Interactive WiFi Connectivity For Moving Vehicles

Aruna Balasubramanian Ratul MahajanUniversity of Massachusetts, Amherst Microsoft Research

Arun Venkataramani Brian Neil Levine John ZahorjanUniversity of Massachusetts, Amherst University of Washington

Abstract – We ask if the ubiquity of WiFi can be lever-aged to provide cheap connectivity from moving vehiclesfor common applications such as Web browsing and VoIP.Driven by this question, we conduct a study of connectionquality available to vehicular WiFi clients based on measure-ments from testbeds in two different cities. We find thatcurrent WiFi handoff methods, in which clients communicatewith one basestation at a time, lead to frequent disruptions inconnectivity. We also find that clients can overcome manydisruptions by communicating with multiple basestationssimultaneously. These findings lead us to develop ViFi, aprotocol that opportunistically exploits basestation diversityto minimize disruptions and support interactive applicationsfor mobile clients. ViFi uses a decentralized and lightweightprobabilistic algorithm for coordination between participatingbasestations. Our evaluation using a two-month long deploy-ment and trace-driven simulations shows that its link-layerperformance comes close to an ideal diversity-based protocol.Using two applications, VoIP and short TCP transfers, weshow that the link layer performance improvement translatesto better application performance. In our deployment, ViFidoubles the number of successful short TCP transfers anddoubles the length of disruption-free VoIP sessions comparedto an existing WiFi-style handoff protocol.

Categories and Subject DescriptorsC.2 [Computer Communication Network] Routing proto-

cols

General TermsMeasurement, design, performance

KeywordsVehicular networks, applications, WiFi, handoff, diversity

1. INTRODUCTIONOur work is driven by two observations: one a growing

need and another an opportunity. Many users want cheapand high-quality Internet access from moving vehicles to stayconnected while traveling. Cellular networks can providesuch connectivity today, but they tend to be expensive. At thesame time, there is an increasingly ubiquitous deployment

of inexpensive WiFi (802.11) networks, and in many cases,entire cities are being covered [38, 39].

The ubiquity of WiFi provokes an intriguing question: canWiFi deployments support common applications such as Webbrowsing, instant messaging, and voice over IP (VoIP), frommoving vehicles? We are, of course, not the first to sug-gest allowing WiFi access from moving vehicles. Severalrecent works study connectivity from vehicles to open-accessbasestations. They propose techniques to improve connectiv-ity to an individual basestation [8, 19]. Some also proposeapplication-specific techniques or new applications that workwell in such environments [4, 14]. The question we pose,however, pushes the envelope beyond this type of special-caseusage to supporting common applications.

Our primary contribution is the design of ViFi, a protocolthat minimizes disruptions in WiFi connectivity in order tosupport interactive applications from moving vehicles. ViFi’sdesign is motivated by a rigorous measurement study of twovehicular testbeds in different cities. The goals of our studyare to understand the fundamental challenges in supportinginteractive applications and to explore opportunities that canbe leveraged in this environment.

We find that with current WiFi handoff methods clients ex-perience frequent disruptions in connectivity even when theymay be close to WiFi basestations. Handoffs in WiFi todayare hard, i.e., at any given time, clients communicate withonly one basestation that is expected to offer the best connec-tivity. Hard handoffs are limited by gray periods in whichconnectivity drops sharply and unpredictably, the difficultyof estimating the continuously changing channel quality tonear-by basestations, and the short-term burstiness of losses.Interestingly, we find that even though the impact on theperformance of delay or disruption-tolerant applications issmall, the user-perceived quality for interactive applicationsthat need consistent connectivity deteriorates significantly.

We also find that macrodiversity, i.e., using multiple bases-tations simultaneously, can help reduce disruptions for ve-hicular clients. Its use has been successful in cellular net-works [37]. In our context, it overcomes the limitations ofhard handoff because of independence of packet losses acrossbasestations and even outperforms an ideal hard handoff strat-egy with future knowledge of loss rates.

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ViFi exploits macrodiversity and opportunistic receptionsby near-by basestations to minimize disruptions for mobileclients. The challenge in designing ViFi is in coordinatingamong basestations that opportunistically receive packets.This coordination must be nimble enough to allow per-packetprocessing and must use the communication channel effi-ciently. ViFi addresses this challenge using a simple yeteffective probabilistic algorithm. Basestations that oppor-tunistically overhear a packet but not its acknowledgment,probabilistically relay the packet to the intended next hop,such that wasted transmissions are minimized. Unlike oppor-tunistic routing protocols for wireless mesh networks [5, 9],the per-packet overhead of ViFi is low enough to not requirebatching. Batching tends to delay packets and is thus unsuit-able for many interactive applications. And unlike diversity-based handoff protocols for enterprise WLANs [23, 25, 26],ViFi places little additional demand on the inter-basestationcommunication plane that is bandwidth limited in our setting.

We implemented and successfully deployed ViFi on ourtestbed in Redmond, Washington, for over two months. Weevaluate ViFi using our deployed prototype and simulationsdriven by traces from our testbed in Amherst, Massachusetts.Our evaluation shows that the link-layer performance of ViFiis comparable to an ideal diversity-based handoff protocoland outperforms an ideal hard handoff protocol. At the sametime, it uses the wireless medium as efficiently as a single-basestation handoff protocol.

We study the performance of ViFi for two commonly usedinteractive applications: VoIP and short TCP transfers that aretypical in Web browsing. Based on our deployment results,we find that ViFi reduces disruptions for these applicationsand improves their user-perceived quality. Trace-driven simu-lations based on the second testbed corroborate our findings.

2. EXPERIMENTAL PLATFORMSOur designs and evaluations are based on experiments

performed on two vehicular mobile network testbeds: Van-LAN and DieselNet. VanLAN is a vehicular testbed thatwe deployed in the Microsoft campus in Redmond, Wash-ington, and we use it for measurements and to conduct liveexperiments. DieselNet [7] is also a vehicular testbed thatwe deployed in Amherst, Massachusetts. Unlike VanLAN,however, in DieselNet we cannot modify the BSes, and canonly control the equipment on the vehicles. We profile theenvironment of DieselNet and use the collected traces fortrace-driven studies of the environment and to evaluate per-formance. Our intent in using two distinct environments intwo different cities is to gain confidence that our results applyin different settings.

2.1 VanLANVanLAN consists of eleven basestations (BSes) and two

vehicles. The BSes are deployed across five buildings in theMicrosoft campus, as illustrated in Figure 1. The box in thefigure, covering a 828×559 m2 area, bounds the region in

Figure 1: The layout of BSes in VanLAN.

which at least one packet is received by vehicles from anyBS. Not all pairs of BSes are within wireless range of oneanother. The vehicles provide a shuttle service around thetown, moving within a speed limit of about 40 Km/h. Eachvehicle visits the region of the BSes about ten times a day.

The BSes and vehicles have small desktops with Atheros5213 chipset radios. The antennae are omnidirectional andare mounted on the roofs of the respective buildings andvehicles. The vehicles are equipped with a GPS unit thatoutputs location information once every second. All nodeswere set to the same 802.11 channel for the experiments inthis paper.

