-
On Modeling Speed-Based Vertical Handovers in Vehicular
Networks“Dad, slow down, I am watching the movie”
Flavio [email protected]
Computer Science Dept.Boston University
Boston, MA
Anna Maria [email protected]
Applied Electronics Dept.University of Roma Tre
Rome, Italy
Ibrahim [email protected]
Computer Science Dept.Boston University
Boston, MA
Alessandro [email protected]
Applied Electronics Dept.University of Roma Tre
Rome, Italy
Abstract—Although vehicular ad hoc networks are emergingas a
novel paradigm for safety services, supporting
real-timeapplications (e.g., video-streaming, Internet browsing,
onlinegaming, etc.) while maintaining ubiquitous connectivity
remainsa challenge due to both high vehicle speed, and
non-homogeneousnature of the network access infrastructure. To
guarantee accept-able Quality-of-Service and to support seamless
connectivity, ver-tical handovers across different access networks
are performed.
In this work we prove the counterintuitive result that in
vehic-ular environments, even if a candidate network has
significantlyhigher bandwidth, it is not always beneficial to
abandon theserving network. To this end, we introduce an analytical
model fora vertical handover algorithm based on vehicle speed. We
arguethat the proposed approach may help providers incentivize
safetyby forcing vehicular speed reduction to guarantee
acceptableQuality-of-Service for real-time applications.
I. INTRODUCTION
In Vehicular Ad hoc NETworks (VANETs) vehicles en-dowed with
sophisticated “on-board” equipment communicatewith each other, and
with the wireless and cellular networkinfrastructure by means of
several network interface cards (i.e.,IEEE 802.11, UMTS, HSDPA, and
so forth [1]).
Future networked vehicles represent the future convergenceof
computers, communications infrastructure, and automo-biles [2]. An
envisioned goal is to embed human-vehicle-interfaces such as color
reconfigurable head-up and head-down displays, and large touch
screen active matrix liquidcrystal displays, for high-quality
video-streaming services [3].Passengers can enjoy their traveling
time by means of real-time applications, e.g., video streaming and
online gaming,using individual terminals next to their seats (Fig.
1 (a)).
To guarantee the delivery of acceptable Quality-of-Service(QoS)
in such environments, Vehicle-to-Infrastructure (V2I)communications
represent a viable solution. However, V2Iprotocols still lack
seamless connectivity: when vehicles en-counter an area with
overlapping wireless networks, a decisionon whether or not a
connectivity switch should be executedhas to be taken. The
mechanism preserving on-the-move user’sconnectivity is defined as
Vertical Handover (VHO) [4].
In this paper we show that in VANET scenarios with
aheterogeneous network access infrastructure, bandwidth gainsare
tightly coupled with the protocol overhead—handoverlatency—and the
speed of the vehicle. Leveraging on thisconsideration, we propose a
new speed-based, QoS-oriented
Vertical Handover algorithm for vehicular networks. In therest
of the paper we refer to our algorithm as S-VHO. Theidea of using
the vehicle speed as assessment criterion forvertical handovers has
been floated before [5], [6]. However,our emphasis lays on
real-time applications for VANETs. Asa baseline of comparison for
our simulations, we consider therecent work of Yan et al. [5],
whose algorithm is based on boththe Received Signal Strength (RSS)
and the terminal speed,and we show that our S-VHO algorithm
outperforms theirapproach, in terms of throughput, delay, jitter,
and overhead(number of vertical handovers).
The paper is organized as follows. In Section II we presentan
analytical model useful to compute a speed upper boundused by our
S-VHO algorithm, described in Section III. Ana-lytical and
simulation results are shown in Sections IV, and V,respectively. We
discuss some related work in Section VI, andin Section VII we
conclude our paper.
