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A Centralized Approach for Setting

Floating Content Parameters in VANETs

Antonio Di Maio, Ridha Soua, Maria Rita Palattella, Thomas Engel

University of Luxembourg

Luxembourg

Email: {name.surname}@uni.lu

Gianluca A. Rizzo

HES-SO Valais

Switzerland

Email: gianluca.rizzo@hevs.ch

Abstract—Floating Content (FC) has recently been proposed as

an attractive application for mobile networks, such as VANETs,

to operate opportunistic and distributed content sharing over a

given geographic area, namely Anchor Zone (AZ). FC perfor-

mances are tightly dependent on the AZ size, which in literature

is classically chosen by the node that generates the ﬂoating

message. In the present work, we propose a method to improve

FC performances by optimizing the AZ size with the support

of a Software Deﬁned Network (SDN) controller, which collects

mobility information, such as speed and position, of the vehicles

in its coverage range.

I. INTRODUCTION

One of the main technical challenges of content dissemina-

tion in VANETs is related to the high dynamism of vehicular

topology and the volatility of inter-vehicular links, either

Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I)

communications, in which infrastructure typically comes in

the form of Road Side Units (RSUs). Such volatility hampers

the efﬁcient spreading of the content. Floating Content (FC),

a push-based communication scheme [1], was proposed to

overcome the rapid vanishing of the disseminated content by a

vehicle in a speciﬁc area, when no support from infrastructure

is available. The goal of FC is to replicate a content in all the

nodes in a limited spatial region called Anchor Zone (AZ),

associated to that content. In this way, the content is stored

probabilistically in the AZ for the content lifetime, or until

its disappearance from the AZ. During such time, the content

is made available to nodes traversing the AZ by means of

opportunistic replication. More speciﬁcally, users that traverse

that AZ and do not possess the associated content, can have

a copy of it when they are within the transmission range of

any vehicle owning a copy of the original content. Hence, the

content ends up being available on a set of nodes within the

AZ and ”ﬂoat” over time even after that the originating node

has left the AZ. When a vehicle exits the AZ and has a copy of

the associated content, it considers it as obsolete and deletes

it.

From a performance viewpoint, the main challenge in

designing the AZ size and shape, and in orchestrating content

replications, is to strike a balance between resources utilization

(e.g. storage and bandwidth) and utility of storing and repli-

cating the content. In general, this is performed by allocating

enough resources to achieve a minimum target value of an

application level performance parameter, such as the average

time to get content, or the percentage of nodes leaving the AZ

with a copy of the message (success probability) [2].

Existing works on FC focus on modeling content availability

(the percentage of nodes with content inside the AZ) and

success probability in function of various mobility metrics

such as node density, average speed, mean rate of node

encounters and arrival rate in the AZ [1], [2], [3]. These studies

assume that the generating node has perfect knowledge of

those parameters, without considering how such knowledge is

built. Of course, these parameters are essential for any practical

application of FC, as they strongly determine shape and size

of the AZ in function of the target performance. Decentralized

estimation of various features of mobility patterns for VANETs

has been already considered in literature [4], [5]. However, in

realistic settings, typically characterized by a high spatial vari-

ability in such parameters as mean node density, mean speed,

etc., the performance of decentralized estimation approaches

may suffer from low accuracy.

In general, all techniques for estimation of such parameters

suffer from some degree of uncertainty, which may lead to

conservative and inefﬁcient AZ dimensioning, or to poor FC

performance. Conservative FC dimensioning has a cost on

the system in terms of storage and bandwidth resources,

but also mechanisms for mobility estimation consume system

resources. Pursuing a high degree of estimation accuracy might

put a higher burden on the system than a more conservative

dimensioning of FC parameters based on coarser estimations.

Such tradeoff depends on speciﬁc features of the mobility

model, on techniques used to estimate some parameters related

to it, and to node density distribution.

The ultimate goal of our work is to determine an optimal

mobility estimation method that, for a target value of success

probability, maximizes FC efﬁciency by striking a balance

between centralized and distributed approach.

In this paper, we take the ﬁrst steps at tackling this issue: We

focus on FC in VANETs and we analyze how FC performance

and resource efﬁciency may beneﬁt from the availability of

a centralized entity, which interacts with moving vehicles,

collects relevant mobility data, and build estimates of those

mobility parameters for optimal FC dimensioning. In partic-

ular, we consider an SDN-based architecture for VANETs

as proposed in [6]. RSUs are SDN-enabled controllers that

cooperate with vehicles and improve the management of

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content delivery and dissemination by setting properly the size

of the AZ.