The radios operate in the ad hoc (IBSS) mode using alocally modified Windows driver. The modifications let ususe a fixed BSSID, which prevents network partitions andreconciliation delays. They also let us log received framesalong with PHY layer information (e.g., RSSI) without placethe radio in monitor mode.

2.2 DieselNetThe DieselNet testbed is in Amherst, a college town in

Massachusetts. DieselNet consists of vehicles equipped witha computer, an 802.11 radio, and a GPS unit. To enabletrace-driven studies, we set one vehicle to log all beaconsheard from nearby BSes. Later sections describe how thisinformation is used for analysis.

In this paper, we use traces from Channels 1 and 6. Eachchannel was profiled for 3 days in December 2007, duringwhich the vehicle logged more than 100,000 beacons. Theprofiling channel was fixed so that beacons are not lost whilescanning. We limit our analysis to BSes in the core of thetown and to BSes that are visible on all three days. Thereare 10 such BSes on Channel 1 and 14 on Channel 6. Abouthalf of the BSes on each channel belong to the town’s meshnetwork and the rest belong to nearby shops. Our traces areavailable at traces.cs.umass.edu.

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3. A CASE FOR DIVERSITYBased on measurements from our testbeds, this section

presents a case for using BS diversity to support vehicularWiFi clients. We show that clients may experience many dis-ruptions in connectivity, which makes supporting interactiveapplications challenging. We also show that using multiplebasestations can mask these disruptions and improve applica-tion performance. These results set up our proposal for a newdiversity-based handoff protocol, ViFi, which we present inthe next section.

Our earlier work showed that the connectivity between avehicular client and individual BSes tends to be very chal-lenging [24]. The period between the vehicle coming in andgoing out of range of a BS is often marred by gray periodswhere connection quality drops sharply. We also showed thatgray periods are hard to predict and can occur even close toBSes.

Below, we first characterize connectivity between a movingvehicle and individual BSes and then across multiple BSes.

3.1 Connectivity from individual BSesTo study connectivity from a mobile client and a BS, we

use beacons that 802.11 BSes send every 100 ms. Usingbeacons lets us measure connectivity from BSes in VanLANas well as those in DieselNet. We cannot modify the BSesin DieselNet and hence are limited to connectivity measure-ments from the vehicle to the BS. Additional experiments onVanLAN confirm that the results below apply to both direc-tions as well as to data packets (not shown). The results inthis section on connectivity in VanLAN BSes are based ontraces collected over a 1-week period.

The period of connectivity between a BS and the vehiclecan be divided into sessions that start when a beacon is firstreceived and expire if no beacon is received for a fixed timethreshold. We use a high threshold of 60 sec to identifymacro sessions. They are intended to capture the entire periodbetween the vehicle coming in and going out of range of theBS. We capture short periods of disruption within macrosessions by also measuring micro sessions that expire witha low threshold of 2 sec (i.e., about 20 beacons are missed

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Figure 3: (a) An example path from VanLAN. Black linesrepresent micro sessions with respect to the circled BS.Dark circles represent gray periods. (b) Variation ofBRR in an example macro session from VanLAN. (c)The three-phase model.

by the vehicle). Periods that separate micro sessions within amacro session are gray periods with very poor connectivity.Our analysis ignores sessions with fewer than 10 receivedbeacons, to omit trivial sessions with distant BSes.

Figure 2 shows the durations of macro and micro sessionsin the two testbeds. The session distribution differs across thetwo environments. The macro sessions in VanLAN are longer,which is likely because its vehicles have external antennae.Interestingly, micro sessions in VanLAN are shorter, which islikely because the environment is more interference-ridden.But we see that the micro sessions in all cases are alwaysshorter than the corresponding macro sessions. The medianduration of micro sessions is roughly one-fifth of that ofmacro sessions in VanLAN and roughly one-half in DieselNet.Because micro sessions are contained within macros sessions,this implies that gray periods often break macro sessions intomultiple micro sessions. Such disruptions can significantlyhurt delay-sensitive applications such as VoIP as well as TCP-based applications. The presence of gray periods was firstreported in a recent preliminary study by Mahajan et al. [24].We confirm their findings across different environments.

Figure 3(a) illustrates the connectivity from a vehicle withrespect to the circled BS in one example trip during our study.As we can see, the connectivity is discontinuous and containsseveral gray periods. The figure also shows that gray periodsare not limited to regions where the vehicle is far; they alsooccur where the vehicle is close to the BS as well.

Our findings differ substantially from previous studies incontrolled environments [29, 17]. Contrast Figures 3(b) and3(c). The former zooms into a typical macro session and plotsthe beacon reception ratio (BRR) over 1 sec intervals. Themacro session has two micro sessions and a gray period. Evenwithin a micro session the connection quality is inconsistent,likely due to the vehicle’s movement. In contrast, the earlier

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studies find that the connectivity between a vehicle and aBS can be roughly divided into three phases, as shown inFigure 3(c). In the entry and exit phases, when the vehicleis far from the BS, connectivity is poor. In the productionphase, when the vehicle is close, connectivity is good. Ourstudy of realistic environments reveals that such well-definedare absent and connectivity can be poor even close to BSes.

We have analyzed gray periods in more detail but onlysummarize our key findings. Gray periods : (i) are hard topredict using online measures such as distance from the BSand RSSI; (ii) arise due to channel quality variations andare more likely to occur in regions with moderate-to-pooraverage connectivity. Thus, gray periods are likely to alwaysbe part of WiFi-based vehicular access since it is difficult toprovide uniformly good coverage to large, outdoor spaces.As we show in the next section, the resulting disruptions fromgray periods particularly hurt interactive applications.

4. A CASE FOR DIVERSITYThis section presents a case for using BS diversity to sup-

port vehicular WiFi clients, based on measurements from ourtestbeds. Client performance in this environment depends onthe handoff strategy. We show that handoff strategies thatuse one BS at a time, as is common today, cause disruptionsin connectivity that make supporting interactive applicationschallenging. We also show that using multiple BSes canmask these disruptions and improve application performance.These results set up our proposal for a new diversity-basedhandoff protocol, ViFi, which we present in the next section.To our knowledge, ours is the first study that evaluates varioushandoff strategies in vehicular environments.

4.1 MethodologyThere are many possible handoff strategies; for a compre-

hensive evaluation, we study six different policies. Four ofthese are practical and are based on existing literature; theother two are idealized methods and lend insight into the in-herent limitations of the practical policies. Our goal is to firstunderstand the fundamental differences among the policies,so we ignore the time taken to switch between BSes and thetime to scan for BSes. Research shows that careful designcan tackle these issues effectively [31, 6, 20, 2].

We analyze the performance of these handoff policies us-ing a trace-driven evaluation on VanLAN. During the evalu-ation, each BS and vehicle broadcasts a 500-byte packet at1 Mbps every 100 ms. We verified that self-interference ofthis traffic is minimal by comparing its packet reception ratiowith the case where only one node sends at a time. Eventhough unicast packets, followed by acknowledgments, aremore common today, broadcast packets suffice to probe theunderlying connectivity and let us measure connectivity froma sender to all receivers simultaneously [1]. Nodes log allcorrectly decoded packets and beacons. Our results are basedon analysis of traces collected over a two-week period.