II. ANALYTICAL MODEL
In this section, we present the counterintuitive result that
inheterogeneous vehicular networks, connectivity switches
arejustified only when the vehicle speed satisfies a given
speedupper bound. To compute this bound we need to formallydefine
(i) a valid handover in VANETs, (ii) the cell crossingtime (i.e.
the time a vehicle spends inside a wireless cell), and(iii) the
handover latency.Definition 1 (Valid Handover in VANETs): A vehicle
cross-ing an area covered by at least two wireless networks
performsa valid handover if and only if the handover results in
athroughput increase. 1
Our model assumptions are depicted in Fig. 1 (b). A vehicleis
moving at speed ~v in a vehicular environment with a
networkinfrastructure composed of several overlapping
heterogeneouswireless access networks, partially covering the road.
Thevehicle’s trajectory follows a Manhattan mobility model, andit
is constrained by the road, composed of straight lanes.Moreover,
each vehicle is assumed to be equipped with anon-board Global
Positioning System (GPS) network interface
1Note that there exist two main types of handover,
horizontal—whenthe two cells involved in the process belong to the
same technology—and vertical—when the two cells belong to different
technologies. AlthoughDefinition 1 holds for any type of handover,
in this work we focus on verticalhandovers between WLAN and UMTS,
and vice versa.
-
Pout,tout
Pin,tin
!
AP
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!UMTS
!!!!
!!!!!
!!!!!!!!UMTS
road
BS
!!"
!!"#
BS
!!!!
!!
!!!WLAN
!v s
AP
!!"
!!"#
!!!!!!!!!!!!!!!!!WLAN
−!v s
50 100 150 200 250 300 350 400
50
100
150
200
250
300
350
400
(a) (b) (c)Fig. 1. (a) Maintaining acceptable QoS level in
video-streaming applications for on-the-move users in VANETs is a
challenge, due to high vehicle speed, andcoexistence of
heterogeneous wireless networks [7]. (b) Manhattan mobility model
for a VANET scenario with heterogeneous overlapping wireless
networks(c) Hysteresis impact on the speed upper bound for
different values of handover latency [s].
handover if and only if the handover results in a
throughputincrease. 1
Our model assumptions are depicted in Fig. 1 (c). A vehicleis
moving at constant speed !v in a vehicular environment, witha
network infrastructure composed of several overlapping
het-erogeneous wireless networks, partially covering the road.
Thevehicle’s trajectory is constrained by the road, composed by
astraight lane, (i.e. Manhattan mobility model) [8]. Moreover,each
vehicle is equipped by an on-board Global PositioningSystem (GPS)
network interface card, so that the vehicleposition is always
known.
B. Modeling Cell Crossing TimeWe start modeling the cell
crossing time—the time that a
vehicle spends inside a wireless cell. Formally, we
have:Definition 2 (Cell Crossing Time): Given a vehicle
V,traversing an area covered by a wireless cell C with aconstant
speed !v such that !|v| > 0, the cell crossing time ofV in C,
denoted as ∆T , is the overall time that V can spendinside C.
Denoting by tin the time in which a vehicle enters an
areacovered by a considered wireless cell, we model the exit
timefrom the cell as:
tout = tin +∆x|!v| , (1)
where ∆x is the distance covered inside the wireless cell inthe
time interval tout− tin. In particular, if we denote as R ∈#+ \ {0}
the radius of the omnidirectional wireless cell, andas φ ∈ [0, π],
the angle between the vehicle’s line of sight withthe cell’s
antenna and the movement direction of the vehicle,we have ∆x = 2R
cos φ.
Notice that ∆x is known since we have assumed eachvehicle
equipped with a GPS receiver, so that coordinates ofthe entrance,
and exit points of the cell are easy calculated as:Pin ≡ (xin,
yin), and Pout ≡ (xout, yout). Therefore, the angleφ can be
computed as φ = arctan
(yout−yin
xout−xout
).
1Notice that there exist two main types of handover,
horizontal—when thetwo cells involved in the process belong to the
same wireless technology—and vertical—when the two wireless cells
belong to different technologies.Although definition 1 holds for
any type of handover, in this work we focuson vertical handovers
for WLAN, and UMTS technologies.