II. NE TWORK SCENARIO

As illustrated in Fig. 1, we consider a set of vehicles moving

in a geographical area, the Service Area (SA), containing a

certain number of RSUs. At any time, each vehicle may start

disseminating a certain content in a given AZ, contained in

the SA. In particular, we are interested in those setups where

the density and the coverage range of RSUs do not allow

a complete coverage of the SA. Vehicles communicate via

V2V between them and via V2I with the RSU, using in both

cases the IEEE 802.11p standard, a Medium Access Control

(MAC) protocol based on the DSRC spectrum at physical layer

[7]. We assume the AZ to be circular, though the analysis

can be extended to more complex shapes. When a vehicle

wants to create an AZ and start disseminating a content in

it, it must ﬁrst determine the AZ radius and the content

lifetime, i.e. the time after which vehicles are allowed to

discard the content. AZ radius is determined in function of

target values of performance estimators such as availability

(the mean fraction of users in the AZ with a copy of the

ﬂoating content) and success rate (the mean fraction of users

getting out of the AZ with a copy of the ﬂoating content),

as deﬁned in [2]. These estimators are strongly correlated to

nodes density, to their transmission range and their speed. In

addition to such performance parameters, we consider also

the FC efﬁciency, which takes into account also bandwidth

utilization. It is deﬁned as the mean number of transmissions

per useful content replication (an useful content replication is

a transfer of content within the AZ, from a node with content

to a node without it), for a given mean success rate. In an ideal

setting where nodes are assumed to have perfect knowledge of

mobility, and in which such knowledge comes at no cost, FC

efﬁciency depends on node mobility patterns, on node density,

and on the ratio between the AZ and vehicle range radii. In

realistic settings, the message exchanges that implement the

speciﬁc mechanism for mobility estimation have to be taken

into account too.

In IEEE 802.11p VANETs, periodic beaconing is crucial

to enable network synchronization and packets transmission

orchestration. Vehicles access the channel to broadcast their

beacons at least once in the Control Channel Interval. In our

setting, we assume these beacons broadcast (to other vehicles

and to RSUs) information about the originating vehicle’s ID,

position, speed and direction, as well as a global timestamp.

The RSUs store these parameters in a Mobility Information

Table (MIT). An entry in the table is updated when the RSU

receives a beacon from the corresponding vehicle. Besides,

an entry in the MIT is deleted if it is not updated during a

speciﬁc period of time. If a vehicle wants to start ﬂoating a

speciﬁc message, and it is in the RSU range, it sends a request

to the RSU (possibly in the form of a Wave Service Message

(WSM) [7]) for the estimated measures of average speed and

density. The RSU sends back the requested data via WSM.

When the vehicle is outside the RSU range, it estimates the

TABLE I

AZ Model parameters

Parameter Deﬁnition

viID of the generic i-th vehicle in the SA

v∗ID of the seeder vehicle in the SA

t(vi)Timestamp of the mobility information sent by vi

p(vi)Position of the vehicle viat time t(vi)

s(vi)Speed of the vehicle viat time t(vi)

R(S, D)Radius of the AZ started by the vehicle v∗

RSU(vi)RSU ID to which viis connected

C(vi)Surface of the virange

C(RSU(vi)) Surface of the RSU(vi)range

B(p(v∗), δ)Closed disk centered in p(v∗), with radius δ

V(α)Set of vehicles (IDs) in a neighborhood area α

VMI T (RSU(vi)) Set of all the vehicles (IDs) in the MIT of RSU(vi)

VC(v∗)VMI T (RSU(v∗))

Vδ(p(v∗), δ)VC(v∗)∩V(B(p(v∗), δ))

VD(v∗)V(C(v∗))

DDensity of vehicles in v∗neighborhood

Dc(V(α)) Cardinality-based density

Dd(V(α), p(v∗)) Distance-based density

SAverage speed of vehicles in v∗neighborhood

AZ radius using a collaborative and distributed approach, as

classically proposed in literature.

III. MOBILITY-BASED AZ SHAPI NG M OD EL

In this section, we detail the proposed method to compute

the AZ radius (R), used by the vehicle that wants to start

disseminating the associated FC (seeder vehicle). The AZ

radius will be function of node density and average speed

in a neighborhood of the seeder. When the average speed

of the vehicles close to the seeder is high, it is expected

that the FC success rate drops due to the reduced time the

vehicles have to exchange information. In order to increase

the number of vehicles that get the content, it is possible

to enlarge the AZ radius, which will imply a higher success

rate. Conversely, when the density of vehicles is high, it is

likely that the message will reside in several vehicles which

tend to form small clusters and therefore keep ﬂoating for

more time. In this case, it is also important to optimize other

practical parameters aside the success rate, such as minimizing

message redundancy and memory occupation, by choosing

a smaller AZ radius. Table I summarizes the notation used

hereafter for describing the mobility-based AZ shaping model.