We evaluate a handoff policy using these traces as follows.The policy determines which BS a client associates with ata given time. The client can communicate with only theassociated BS when using a hard handoff policy. We assumethat clients have a workload that mirrors our trace traffic;i.e., they wish to send and receive packets every 100 ms.The traces of broadcast packets and the current associationdetermine which packets are successfully received.

We use two measures to provide insight into the perfor-mance of different kinds of applications: (i) aggregate per-formance; (ii) periods of uninterrupted connectivity. Anaggregate performance measure considers the total number ofpackets delivered and the total time or distance over which thevehicle is connected. These are relevant to delay or disruption-tolerant applications that care most about total throughput,e.g., synchronizing mail folders in the background. The pe-riod of uninterrupted connectivity measures contiguous timeintervals when the performance of an application is above athreshold, for some definition of performance and threshold.Measuring periods of uninterrupted connectivity will, for ex-ample, tell us the length of time a VoIP caller can talk beforethe call quality drops. Applications such as instant messag-ing lie between these extremes; interpolating our results canprovide insight into their performance.

The six handoff policies that we study are the following.1. RSSI, where the client associates to BSes with higher

signal strength, measured as the exponential average of theRSSIs of received beacons. This policy is similar to whatmany clients, including the NICs in our testbed, use currentlyin infrastructure WiFi networks.

2. BRR, where the client associates to the BS with thehighest exponentially averaged beacon reception ratio. Thispolicy is inspired by wireless routing protocols that are basedon the reception ratio of probes [12].

We use an exponential averaging factor of half for bothmethods above and find the results robust to the exact choice.

3. Sticky, where the client does not disassociate fromthe current BS until connectivity is absent for a pre-definedtime period, set to three seconds in our evaluation. Oncedisassociated, the client picks the BS with the highest signalstrength. This policy was used in the CarTel study [8].

4. History, where the client associates to the BS that hashistorically provided the best average performance at thatlocation. Performance is measured as the sum of reception ra-tios in the two directions, and the average is computed acrosstraversals of the location in the previous day. MobiSteershows the value of history in vehicular environments [28].

5. BestBS, where at the beginning of each one-secondperiod, the client associates to the BS that provides the bestperformance in the future one second. Performance is mea-sured as the sum of reception ratios in the two directions.This method is not practical because clients cannot reliablypredict future performance.

In cellular terminology, all of the policies above use hardhandoff because the client associates with only one BS at a

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time. Using future knowledge, BestBS represents an upperbound on the performance of hard handoff methods. Com-paring it with macrodiversity methods (below) reveals theinherent limitations of hard handoff.

6. AllBSes, where the client opportunistically uses all BSesin the vicinity. A transmission by the client is consideredsuccessful if at least one BS receives the packet. In thedownstream direction, if the client hears a packet from atleast one BS in an 100-ms interval, the packet is consideredas delivered.

AllBSes is an ideal method that represents an upper boundon the performance of any handoff protocol. It exploits pathdiversity between the client and the set of nearby BSes. Be-cause of differences in CDMA and CSMA, it is not identicalto, but is inspired by, macrodiversity methods in cellular net-works [37]. Path diversity is known to improve performancein WiFi mesh and indoor infrastructure networks as well [5,25]. We study if such benefits materialize in vehicular WiFisettings.

4.2 Aggregate Performance ResultsFigure 4 shows the packets delivered by the six handoff

policies. To study the impact of BS-density, the independentvariable in the graph is the number of BSes in the system.There are eleven BSes in VanLAN, and each point in thefigure represents the average of ten trials using randomlyselected subset of BSes of a given size.

The graph shows that more packets are delivered as thedensity of BSes increases but the relative performance of var-ious methods is similar.1 AllBSes performs best, followed byBestBS, and then by History, RSSI, and BRR; the performanceof Sticky is the worst. Ignoring Sticky, all methods are within25% of AllBSes. This result suggests that for non-interactiveapplications, the choice of the exact method is not critical —however, results below demonstrate that interactive applica-tions manifest great differences among the policies. Becauseof space limitations, we omit similar results for other aggre-1We find it surprising that the curves for number of packet delivereddid not flatten. We expected the density of VanLAN to be sufficientlyhigh for performance to be the same as a sparser deployment. Thispoints to the challenge of deploying WiFi in outdoor areas such thatperformance is maximized and not just coverage.

gate performance metrics, such as the total time or distancefor which the methods provide some minimal connectivity.

History, RSSI and BRR perform similarly for all measuresthat we study. The competitive performance of History con-firms recent results [28] about the feasibility of using pastexperience to predict future performance. For visual clarity,we present results for only BRR as representative of all threein the remainder of this paper.

4.3 Uninterrupted Connectivity ResultsTo compare the ability of different handoff methods in pro-

viding uninterrupted connectivity, we start with a qualitativeexample. Figure 5 shows the behavior of BRR, BestBS, andAllBSes during one example trip of the vehicle. In this exam-ple, we define adequate connectivity to mean at least 50% ofthe packets are received in a one-second interval. Consistentwith our aggregate performance measurement, each methodprovides adequate connectivity for roughly the same totalpath length. However, BRR contains several regions of inade-quate connectivity. BestBS has fewer interruptions because ituses the optimal BS. AllBSes performs best as it uses multipleBSes to further reduce the number of interruptions.

Frequent interruptions in BRR can be explained througha detailed analysis of the connectivity between a vehicularWiFi client and a BS. Contrary to earlier studies of controlledenvironments [29, 17], we find that in realistic environmentsthis connectivity is often marred by gray periods where con-nection quality drops sharply. Gray periods are unpredictableand occur even close to BSes. With BRR, the clients of-ten find themselves experiencing a gray period with respectto the associated BS, which causes frequent disruptions inconnectivity. But because they tend to be short-lived, grayperiods do not severely impact aggregate performance. Wehave analyzed gray periods in our testbeds in more detail [3,24] but omit that analysis from this paper.

Figure 5(d) quantitatively compares the handoff policieswith respect to the cumulative time clients spend in an unin-terrupted session of a given length. We see that the mediansession length of AllBSes is more than twice that of BestBSand more than seven times that of the more practical BRR.This suggests that a practical, multi-BS handoff policy canachieve significant gains over hard handoff.

To investigate how applications with different requirementscan be supported, we now explore other definitions of ade-quate connectivity. Figure 6(a) varies the averaging intervalwhile keeping the minimum reception ratio requirement fixedat 50%; Figure 6(b) varies the minimum reception ratio whilekeeping the averaging interval fixed at one second. A longeraveraging interval represents less stringent requirements be-cause the session is said to be interrupted only if there is noactivity for a longer period. Similarly, a shorter receptionratio represents a weaker requirement. The results suggestthat when the requirements are less stringent all methodsother than Sticky perform similarly. But as the requirementsbecome more demanding the relative advantage of using mul-

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tiple BSes increases. The right end of Figure 6(b) does notrepresent convergence but a degenerate point where the re-quirements are so strict that all methods have short sessions.