Thus, the cell crossing time is expressed as combination
ofwell-defined terms, that is:
∆T =∆x|!v| =
2R|!v| · cos
[arctan
(yout − yinxout − xin
)]. (2)
From these assumptions, we now model the throughput Θthat the
vehicle would experience remaining connected withthe Serving
Network (SN), during the cell crossing time ∆T =tout − tin, as a
function of bandwidth BSN, assumed to beconstant during ∆T , such
as Θ =∆ T · BSN.
Notice that so far we have only modeled the throughputin a VANET
network where vehicles are covered by a singleservice network; we
now model network switches, as well.
C. Network Switches and Speed Upper Bound
In this Subsection we shall both introduce heterogeneityin our
ad-hoc networks, and analyze how the speed of avehicle, through the
crossing time information, helps makinghandover decisions. To do
so, we need to consider anotherparameter in our model, that is, the
handover latency:Definition 3 (Handover Latency): Given a vehicle V
,traversing an area covered by at least two wireless cells,the
handover latency L is the time interval during which Vdoes not
receive any data due to socket switching signalingmessages
exchange.
Let a vehicle be connected to a Serving Network (SN),entering
the wireless range of a Candidate Network (CN).In this
non-homogeneous scenario, we model the differencebetween the data
collected at the two time instants tin, andtout as:
Θ(tout)−Θ(tin) = γ(L, BSN, BCN), (3)
where γ is a positive range function defined as:
γ = α · (BCN − δ) (∆T − L) + (1− α) BSN∆T, (4)
and α is an indicator function, such as α = 1 when a
verticalhandover is executed; α = 0, otherwise. Notice that whenα =
1, γ is equivalent to the throughput computed using BCN,while for α
= 0, γ is the throughput in the SN.
Through the hysteresis γ, we capture the throughput lossdue to
the vertical handover latency L. Since we aim at a
!
!x
UMTS
WLAN
BS
AP
R
50 100 150 200 250 300 350 400
50
100
150
200
250
300
350
400
(a) (b) (c)Fig. 1. (a) Video-streaming applications for
on-the-move users, (b) Manhattan mobility model for a VANET
scenario with heterogeneous overlapping wirelessnetworks, (c)
Simulated VANET scenario.
card, so that the vehicle position is ubiquitously
known.Definition 2 (Cell Crossing Time): Given a vehicle V
,traversing an area covered by a wireless cell C at constantspeed
|~v|, the cell crossing time of V in C, denoted as ∆T ,is the
overall time that V can spend under C’s coverage.
Denoting by tin the time at which a vehicle enters in anarea
covered by a wireless cell, we model the exit time fromthe same
cell as:
tout = tin +∆x|~v|
, (1)
where ∆x is the distance covered inside the wireless cell inthe
time interval ∆T = tout − tin. In particular, if we denoteas R ∈
BSN
1− L/∆T+ δ (5)
holds, where δ ∈
-
well as to promote vehicle safety applications. Providers mayin
fact offer lower data rate in those areas where the speedlimit is
lower, to induce vehicles to maintain lower speeds, inorder to
experience acceptable QoS levels — low jitter andhigh throughput —
throughout valid handovers.
III. SPEED-BASED VERTICAL HANDOVER ALGORITHM
In this section we present our S-VHO algorithm, and wediscuss
the related work — Speed Probability-Based VHO [5](SPB) — which we
compare against in our simulations. Inboth algorithms, the speed of
the vehicle is used as handoverassessment criterion.
Consider Algorithm 1. Our S-VHO accepts three inputs:the vehicle
speed ~v, the ingress time tin of the vehicle intoa wireless cell,
and the GPS location information Pin, andreturns the handover
decision variable α ∈ {0, 1}. Let avehicle connected to a SN,
entering into an area with alsoa CN coverage.
After each handover execution, the algorithm enters inidle mode
for an inter-switch waiting time period, Tw. Forexample, if a
vehicle travels at 15 m/s, a 10 seconds inter-switch waiting time
results in 150 meters covered by thevehicle, before the algorithm
is re-activated. This shrewdnessis necessary to avoid a high
handover frequency that may occurwhen vehicles travel on a border
line between two wirelesscells (effect known as ping-pong [7]).
