On Modeling Speed-Based Vertical Handovers in Vehicular Networks“Dad, slow down, I am watching the movie”

Flavio Espositoflavio@cs.bu.edu

Computer Science Dept.Boston University

Boston, MA

Anna Maria Vegniavegni@uniroma3.it

Applied Electronics Dept.University of Roma Tre

Rome, Italy

Ibrahim Mattamatta@cs.bu.edu

Computer Science Dept.Boston University

Boston, MA

Alessandro Nerineri@uniroma3.it

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.

 

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Pin,tin

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road

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AP

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!!!!!!!!!!!!!!!!!WLAN

−!v s

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(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

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(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

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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

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(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.

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(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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Standards and Applications, Auerbach publishers, 2009.[2] R. Lind et al., “The Network Vehicle-a glimpse into the future of mobile

multi-media”, IEEE Aerospace and Electronic Systems Magazine, vol.14, no. 9, pp. 27–32, Sept. 1999.

[3] “Backseat child navigation concept for kids”, available online athttp://www.slipperybrick.com/.

[4] G.P. Pollini, “Trends in handover design”, IEEE CommunicationMagazine, vol. 34, no. 3, pp. 82–90, 1996.

[5] Z. Yan, H. Zhou, H. Zhang, and S. Zhang, “Speed-Based Probability-Driven Seamless Handover Scheme between WLAN and UMTS”, inMSN, Wuhan, China, Dec. 10-12, 2008.

[6] D. Kwak, J. Mo, and M. Kang, “Investigation of handoffs for IEEE802.11 networks in vehicular environment”, in ICUFN, Hong Kong,June 7-9, 2009, pp. 89–94.

[7] S. Balasubramaniam and J. Indulska, “Vertical Handover SupportingPervasive Computing in Future Wireless Networks”, Computer Com-munication Journal, vol. 27, no. 8, pp. 708–719, 2003.

[8] “IEEE Standard for Information Technology Telecomm. and InformationExchange between Systems. Local and metropolitan area networks. Part11: Wireless LAN MAC and PHY Layer Specifications”.

[9] J. Laiho, et al., Radio Network Planning and Optimisation for UMTS,2nd edition, Dec. 2005, Chapter 6.

[10] Anna Maria Vegni, Multimedia Mobile Communications in Heteroge-neous Wireless Networks, PhD thesis, Universita’ di Roma Tre, 2010.

[11] K. Yang, I. Gondal, B. Qiu, and L.S. Dooley, “Combined SINR basedvertical handoff algorithm for next generation heterogeneous wirelessnetworks”, in GLOBECOM, Washinton DC, USA, Nov. 26-30, 2007.

[12] A.M. Vegni, M. Carli, A. Neri, and G. Ragosa, “QoS-based VerticalHandover in Heterogeneous Networks”, in Proc. of 10th Int. Symp. onWireless Personal Multimedia Comm., Jaipur (India), Dec. 3-6, 2007.

[13] A.M. Vegni et al., “A Combined Vertical Handover Decision Metricfor QoS Enhancement in Next Generation Networks”, in WiCOM,Marrakech (Morocco), Oct. 12-14, 2009, pp. 233–238.

[14] Y.S. Chen, C.H. Cheng, C.S. Hsu, and G.M. Chiu, “Network MobilityProtocol for Vehicular Ad Hoc Network”, in Proc. of IEEE WirelessComm. and Networking Conf., Budapest, April, 2009.

[15] J.A. Olivera et al., “VANBA: a simple handover mechanism fortransparent, always-on V2V communications”, in Proc. on IEEE 69thVehicular Technology Conference (VTC2009-Spring), April 26-29, 2009.

[16] A.M. Vegni and F. Esposito, “A Speed-based Vertical HandoverAlgorithm for VANET”, in Proc. of 7th Int. Workshop on IntelligentTransportation, Hamburg, March 23-24, 2010.