4.4 Why is using multiple BSes effective?We now explain why AllBSes is significantly more effective

than using only one BS even when that BS is judiciouslychosen. We show that its effectiveness stems from two factors:(i) the vehicle is often in range of multiple BSes; (ii) packetlosses are bursty and roughly independent across senders andreceivers. In the next section, we leverage these findings inthe design of ViFi.

4.4.1 Extent of diversityTo exploit BS diversity, a vehicle must be in range of

multiple BSes on the same channel. As shown in Figure 7,this is true not only in VanLAN, which we have deployed, butalso in DieselNet. The graphs plot the CDF of the numberof BSes from which the vehicles hear beacons in one-secondintervals. Our results are consistent with measurements inother cities [8]. While future deployments may be engineeredfor diversity (§7), we find sufficient diversity even in existing

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deployments.In separate experiments (not shown here), to understand the

extent of diversity actually needed, we find that using as fewas two BSes brings most of the gain and there is no additionalbenefit to using more than three. A similar observation holdsfor cellular networks [37].

4.4.2 The nature of lossesDiversity can effectively tackle losses in the vehicular en-

vironment. In the upstream direction, diversity is effectivebecause losses are roughly independent across BSes and apacket sent by the vehicle is received by at least one BS witha high probability. In other words, the fact that a packet fromthe vehicle is lost at, for instance, the closest BS, has littlebearing on whether it is lost at another BS. This independenceof losses at receivers has been shown previously for outdoorWiFi meshes [5]. We find that it holds in our setting as wellbut omit detailed results.

Diversity is effective in the downstream direction becauseit can tackle bursty losses better than single-BS systems [25].Figure 8(a) shows evidence that losses are bursty in the ve-hicular setting. The figure plots the probability of losing thepacket (i+k) from a BS to vehicle in VanLAN given thatpacket i was lost. In this experiment, a single BS sends pack-ets every 10 ms; we pick a different sending BS for each

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Figure 8: (a) Probability of losing packet i+k from a BSto vehicle in VanLAN given that packet i was lost. (b)Unconditional and conditional packet reception proba-bility from two BSes to the vehicle.

trip by the vehicle. The probability of losing a packet im-mediately after a loss is much higher than the overall lossprobability. Thus, even when a vehicle is associated to a BSwith a low average loss rate, it can lose many packets in asmall time period, hurting interactive applications.

Diversity helps overcome burst losses because when thevehicle is in a burst-loss phase with one BS a second BScan deliver packets to it. That is, most burst losses are pathdependent (e.g., due to multipath fading) rather than receiverdependent. Figure 8(b) shows evidence that this holds forthe vehicular environment and quantifies the effect for onepair of chosen BSes in VanLAN. Each BS sends a packetevery 20 ms. P (A) and P (B) are the unconditional down-stream packet reception probabilities from BSes A and B.P (Ai+1|¬Ai) is the conditional reception probability of re-ceiving (i+1)-th packet from A given that the i-th packet fromA was lost. Other probabilities can be similarly interpreted.We see that after a loss from a BS, the reception probabilityof the next packet from it is very low. But the second BS’sprobability of delivering the next packet is only slightly lowerthan its unconditional loss probability.

5. ViFi DESIGN AND IMPLEMENTATIONIn this section, we present ViFi, a protocol designed to

minimize disruptions in connectivity between moving vehi-cles and a network of BSes. We focus on improving theunderlying link-layer connectivity. In some environments,providing continuous connectivity to applications may alsorequire higher-layer techniques, for instance, to handle IPaddress changes; for these, we rely on existing solutions [35,30].

5.1 Target environmentThe design of ViFi assumes that its operating environment

has the following characteristics.• Diversity: A packet sent by a moving vehicle can often

be heard by multiple BSes, and multiple BSes can oftendeliver packets to a moving vehicle. This assumptionis fundamental for leveraging BS diversity. It is not

necessary that the reception rate between the vehicleand each BS be high.

• Bandwidth-limited inter-BS communication: The BSescan communicate with each other. However, in WiFideployments today, inter-BS communication tends to bebased on relatively thin broadband links or a multi-hopwireless mesh. Accordingly, we assume that inter-BScommunication is bandwidth constrained.

We also assume that some of the nearby BSes can overheareach other over the vehicle-BS channel. This assumption isnot strictly necessary. For example, the functionality can befulfilled using the inter-BS backplane.

5.2 Goals and approachMotivated by the effectiveness of AllBSes, which itself

is impractical, we seek to develop a protocol that leveragesBS diversity to reduce disruptions. The key challenge isin coordinating among the BSes such that the coordinationscheme: (i) imposes minimal additional load on the inter-BSand vehicle-BS communication media; (ii) does not increaseper packet latency, as that hurts interactive applications; (iii)can handle rapidly changing sets of BSes.

Other works that leverage diversity in WiFi networks eitherassume a high-speed inter-BS backplane [26, 25] or batchpackets to amortize overhead [5, 9]. In our setting, however,high-capacity backplane is often not available. We also can-not use batching because that increases latency for packets.

Our approach is to leverage opportunistic receptions bynearby BSes, followed by probabilistic relaying. Opportunis-tic receptions provide a low-overhead but unreliable meansfor disseminating information. With probabilistic relaying,each BS relays based on an independently computed relayingprobability, which avoids the need for explicit coordinationmessages between BSes. The resulting protocol is lightweightand decentralized.

Another aspect of ViFi that differs from traditional WiFihandoff is salvaging, in which BSes attempt to save packetsthat are stranded at old BSes when the vehicle moves away.

5.3 Protocol overviewIn ViFi, the vehicle designates one of the nearby BSes

as the anchor. The anchor can be selected using any ofthe association methods that clients use today to pick a BS.Our implementation uses BRR. The anchor is responsible forthe vehicle’s connection to the Internet—packets from thevehicle are forwarded through the anchor and packets fromthe Internet destined for the vehicle first arrive at the anchor.A client acquires its IP address from the anchor BS, if needed.

The vehicle designates other nearby BSes as auxiliary. Wecurrently pick all BSes that the vehicle hears as auxiliaries.In certain environments, such as those that are highly dense,the list of auxiliaries may need to be more carefully selected.

The vehicle embeds the identity of the current anchor andauxiliary BSes in the beacons that it broadcasts periodically.Beacons enable all nearby BSes to learn the current anchor

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and the set of auxiliary BSes. Thus, changes to the identityof the anchor or the set of auxiliary BSes are communicatedto the BSes by the vehicle at the beaconing frequency. Thevehicle also embeds the identity of the previous anchor forsalvaging (§5.5).

The operation of ViFi is symmetric in both directions and isdescribed below in terms of the source, src, and destination,dst, of the transfer. In the upstream direction, the vehicle isthe source and the anchor is the destination. The roles arereversed in the downstream direction.