The SPB technique instead focuses on an adaptive han-dover
mechanism between WLAN and UMTS [5], based onthe evaluation of a
handover probability Pho, obtained frompower measurements. The
handover decision is then taken bycomparing the handover
probability with a fixed probabilitythreshold PT , depending on the
vehicle speed and on handoverlatency among the two networks. In
particular, the controldecision equation to assess whether
Pho =λ ·RXth −RXWλ ·RXth −RXth
>v
2RL −
2R·BUL·BW
= PT , (7)
initiating an handover (to the WLAN hotspot) when theinequality
holds. In (7), λ is a coefficient whose larger valuedenoted more
difficulty to perform handover, RXth is thethreshold value of RSS
that denotes the successful receivingof packets, RXW the currently
measured RSS of the WLAN,R is the radius of the WLAN cell, L
denotes the averagehandover latency between WLAN and UMTS, while BU
andBW are the data rate of UMTS and WLAN respectively.
IV. ANALYTICAL RESULTS
In this section we dissect the impact of both the
handoverlatency, and the hysteresis effects on the speed upper
boundcomputed in Section II with some analytical results.
In Fig. 2 (a) we show the impact of the handover latency onthe
speed upper bound, for a given bandwidth ratio of
availabletechnologies. The bandwidth ranges were chosen according
toWLAN [8], and UMTS [9] requirements. The hysteresis factorδ was
set to zero to isolate the impact of the single parameter
Input: ~v, tin, PinOutput: α (handover decision)while inside
area with at least two overlapped cells do
if (BCN > BSN/(1− L∆T
)+ δ) then
α← 1 (VHO executed)set a decreasing counter to Tw [s].while Tw
> 0 do
idle modeend
elseα← 0
endend
Algorithm 1: Speed-based Vertical Handover Algorithm.
L (handover latency), and the range of speed was bounded by35
m/s, being a common highway speed limit.
The first take-home message confirms the validity of ourmodel,
revealing that for higher values of handover latency,the speed
bound (i.e. the maximum speed at which vehiclesexperience valid
handovers) decreases. This makes sense sincevehicles traveling at
higher speed may not spend enough timeunder higher data-rate cells
to justify the degraded perfor-mance introduced by the handover
overhead of the signalingmessages.
The second result comes by observing the epigraph — theset of
points above the drawn curves. Any point belongingto the epigraph
represents no performance gain in initiatinghandovers, even if the
CN has higher bandwidth than the SN.In contrast, for any point in
the hypograph — the set of pointsbelow the curve — valid handovers
occur. As a limit casestudy, note how the curve with zero bandwidth
gap has emptyhypograph; this follows directly from the definition
of validhandover: a handover cannot be valid when the data rates
areequal.
In Fig. 2 (b) we show the impact of the hysteresis δ [Mbps]on
the speed bound, given the handover latency L [s]. Weconsidered the
hysteresis range to be δ ∈ [0, BCN−BSN] andwe have simulated the
case BCN−BSN = 16 [Mbps], a typicalgap in data rates between UMTS
and WLAN [8], [9] . It isuseful to note that BCN−BSN is the maximum
δ after whichno valid handover would occur.
The message for this simulation setting concerns the dif-ference
in the hypographic area for different values of L:when the handover
latency increases, the hypographic areasignificantly reduces. From
this observation, it follows thathandover latency should be taken
into account when designingprotocols for seamless connectivity in
VANET, instead ofconsidering only physical parameters or speed of
the vehicles.
V. SIMULATION RESULTS
In this section we report on network performance,
i.e.throughput, delay, and jitter, as well as the number of
verticalhandovers obtained with our event-driven simulator. Details
ofthe simulator can be found in [10].