The relationship between AZ radius, average speed and vehicle

density [2], [3] is generally in the form of:

R(S, D) = wS

D(1)

where Sis average speed in a neighborhood of v∗,Dis

vehicle density in the same neighborhood, and wis a weight

which depends on the speciﬁc mobility model, to be either

empirically ﬁxed or deﬁned using some dynamic policies.

The radius is always computed locally by each seeder vehicle

v∗, with Sand Dobtained either from an RSU, if the

seeder is in the RSU range, or through estimations performed

collaboratively by vehicles. In both cases, the WSMs messages

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)

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Fig. 1: An example scenario, showing ranges, neighborhoods and AZs in a generic SA. The thick lines represent the neighborhood boundaries

for each approach: C-based (orange), δ-based (purple) and distributed (yellow).

sent by each vehicle contain information about its current

position, speed, and timestamp:

p(vi)=(px(vi), py(vi)) ∈R2(2)

s(vi) = (sx(vi), sy(vi)) ∈R2(3)

t(vi)∈R+(4)

A. Centralized approach

When a seeder vehicle is in the range of an RSU, it can rely

on the mobility parameters estimated by the SDN controller.

Each RSU can build its own MIT, whose generic structure is

shown in Fig.1. The MIT is organized like a database table

in which the primary key is the viﬁeld and therefore no

duplicates are allowed.

Considering that the RSU memory is ﬁnite, an update policy

must be implemented to keep in the MIT only the most up-

to-date information about the mobility of each vehicle. For

instance, an expiration time can be set so that entries older

than the speciﬁed time will be discarded. In the centralized

approach, a request-response mechanism is devised: the seeder

vehicle v∗must choose which kind of neighborhood to use

(C-based or δ-based, described in the following sections) and

send to RSU(v∗)the suitable information to determine the

values of D,Sand the set of vehicles that belong to the

selected neighborhood (neighbor vehicle set). The RSU(v∗)

will compute S(Eq.12) and D(Eq.11 or Eq.10) and send their

values back to the vehicle for determining the AZ radius.

1) C-based Neighborhood: In the simpler case, the whole

surface of the RSU range, C(RSU(v∗)), can be considered

as v∗neighborhood. Given that all and only the vehicles that

send their mobility information to the RSU will have a valid

entry in the MIT, they will also be the only ones included in

the neighbor vehicle set VC(v∗). Therefore:

VC(v∗) = VMIT (RSU(v∗)) ⊆V(C(RSU(v∗))) (5)

2) δ-based Neighborhood: If the range of the RSU is too

large, it is possible to deﬁne a custom-shaped neighborhood

around the seeder vehicle, exploiting the mathematical def-

inition of neighborhood. A two-dimensional closed ball (or

closed disk) [8] in a metric space (R2, d), centered in p(v∗)

and with radius δ, is deﬁned as:

B(p(v∗), δ) = x∈R2:d(p(v∗), x)≤δ(6)

To physically shape the neighborhood as a circle, we assume as

distance function dthe euclidean (2-norm) distance in R2. Any

other p-norm distance could be used as well as the Chebyshev

distance (∞-norm) or a generic distance function to shape the

neighborhood in a custom way.

d(x, y) = kx−yk2(7)

Let V(B(p(v∗), δ)) be the set of vehicles in the closed disk

of radius δand centered on the position of the seeder p(v∗).

We deﬁne the δ-based neighbor vehicle set Vδ(p(v∗), δ)as:

Vδ(p(v∗), δ) = VC(v∗)∩V(B(p(v∗), δ)) (8)

The value of δshould be properly chosen. For instance,

δcan be heuristically set by the RSU for all the vehicles in

its range. In this case, δcould be such that the area of any

B(p(vi), δ)is a proportional fraction of the total extension of

C(RSUi). It is also possible to let the seeder vehicle determine

which is the most suitable value of δ, accordingly to its needs.

In this case, the vehicle must communicate the desired value

of δto the RSU, along with the value of p(vi). The seeder

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)

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vehicle v∗might initially use a standard value (e.g. equal to

the expected or desired AZ radius) and subsequently adjust it

using information provided by the RSU. A special case may

consist in setting δequal to the radius of seeder range.

B. Distributed approach

It may happen that the seeder vehicle is outside the range

of any RSU in the SA, therefore it does not have any

infrastructure support for determining the neighbor vehicle set

VD(v∗). In this case, it can simply consider its own range as

neighborhood, resulting in:

VD(v∗) = V(C(v∗)) (9)

The seeder collects the mobility information by asking all the

vehicles in its range for their position and speed, in a dis-

tributed fashion. After having gathered the needed information,

the density and average speed values will be computed locally

by the seeder and not provided by the RSU as in the centralized

approach.

