROI. The probability mass functionof incoming vehicles, which represents the probability that wehave n vehicle in the ROI at a given time t, is expressed as:

P (n) = e−λt(λt)

n

n!

For λ = 0.1 and n ≤ 20 (according to results discussion of

5

0

20

40

60

80

100

0 0,1 0,2 0,3 0,4

Off

load

(%

)

λ

Direct Link PAth=0,5 V2V Link PAth=0,5Direct Link PAth=0,1 V2V Link PAth=0,1

Figure 4. Offloading Fraction Vs Lambda

0

20

40

60

80

100

120

0 5 10 15 20 25

Off

load

(%

)

Data Volume (Mb)

1 Trial

30 Trials

Figure 5. Impact of data volume on the offloading fraction

Fig. 2-(a)) and within a time interval of 200s, the probabilityto have at least n vehicles in the ROI is 0.09. Therefore,PAth is a very interesting and attractive parameter that it couldbe set by the operator to reach a certain level of offloadingfraction depending on its offloading needs, e.g. base stationload variation, and vehicular traffic density.

According to the offloading problem formulation, the of-floading potential of VANET depends on the saturation levelof the medium which is related to the number of simultaneousvehicular nodes that attempt to transmit on the channel andconsequently, the number of transmission attempts that couldbe performed by a source to access to the medium. τ(ϕ)parameter is computed based on this number of mediumattempts. To properly evaluate the impact of channel load onthe offloading potential of the VANET network, we made theassumption that the LA constraint is always valid, i.e PAi ≥PAth. This assumption is made only for this evaluation. Twosituations are considered, the first case is when there is a lowcontention of the medium (1 Trial) and the second case iswith a high contention (30 Trials). According to Fig. 5, anoffloading of 100% of data traffics is possible for low datavolumes. The maximum offloading fraction proposed in [5] is76% (see Fig. 6.a). According to the Fig. 5, as the volume ofthe data flows increases, the percentage of content offloadingdecreases. This is due to the fact that, by increasing the contentvolume of the flow, more time is needed to download it to theVANET vehicles which become inadequate as vehicles movesin the RSU and could leave the ROI before downloading alldata. Thus, due to the dynamicity of the VANET network,it is better to offload data flow which contents volume doesnot exceed 12Mb. On the other side, offloading proportion ishighly affected by the channel load. For example for a flowof 10Mb, the offloading fraction is 50% if only one trial is

02468

101214

0,2 0,365 0,6 1

Pac

ket

Loss

(%

)

Flow Volume (Mb)

1 hop

2 hops

Figure 6. Impact of data volume on the packet loss

required to transmit the flow, however, it falls down to 10%,if at least 30 trials are required to transmit this flow. Therefore,according to Fig. 5, offloading potential of the VANETs highlydepends on the data volume of the flows and on the mediumload.

In Fig. 6, we plot the packet loss average of offloadedflows for one hop and two hops traffics while varying thedata flow volume per vehicle. Simulation scenario is basedon 13 vehicular nodes moving within the ROI of an RSUpositioned in a multi-lane highway. We notice that the packetloss increases for high data volume. Moreover, the packet lossof traffic of vehicles that are at two hops from the RSU ishigher than the one at one hop. Thus, it is more appropriateto prioritize offloading one hop flows than two hops flows.

V. CONCLUSIONS

This paper presents an analytical study for evaluating the po-tential of VANET networks to offload the LTE infrastructure.Offloading decision considers the VANET link availability,channel load, V2V link quality and the I2V link connectivityduration. The proposed model was validated by simulationresults. An offloading of 100% of data traffics could bereached. The offloading fraction is highly affected by channelload and data volume. As future work, we will extend ourmodel to consider QoS constraints of various traffic types.

REFERENCES

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[4] S. Wietholter, M. Emmelmann, R. Andersson, and A. Wolisz, “Per-formance evaluation of selection schemes for offloading traffic to ieee802.11 hotspots,” IEEE ICC, pp. 5423 – 5428, 2012.

[5] F. Malandrino, C. Casetti, C.-F. Chiasserini, and M. Fiore, “Offloadingcellular networks through ITS content download,” IEEE SECON, pp.263 – 271, 2012.

[6] B. B. Chen and M. C. Chan, “Exploiting temporal dependency for op-portunistic forwarding in urban vehicular networks,” IEEE INFOCOM,pp. 1404–1412, 2009.

[7] G. e. m. Zhioua, H. Labiod, N. Tabbane, and S. Tabbane, “LTEAdvanced Relaying Standard : A survey,” Wireless Personnal Commu-nications, Springer, DOI: 10.1007/s11277-013-1157-1, 2013.

[8] G. Araniti and C. Campolo and M. Condoluci and A. Iera and A. Moli-naro, “LTE for Vehicular Networking: A Survey,” IEEE CommunicationsMagazine, vol. 51, Issue 5, pp. 148–157, May 2013.

[9] “IEEE Standard for Wireless Access in Vehicular Environments (WAVE)– Multi-Channel Operation,” IEEE Std. 1609.4, Sep. 2010.

[10] “The network simulator NS2.33,” http://www.isi.edu/nsnam.