-
10!2
10!1
0
5
10
15
20
25
30
35
Handover Latency [s]
Sp
ee
d U
pp
er
Bo
un
d [
m/s
]
{BCN
,BSN
}= {18,2} [Mbps]
{BCN
,BSN
}= {18,5} [Mbps]
{BCN
,BSN
}= {18,9} [Mbps]
{BCN
,BSN
}= {18,14} [Mbps]
{BCN
,BSN
}= {18,18} [Mbps]
10!1
100
101
0
10
20
30
40
50
60
Hysteresis (!) [Mbps]
Sp
ee
d U
pp
er
Bo
un
d [
m/s
]
BSN
= 18 Mbps, BCN
= 2 Mbps
L = 0.01 [s]
L = 0.02 [s]
L = 0.04 [s]
L = 0.08 [s]
(a) (b)
Fig. 2. Performance of Speed Upper Bound. Impact of (a) handover
latency, and (b) hysteresis.
Simulation Scenario: A vehicle enters from a randomlocation and
is restricted to travel along a grid of streetsand intersections,
following a path inside a grid. Fig. 1 (c)depicts one of the
simulation scenarios, in terms of datarate distribution from three
UMTS base stations, and twentyWLAN access points, in a region of 2
km2. Typical valueshave been considered for UMTS and WLAN,
respectively [8],[9]. The location of each wireless cell has been
generateduniformly at random, and a vehicle moves in this area
withspeed in the range [5, 35] m/s. A vehicle downloads a seriesof
video frames.
Network Performance: In Fig. 3 (a) we show the through-put as
cumulative received bits in a downlink connectionfor both S-VHO and
SPB techniques, versus the inter-switchwaiting time. The
effectiveness of S-VHO is clear whenvehicle speed is below a given
limit (e.g., 20 m/s). On theother hand, SPB does not appear
sensitive to either speedor inter-switch waiting time, and its
throughput is limited.Note however, the S-VHO throughput drops when
the vehiclespeed exceeds the desired limit. Fig. 3 (b) shows, with
95%confidence intervals, how the average frame delay for both S-VHO
and SPB increases for higher speeds. This is becausethere is not
enough time to download the next frame beforethe signal from the SN
gets too weak. Moreover, S-VHOexperiences lower delays compared to
SPB, since, on average,it performs less handovers.
Jitter performance from S-VHO and SPB have been com-pared in
Fig. 4 (a), (b), (c), for different values of speed, anda fixed
inter-switch waiting time value of Tw = 10 [s]. Eachpoint
represents the cumulative jitter, defined as the differencebetween
maximum and minimum frame delay, averaged over100 simulations. As
we can see, jitter increases with speedsince two frames may be more
often coming from differentwireless networks, and also because the
cell crossing timedecreases when the speed increases.
Overhead (Handover Frequency): Fig. 3 (c) depicts, with95%
confidence intervals, the average number of handoversfor different
values of inter-switch waiting time Tw ∈ [0, 50] s.As expected, the
number of vertical handovers decreases whenthe system is idle for
longer periods (Tw increases). Since oursimulations count all the
handovers (valid or invalid), the gapbetween the S-VHO and SPB
curves represents the number
of invalid handovers that are executed not taking into
accountthe handover latency L.
VI. RELATED WORKA VHO decision is usually taken on the basis of
(i) physical
parameters e.g., received signal strength level [7],
signal-to-noise and interference ratio [11], and (ii) QoS metrics
[12].QoS-based vertical handover algorithms mostly suggest thatthe
user connectivity should be switched to a candidate net-work,
whenever the bandwidth is higher than the currentlyexperienced in
the serving network, in order to improveperceived received quality
[13]. Although this strategy seemsreasonable, in vehicular
environments it may fail due to thespeed and the time that the
vehicle is going to spend in thenew cell. In VANETs, vehicles move
at high speed, there-fore handovers should be performed on the
basis of specificfactors as vehicle mobility pattern, and locality
information,rather than standalone QoS requirements. Past solutions
havepartially but not fully considered these aspects: in [14],
forexample, the authors deal with a novel network mobilityprotocol
for VANETs, to reduce both handover delay andpacket loss rate,
while Olivera et al. [15] proposed the AlwaysBest Connected
paradigm, to achieve seamless connectivitybetween WLAN and UMTS
networks. Our method insteadfocuses on a vehicle-controlled VHO,
due to smart on-boardcomputer equipped with GPS connectivity [1].