C. Density Function

We propose two different methods for computing the vehi-

cles density Din a given v∗neighborhood. The methods can

be applied in both approaches (centralized and distributed),

choosing the appropriate neighbor vehicle set Vamong VC,

Vδ, and VD.

The density can be computed as a function of the distance

between the seeder v∗and every other vehicle in the selected

neighbor vehicle set, as in

Dd(V, p(v∗)) = X

vi∈V

vi6=v∗

kp(v∗)−p(vi)k−1(10)

Note that Eq.10 returns a density score that combines informa-

tion about number of vehicles in the neighbor vehicle set and

information on how physically close they are to the seeder.

An alternative way for estimating the density consists in

considering the cardinality of the neighbor vehicle set V,

normalized by the area of the v∗neighborhood.

Dc(V, A) = |V|

A(11)

This deﬁnition of density does not necessarily require to

know the position of the seeder and it is strictly dependent

on the area of the chosen neighborhood. Differently from

Dd(V, p(v∗)), it is not continuous but discrete with a minimum

step of 1/A. We might prefer using Eq.11 instead of Eq.10

when the information about the neighborhood area is available.

Note that A is equal to the area of C(RSU(v∗)),

C(B(p(v∗), δ)), or C(v∗), when adopting, respectively, the

centralized C-based, centralized δ-based, or distributed ap-

proach.

D. Average Speed Function

We deﬁne the average speed S as the arithmetic mean of

the speed vector modules of the vehicles in a neighbor vehicle

set V.

S(V) = 1

|V|X

vi∈V

ks(vi)k2(12)

Similarly to the density, the same formula of the average

speed can be used in both centralized and distributed scenarios,

choosing the appropriate neighbor vehicle set of vehicles V.

IV. FUTURE WORK

The increasing availability of a large amount of informa-

tion in Vehicular Ad-hoc Networks is giving rise to interest

on Floating Content (FC). FC supports infrastructure-less

distributed content sharing in an Anchor Zone (AZ). The

performance of FC paradigm in function of speciﬁc mobility

patterns and for speciﬁc applications has been at the center of

recent investigations. However, the issue of how to estimate

the main mobility features, on which AZ dimensioning is

based, has not been considered so far. More speciﬁcally, the

issue of how to maximize FC efﬁciency by striking a balance

between estimation accuracy and conservative dimensioning of

FC service is still unsolved. In this paper, we take the ﬁrst steps

at tackling this issue by proposing AZ dimensioning based on

centralized (SDN-based) estimation, as well as a dimensioning

based on information built collaboratively by vehicles through

a distributed approach. The next step of the investigation will

encompass the validation of the preliminary hypotheses and

the derivation of an overall resource consumption model, for

each of the proposed approaches. This work will lay the

foundation to determine a resource-optimal strategy for FC

dimensioning in VANETs.

REFERENCES

[1] J. Kangasharju, J. Ott, and O. Karkulahti, “Floating content: Information

availability in urban environments,” in Pervasive Computing and Commu-

nications Workshops (PERCOM Workshops), 2010 8th IEEE International

Conference on, March 2010, pp. 804–808.

[2] S. Ali, G. Rizzo, V. Mancuso, and M. A. Marsan, “Persistence and

availability of ﬂoating content in a campus environment,” in 2015 IEEE

Conference on Computer Communications (INFOCOM), April 2015, pp.

2326–2334.

[3] E. Hyyti, J. Virtamo, P. Lassila, J. Kangasharju, and J. Ott, “When

does content ﬂoat? characterizing availability of anchored information in

opportunistic content sharing,” in INFOCOM, 2011 Proceedings IEEE,

April 2011, pp. 3137–3145.

[4] G. V. Rossi, K. K. Leung, and A. Gkelias, “Density-based optimal

transmission for throughput enhancement in vehicular ad-hoc networks,”

in 2015 IEEE International Conference on Communications (ICC), June

2015, pp. 6571–6576.

[5] S. Zemouri, S. Djahel, and J. Murphy, “A short-term vehicular density

prediction scheme for enhanced beaconing control,” in 2015 IEEE Global

Communications Conference (GLOBECOM), Dec 2015, pp. 1–7.

[6] I. Ku, Y. Lu, M. Gerla, R. Gomes, F. Ongaro, and E. Cerqueira, “Towards

software-deﬁned VANET: Architecture and service,” Conference: Annual

Mediterranean Ad Hoc Networking Workshop MEDHOCNET, 2014.

[7] C. Campolo, A. Molinaro, and R. Scopigno, Vehicular ad hoc Networks.

Standards, Solutions, and Research. Springer, 06/2015 2015, editors.

[Online]. Available: http://bit.ly/1Fm4TEt

[8] M. O Searcoid, Metric Spaces. London: Springer London, 2007, pp.

71–82.

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