1. src transmits the packet P .2. If dst receives P , it broadcasts an ACK.3. If an auxiliary overhears P , but within a small win-

dow has not heard an ACK, it probabilistically re-lays P .

4. If dst receives relayed P and has not already sentan ACK, it broadcasts an ACK.

5. If src does not receive an ACK within a retransmis-sion interval, it retransmits P .

Upstream packets are relayed on the inter-BS backplaneand downstream packets on the vehicle-BS channel. A packetis considered for relaying only once, and packets overheardfrom other auxiliary BSes are not relayed. In Step 3, theoverheard ACK that suppresses relaying by an auxiliary BScould be in response to either the source’s transmission or arelayed transmission by another auxiliary BS.

It is instructive to understand why relaying by an auxiliaryBS is better than a retransmission by the source itself. Thefirst reason is that losses are bursty—if the original was lost,there is a high chance that an immediate retransmission willbe lost as well. After a loss, other nodes are better positionedto deliver the packet to the destination (§4.4.2). An additionalreason in the upstream direction is that relaying uses the inter-BS communication plane, which in many cases will be morereliable than the vehicle-BS channel.

5.4 Computing relaying probabilityThe key challenge in computing relaying probability for

auxiliary BSes is to balance the trade-off between too fewand too many relayed transmissions. With the former, theperformance will degrade to that of no diversity; the latterwill lead to excessive load on the vehicle-BS and inter-BScommunication mediums.

The relay probability computation in ViFi is based on thefollowing guidelines.

G1: Account for relaying decisions made by other poten-tially relaying auxiliaries.

G2: Prefer auxiliaries with better connectivity to the desti-nation.

G3: Limit the expected number of relayed transmissions.

The first two guidelines are easily motivated, but the thirdone is not immediately obvious. Should the number of re-layed transmissions be low or be such that at least one of them

reaches the destination? We use the former in ViFi, but wealso considered a formulation based on the latter. We outlinethis formulation in §6.5.1, and show that it leads to too manyrelayed transmissions. Similarly, we study formulations thatdo not adhere to the other two guidelines and show that theydo not perform well either.

Let B1, · · · , BK be the current set of auxiliary BSes. Letnode s be the source of a packet and node d be its des-tination, where a node is a vehicle or anchor BS depend-ing on the packet’s direction. Let pab represents the prob-ability that b correctly receives a transmission from a, fora, b ∈ {s, d,B1, ..., BK}. ViFi estimates and disseminatesthe pab using periodic beacons (§5.6).

When some auxiliary Bx hears a packet but not an ac-knowledgment, it must use a locally computed probability todecide whether to relay. The overall strategy is to computerelaying probabilities so that the expected number of packetsrelayed across all auxiliary BSes is equal to 1. Within thisconstraint, auxiliary BSes that are better connected to thedestination are preferred.

We reflect the constraint on the expected number of packetsrelayed using

K∑i=1

ciri = 1 (1)

Here ci is the probability that auxiliary Bi is contending onthis packet, that is, that Bi has heard the packet but not anacknowledgment, and ri is Bi’s relay probability. Strictlyspeaking, however, ri is the number of times Bi should relaythe packet. Except in pathological cases, ri evaluates to lessthan one. We do not allow an auxiliary BS to relay a packetmore than once.

We compute the ci using an approach described below.We then pick ri satisfying Eq. 1 in a way that favors aux-iliaries that are better connected to the destination node d.Specifically, we choose ri such that

ri

rj=

pBid

pBjd(2)

implying that ri = r · pBid for some r. Each contendingauxiliary Bx solves Eq. 1 uniquely for r, and then relays thepacket with probability min(r · pBxd, 1).

A contending relay Bx computes ci for each Bi, includingitself, as the unconditional probability:

ci = psBi(1 − psdpdBi) (3)

Here the first term, psBi , is the probability that Bi receivesthe original packet, the second is the probability that Bi doesnot hear an acknowledgment. We have assumed that the twoare independent.

The probability computation method described above isbut one of the possibly many that adhere to the guidelinesabove. We use it because it is simple and works well in ourexperiments. It strikes a balance between false positives, i.e.,relaying packets that are already at the destination, and false

8

negatives, i.e., no relaying for packets that are lost at thedestination.

In practice, false positives are also reduced because relayattempts of auxiliary BSes are not synchronized. Each auxil-iary BS has a timer that fires periodically. When that happens,the auxiliary BS uses the equations above to decide whetherit needs to relay any unacknowledged packet. In some cases,an acknowledgment arrives at the auxiliary BS even beforeits timer fires. Such an event prevents the BS from relayingunnecessarily, even if the equations indicate that the packetshould be relayed.

The combination of ViFi’s relaying probability computationmethod, asynchronous relaying timers, and suppression basedon overheard acknowledgments means that we do not need toexplicitly, temporally order the relaying BSes based on theirproximity to the destination; such ordering has been used inthe past in both wired and wireless settings [5, 15].

5.5 SalvagingSometimes a vehicle moves out of range before the anchor

BS can deliver packets from the Internet. Application perfor-mance, especially that of TCP, can suffer if such groups ofback-to-back packets are lost frequently.

To avoid this problem in ViFi, newly designated anchorssalvage packets by contacting the previous anchor over thebackplane. The new anchor learns the identity of the previousanchor from the beacons. Upon contact, the old anchor trans-fers any unacknowledged packets that were received from theInternet within a certain time threshold. We set the thresholdto one second in our experiments, based on the minimumTCP retransmission timeout. The new anchor treats thesepackets as if they arrived directly from the Internet. Our sal-vaging mechanism is inspired by DTN routing and DSR [21],but it is based on pulling data rather than pushing.

5.6 Estimating packet reception probabilitiesusing beacons

As WiFi BSes do today, ViFi nodes send periodic bea-cons. The beacons are used to disseminate information aboutthe packet reception probabilities needed by auxiliary BSes,which include those between the other auxiliary BSes and theanchor and between the other auxiliary BSes and the vehicle.

A ViFi node estimates the reception probability from an-other node to itself using the number of beacons received in agiven time interval divided by the number that must have beensent. Incoming reception probabilities are maintained as ex-ponential averages (α=0.5) over per-second beacon receptionratio. In their beacons, nodes embed the current incomingreception probability from all nodes that they heard from inthe last interval. They also embed the packet reception prob-ability from them to other nodes, which they learn from thebeacons of those other nodes. This embedded informationsuffices for an auxiliary BS to learn all the packet receptionprobabilities that it needs.

5.7 Retransmission timers

In the current 802.11 standard, acknowledgments are sentimmediately after packet transmission, so the source knowswhen to retransmit an unacknowledged packet. But acknowl-edgments in ViFi may be delayed if they are generated inresponse to a relayed packet. The delay depends on thetime for relayed packets to reach the destination, and thusretransmission timers must be set based on current networkconditions.