The idea ofhandover decisions based on both vehicle speed and
handoverlatency was previously introduced in [16]. We augment
ourcontributions by extending the algorithm’s usefulness to
real-time applications, completing the performance evaluation,
andsignificantly extending the analytical results.
We compare our technique with respect to another speedbased VHO
algorithm [5], that considered a probability-drivenhandover scheme
between WLAN and UMTS, based on avehicle traveling distance
prediction within a wireless cell.Although their main results also
address the minimization ofvertical handovers, we outperform the
performance of theirapproach and we focus more on jointly improving
three QoSmetrics: delay, jitter, and throughput.
VII. CONCLUSIONS AND FUTURE WORKWe have presented, via
analytical modeling and a simula-
tion study, a counterintuitive result for vertical handovers
in
-
10 20 30 40 50
4
6
8
10
12
14
x 109
Tw
[s]
Thro
ug
hp
ut
[Bit
/s]
5 m/s
20 m/s
35 m/s
0 10 20 30 40 504.5
5
5.5
6
6.5x 10
9
5 m/s
20 m/s
35 m/s
S-VHOSPB
0 5 10 15 20 25 30 35
0
200
400
600
800
1000
1200
Speed [m/s]
Dela
y [m
s]
SPB
S!VHO
10 20 30 40 500
100
200
300
400
500
600
Tw
[s]
Num
ber
of
VH
Os
S!VHO 5 m/s
S!VHO 20 m/s
S!VHO 35 m/s
SPB 5 m/s
SPB 20 m/s
SPB 35 m/s
(a) (b) (c)
Fig. 3. (a) Throughput averaged over 100 simulations for S-VHO
(white markers), and SPB (black markers). (b) Packet delay
increases less rapidly withvehicle speed when using S-VHO. (c)
Number of Vertical Handovers for S-VHO (white markers), and SPB
(black markers) algorithms.
0 20 40 60 80 1000
200
400
600
800
1000
1200
Received Frames
|Cum
ula
tive J
itte
r| [
ms]
S!VHO
SPB
Cum
ulat
ive
Jitte
r [m
s]
Cum
ulat
ive
Jitte
r [m
s]
0 20 40 60 80 1000
500
1000
1500
2000
Received Frames
|Cum
ula
tiv
e J
itte
r| [
ms]
S!VHO
SPB
Cum
ulat
ive
Jitte
r [m
s]
0 20 40 60 80 1000
2000
4000
6000
8000
10000
12000
Received Frames
|Cum
ula
tiv
e J
itte
r| [
ms]
S!VHO
SPB
(a) |~v| = 15 m/s, Tw = 10 s (b) |~v| = 25 m/s, Tw = 10 s (c)
|~v| = 35 m/s, Tw = 10 s
Fig. 4. Cumulative jitter experienced by a vehicle averaged over
100 simulations, for different speeds —note the scale difference
among different graphs.Higher speed implies higher jitter, unless
the number of unnecessary handovers is reduced (S-VHO).
heterogeneous vehicular ad hoc networks (VANETs), that is,when a
vehicle encounters a candidate network with higherdata rate, a
connection switch will not necessarily result in athroughput
improvement.
Our proposed technique uses both handover latency and
cellcrossing time estimation to simultaneously improve
throughputand delay, and it is driven by the vehicle’ speed:
vehicles arerequired to maintain a given speed limit to maintain
acceptablelevels of throughput, delay and jitter.
The results presented in this paper are helpful for both
theresearch community, when designing novel VANET protocolsfor
real-time applications, and the business community, as theysuggest
how providers, with the help of vehicular networks,could enforce
speed limits and therefore safety while deliver-ing real-time
services as video-streaming or online gaming.
We plan to extend our analytical model into more
realisticscenarios, removing the simplifying assumptions of
constantthroughput across the coverage area, constant velocity
andpredictable vehicle motion. Moreover, experiments comparingour
handover algorithm with other QoS-based approaches(using real data
sets) are left for future work.
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