The ViFi source sets the retransmit timer adaptively basedon the observed delays in receiving acknowledgments. Thesource keeps track of the delays in receiving acknowledg-ments for its transmissions. Each packet carries a uniqueidentifier so that acknowledgments are not confused withan earlier transmission. The source then picks as the min-imum retransmission time the 99th percentile of measureddelays. Picking this high percentile means that sources errtowards waiting longer when conditions change rather thanretransmitting spuriously.

Transmission opportunities can arise for the source beforethe retransmission time for the earliest packet in the queueelapses. In such an event, instead of leaving the mediumidle, the source sends the earliest queued packet that is readyfor transmission. This can cause some amount of reorderingwhen a later packet reaches the destination first. In our ex-periments, we find that the amount of reordering is small anddoes not hurt TCP performance. Hence, our current imple-mentation does not attempt to order packets. If need be, it isstraightforward to order packets using a sequencing buffer atanchor BSes and vehicles.

5.8 System ImplementationWe have implemented ViFi on the Windows operating sys-

tem. Almost all of our implementation sits in user space. Aspecial in-kernel network driver receives outgoing packetsfrom the OS and hands it to our process. This process thensends it back down to the wireless interface after adding ap-propriate headers. Upon receiving incoming packets from thewireless interface, this process strips the headers and handsthe packet to the special driver which then passes it on tothe OS. We embed our own sequence numbers as identifiersin transmitted packets, though it should be possible to use802.11 sequence numbers with a tighter integration with thedevice driver.

Our current implementation uses broadcast transmissionsat the MAC layer because this lets us disable the automaticretransmission behavior of the NIC. Instead, a ViFi node re-transmits unacknowledged packets as described in §5.3. TheViFi node also send acknowledgments for received packets,since broadcast transmissions in 802.11 are not acknowl-edged. However, broadcast transmissions disable exponentialbackoff in response to losses which is intended to reduce col-lisions. Given that many losses in the vehicular environmentwill not be due to collisions but due to poor radio links, it isunclear if the standard 802.11 exponential backoff behavior

9

is appropriate. To reduce collisions, our implementation re-lies on carrier sense. The implementation also ensures thatthere is no more than one packet pending at the interface, toprevent a node from sending multiple back-to-back broadcastpackets.

As an optimization, ViFi packets carry a 1-byte bitmap thatsignals which of the last eight packets before the currentpacket were not received by the sender. This helps save somespurious retransmissions of data packets that are otherwisemade due to loss of acknowledgment packets.

Our implementation of ViFi has been deployed on VanLAN,where it ran successfully for more than two months.

6. EVALUATIONIn this section, we evaluate the performance of ViFi accord-

ing to several criteria. We show the following:• The link-layer performance of ViFi is close to ideal (§6.2).• ViFi improves application performance two-fold com-

pared to current handoff methods (§6.3).• It does that without placing little additional load on the

vehicle-BS wireless medium (§6.4).• Its coordination mechanism has low false positive and

false negative rates (§6.5).• It performs well across a range of environmental factors

such as vehicle speed and density of BSes (§6.6).In addition to the experiments presented in this paper, we

have conducted a broader study of the performance of ViFiacross a range of environmental factors. These factors includethe density of BSes and the speed of the vehicle, which wecould not control for either of our testbeds. Our results, whichare presented in a separate technical report [3], show that ViFiperforms well across these factors.

6.1 MethodologyOur evaluations use the deployment of ViFi on VanLAN

and a trace-driven simulation based on measurements fromDieselNet. The first approach provides results in the contextof complete real-world complexities. The second approachallows us to verify that the results are not due to characteris-tics specific to our deployment on VanLAN. Below, resultsthat are marked with VanLAN, are deployment-based andthose marked with DieselNet, are based on trace-driven simu-lations.

The trace-driven simulations are based on beacons loggedby the buses in DieselNet. The beacon loss ratio from a BS tothe vehicle in each one-second interval is used as the packetloss rate from that BS to the vehicle and from the vehicleto the BS. This assumption ignores any asymmetry or finer-timescale behavior of packet loss. For inter-BS loss rates, weassume that BS pairs that are never simultaneously within therange of a bus cannot reach one another. For other pairs, weassign loss ratios between 0 and 1 uniformly at random. Ourresults are based on multiple trials and random seeds.

We use a QualNet-based implementation of ViFi to analyzeperformance. The loss rates are instantiated in the QualNet

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simulator by mapping them to the corresponding path lossvalues. This method allows us to program loss rates found in areal vehicular environment and therefore includes losses dueto mobility and multipath fading, while still losing packets toevents such as collisions.

We validate our trace-driven simulation method by collect-ing the same measurements from VanLAN and comparing itsresults to the deployment, i.e., we configure the loss rate foreach one-second interval to be the beacon loss ratio betweenthe vehicle and the BS in that one second. Because we haveinter-BS beacon loss ratios in VanLAN, unlike DieselNet, weconfigure the inter-BS loss rates also as the inter-BS beaconloss ratio at each one-second interval.

We compare the performance of ViFi against BRR, the prac-tical, hard handoff protocol that we studied previously. Toensure a fair comparison, we implement BRR within the sameframework as ViFi but with the auxiliary BS functionalityswitched off. Like ViFi, BRR uses broadcast transmissionswithout exponential backoff restrictions and uses bitmap ac-knowledgments. We omit experiments that show that BRRperforms worse with unicast transmissions. The poor perfor-mance is because of backoffs in response to losses. In VoIPexperiments, for instance, the length of disruption-free callswere 25% shorter.

Our experiments are based on a fixed 802.11b transmis-sion rate of 1 Mbps to maximize range. Rate adaptation invehicular networks is an open problem as current algorithmsassume an environment that is less dynamic [19, 14].

Unless otherwise specified, results for VanLAN are basedon at least three days of data for each protocol and workloadconfiguration. Details for the data we use for the DieselNetsimulations are provided in §2.2. All errors bars in the graphsbelow represent 95% confidence intervals.

6.2 Link-layer performanceWe start by evaluating the basic link-layer connectivity

provided by ViFi. This analysis is based on the VanLANdeployment and uses a methodology similar to §4.

10

(a) BRR (b) ViFi

Figure 10: The behavior of BRR and ViFi along a pathsegment in VanLAN. Black lines represent regionswhere the reception ratio was more than 50% in 1-second intervals. Dark circles represent interruptions.

Figure 9 quantifies the performance of ViFi in comparisonto the BRR, BestBS, and AllBSes handoff policies. In this ex-periment, the van and a remote computer attached to the wirednetwork send a 500-byte packet to each other every 100 ms.Since we focus on basic link-layer quality provided by eachprotocol, link-layer retransmissions are disabled. The figureplots the median uninterrupted session length for various def-initions of interruptions, as in Figure 6. The performance ofViFi is even better than BestBS and closely approximates AllB-Ses. It is notable that our simple and practical opportunisticprotocol is able to beat the performance of the ideal single-BS protocol and approximate the ideal multi-BS protocol. In§6.4, we show that this high performance does not come atthe cost of efficiency.

Figure 10 illustrates the behavior of BRR and ViFi, in aformat similar to Figure 5. These are average case examplesfor the performance of these two protocols; individual runsdiffer. The paths are similar but not identical as they representdifferent days. We see that with BRR the path has severalinterruptions. ViFi performs significantly better, with only oneinterruption.

6.3 Application performanceOur experiments also show that the resilient link-layer

connectivity of ViFi translates into better performance forinteractive applications. In particular, we evaluate short TCPtransfers, which are motivated by typical Web workloads, andVoice over IP. In these experiments, unacknowledged packetsare retransmitted by the source at most three times. We findthat higher limits yield similar or slightly worse results.

6.3.1 Performance of TCP transfersOur TCP experiments evaluate two performance measures:

(i) the time to complete a transfer; (ii) the number of com-pleted transfers in a session, where a session is a period oftime in which no transfer attempt was terminated due to alack of progress. The vehicle repeatedly fetches a 10 KBfile from a machine connected to the wired network and the

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machine does the same in the other direction. Transfers thatmake no progress for ten seconds are terminated and startedafresh; we impose this limit because some transfers eitherhang or take a very long to complete due to packet losses atinopportune times in the TCP exchange.

Figure 11(a) shows the median time to complete a trans-fer. To isolate the benefits of diversity and salvaging in ViFi,the middle bar shows the median time for a configuration inwhich diversity was enabled but salvaging was disabled. Theresults show that ViFi’s median TCP transfer time is about0.6 seconds, which represents a 50% improvement over BRR.This improvement is higher than what would be predictedbased on the number of additional packets delivered by thelink layer (Figure 4). This brings out the difference betweenimprovement in aggregate performance versus performanceof interactive applications when using diversity. The figurealso shows that most of ViFi’s gain is a result from diversity,although salvaging does provide a noticeable gain of about10%. Given that we find that only 1.2% of the packets aresalvaged, this benefit of salvaging is disproportionate. It con-firms our intuition that the few packets that get stuck at olderbasestations when the vehicle moves away can disproportion-ately hurt the TCP performance.

We find that ViFi’s TCP performs comparably to moderncellular technologies. We added an EVDO Rev. A basedcellular modem to one of our vehicles and generated a similarTCP workload. The median connection time in the downlinkwas 0.75 sec and in the uplink was 1.2 sec. (Cellular datarates are asymmetric.)

Figure 11(b) shows the average number of completed trans-fers per session. The average for ViFi is more than twice ofBRR. Combined with its lower transfer times, this impliesthat users of ViFi will experience fewer disrupted transfers aswell as better performance for individual transfers.

Figure 12 shows the results for trace-driven simulations ofthe DieselNet environment. Unlike real runs over VanLAN,these simulations run for exactly the same period of time foreach configuration. We can thus quantify performance simplyas the number of completed transfers per second. ViFi showsa significant gain: 50% on Channel 1 and 80% on Channel6. Channel 1 performs better overall but its gain from ViFiis lower, which may be a result of its longer connectivitysessions and lower diversity level (Figure 7).

6.3.2 Performance of VoIP traffic

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Figure 12: TCP performance in DieselNet.

We evaluated the performance of VoIP sessions over ViFiby measuring the length of uninterrupted sessions. Support-ing VoIP is more challenging than TCP because quality issensitive to both loss and delay.

The industry-standard for evaluating a voice call is theMean Opinion Score (MoS), which ranges from 1–5, withlabels of perfect (5), fair (4), annoying (3), very annoying(2), and impossible to communicate (1). MoS is a perceptualmeasure, but it is commonly estimated from an R-factorscore [11] as: 1, if R < 0; 4.5, if R > 100; and 1+0.035R+7 × 10−6R(R − 60)(100 − R), otherwise. R-factor is sumof four terms R = 100 − Is − Id − Ief + A, where Is isthe signal-to-noise impairments, Id and Ief are impairmentsdue to delay and loss, and A is expectation factor, which ishigher when users expect lower quality. The impairments arefunctions of the codec.

We use the G.729 codec, which is implemented on mostVoIP devices. For simplicity, we set A to zero (though it maybe higher given the challenging environment). Then, the R-factor reduces to [11]: 94.2−0.024d−0.11(d−177.3)H(d−177.3) − 11 − 40log(1 + 10e), where d is the mouth-to-eardelay which includes the coding delay, network delay, andthe delay introduced by the jitter buffer, e is the total loss ratewhich includes losses in the network and losses due to latearrivals, and H is the Heaviside step function: H(x) = 1 ifx > 0; 0 otherwise.

Per the codec, we generate 20-byte packets every 20 ms.Following convention, we assume that the coding delay is25 ms, the jitter buffer is 60 ms, and that the wired segmentof the end-to-end path adds 40 ms (corresponding to cross-country paths in the USA) to each VoIP packet. Aiming fora mouth-to-ear delay of 177 ms (because the impairmentdue to delay increases significantly beyond that) means thatpackets that take more than 52 ms in the wireless part shouldbe considered lost. We measure one-way delays by applyingpiecewise linear regression [27] to remove clock skew andassuming that the minimum one-way delay is identical in thetwo directions.

We quantify the VoIP performance using the median lengthof time between interruptions. We deem an interruption tohave occurred when the MoS value drops below 2 for a three-second period. Three seconds is roughly the time it takes toenunciate a short English sentence and a MoS value lower

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Figure 14: Comparing VoIP performance between trace-driven simulation and VanLAN deployment.

than 2 represents a severe disruption in call quality. We arenot aware of an existing method to evaluate voice calls whenpacket delay and loss varies with time, and this definitionseemed reasonable to us. We also studied different MoSthresholds and time periods within this framework. Resultsfor those fit the qualitative behavior of Figure 9: the relativeadvantage of ViFi over BRR increases as the definition of aninterruption becomes more stringent.

Figure 13 shows the results for our deployment on Van-LAN and trace-driven simulations based on DieselNet. Be-cause our results indicate that salvaging brings little benefitfor VoIP, we do not isolate diversity and salvaging compo-nents of ViFi in the figure. The results show that the averagesession lengths are much longer with ViFi: the gain is over100% in VanLAN, over 50% in Channel 1 of DieselNet, andover 65% in Channel 6 of DieselNet. We find that the overallcall quality with ViFi is better as well. In VanLAN, the aver-age of three-second MoS scores is 3.4 with ViFi and 3.0 withBRR. Thus, our results show that users of ViFi experiencebetter call quality and significantly fewer disruptions in theirvoice calls.

Finally, we validated our simulator by comparing results ofour deployment with trace-driven experiments using VanLANtraces using two different days of trace data. Figure 14 showsthat the VoIP session lengths in the simulation come within 5seconds of the observed session length for experiments doneon two different days.

12

Upstream DownstreamA1 Median number of auxiliary BSes 5 5A2 Average number of auxiliary BSes that hear a source transmission 1.7 3.6A3 Average number of auxiliary BSes that hear a source transmission but not the acknowledgment 0.6 2.5B1 Source transmissions that reach the destination 67% 74%B2 Relayed transmissions corresponding to successful source transmissions (i.e., false positives) 25% 33%B3 Average number of auxiliary BSes that relay when a false positive relay occurs 1.5 1.5C1 Source transmissions that do not reach the destination 33% 26%C2 Cases where at least one auxiliary BS overhears a failed source transmission 66% 98%C3 Cases where zero auxiliary BSes relay a failed source transmission (i.e., false negatives) 10% 34%C4 Relayed packets that reach the destination 100% 50%

Table 1: Detailed statistics on the behavior of ViFi in VanLAN.

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6.4 Efficiency of medium usageThe higher application performance of ViFi does not stem

from it using the medium more aggressively; in fact, itsoverall efficiency is comparable to that of BRR. We measureefficiency as the number of application packets delivered pertransmission, by the source or an auxiliary BS, in the channelbetween the vehicle and the BSes.

We compare ViFi with PerfectRelay and BRR. In the Perfec-tRelay protocol, exactly one basestation relays the packet tothe destination and only if the intended destination did nothear the packet. We estimate its efficiency using packet-levellogs of ViFi. In the upstream direction, a packet is considereddelivered by PerfectRelay if at least one BS hears it. In thedownstream direction, a complication is that even if a BSrelays the packet, the vehicle may not hear it. We get aroundthis by: (i) assuming that the outcome of the relaying will beidentical to that of ViFi if at least one of the BSes relayed thepacket; and (ii) the relaying is successful if no BS relayed itin ViFi.

Figure 15 shows the results for the TCP experiments inVanLAN (§6.3.1). For upstream, we see that the efficiency ofViFi is better than BRR and nearly as high as PerfectRelay. Fordownstream, all three protocols have similar efficiency. BRRhas slightly better efficiency because in ViFi the BS chosen torelay a packet may be distant. Considering both directionstogether, we find that ViFi is slightly more efficient.

6.5 Effectiveness of coordinationIn this section, we present detailed statistics on the be-

havior of ViFi to provide insight into the effectiveness of itscoordination mechanism. Table 1 shows data from the TCP

experiments in VanLAN. Row B2 shows that ViFi has fewfalse positives, that is, relayed packets that are already presentat the destination divided by the number of successful sourcetransmissions. Comparison with the average number of auxil-iary BSes that receive the source transmission (Row A2), wecan infer that the coordination mechanism of ViFi is effectiveat curtailing unnecessary relaying. Without that, the falsepositive rate would have been 170% and 360%. Row A3reveals that overhearing acknowledgments sent in responseto either the source or a relayed transmission is not sufficientto curtail false positives; probabilistic relaying is needed aswell. If auxiliary BSes deterministically relayed wheneverthey hear source transmission but not an acknowledgment,the false positive rate would have been 60% and 250%.

Row C2 confirms that auxiliary BSes are often in a positionto relay packets that do not reach the source. Row C3 showsthat in such cases, the false negative rate is low. We definefalse negative rate as the number of times no auxiliary relaysa failed transmission divided by the number of failed sourcetransmissions. Combining the two rows, we can infer thatroughly 65% of the lost source transmissions are relayed ineach direction.

6.5.1 Comparison with other formulationsWe compare ViFi’s coordination mechanism with three

other formulations. Each formulation violates one of thethree guidelines outlined in §5.4.¬G1: Auxiliary BSes ignore the presence of other potential

auxiliary BSes. Each relays with a probability equal toits delivery ratio to the destination.

¬G2: Auxiliary BSes ignore loss rate to the destination. Eachrelays with a probability equal to 1P

i ci, where ci is that

the auxiliary BS i is contending (Eq. 3)¬G3: Auxiliary BSes relay such that the expected number of

packets received by the destination is 1. (Recall thatin ViFi, the expected number of packets relayed is 1.)Within this constraint, the objective is to minimize thenumber of relays. This formulation is an optimizationproblem: min

∑i ri ·ci subject to

∑i ri ·pBid ·ci >= 1.

An optimal solution to this optimization problem, isri = 0 if si > 1; ri = 1 if si + pBid · ci < 1; and ri =

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ViFi ¬G1 ¬G2 ¬G3False positives 19% 50% 40% 157%False negatives 14% 14% 12% 10%

Table 2: Comparison of different downstream coordina-tion mechanisms for DieselNet Ch. 1.

1−si

pBid·ciotherwise; where si =

∑j:pBjd≥pBid

pBjd · cj .In simpler terms, this solution first picks the auxiliaryBSx with the highest pBxd and sets rx=1. It stops if thatsatisfies the constraint above. Otherwise, it picks BSy

with the next highest pByd. If the constraint is satisfiedby ry=1, then y relays with a probability (1−pBxd·cx)

pByd·cy.

Otherwise, ry is set to 1, and the BS with the thirdhighest pBid is picked, and so on.

We find that compared to these other schemes ViFi strikesa good balance between false positives and false negatives.Table 2 shows the results for simulations over DieselNet’sChannel 1 environment. We see that while the false negativesfor all schemes are roughly similar, ViFi has substantiallylower false positives. Further, we observe in our experimentsthat the number of packets saved by ¬G2 is a lot lower thanViFi and that the false positive rate of ¬G1 increases rapidlywith the number of auxiliary BSes.

Figures 16(a) - (b) show that the performance of the TCPapplication of all three alternate schemes is worse than that ofViFi. However, in Figures 16(c) and (d), the VoIP performanceof ¬G1, ¬G3 is similar to that of ViFi for channel 1. Both¬G1 and ¬G3 are aggressive protocols that relay severalpackets at the expense of congesting the medium. The TCPapplication performance appears to be significantly affectedby congestion, unlike VoIP.

6.6 Findings from parameter-driven simula-tions

To understand the impact of environmental factors that wecannot control in testbed experiments, we conduct parameter-driven simulations using Qualnet.

For the QualNet experiments, the node’s were placed ran-domly and the propagation pathloss model was set to thetwo-ray model. Figures 17(a) - (d) compares the performanceof ViFi and BRR for varying BS density and speed. Our mainfindings are:

1. Across all BS density levels that we study, ViFi performssignificantly better than BRR for both TCP and VoIP experi-ments. Its relative benefit is highest for higher density levels.For these experiments, the speed was set at 30Kmh.

2. Across all speed levels that we study, ViFi performssignificantly better than BRR. While speed does not affectthe TCP experiments, higher speeds decreased the sessionlength of VoIP. We fixed the number of BSes to 8. In aseparate experiment, we also find that at low BS densities, itis better for vehicles to drive faster as they are able to get outof regions of poor coverage faster.

6.6.1 LimitationsFinally, we tested the relaying mechanism of ViFi in a range

of simulated conditions to understand where it might performpoorly. We find two such conditions. First, when the numberof auxiliary BSes is high (e.g., greater than 15). Second, allauxiliary BSes are equi-distant from both the source and thedestination. In both conditions, while the average number ofrelays per packet is one (Eq. 1), the variance in the numberof relays per packet increases, resulting in higher false pos-itives and negatives. Neither of these situations arise in ourtestbed environments. To make ViFi robust in environmentswhere they might, it can be extended such that the number ofauxiliary BSes is limited or the symmetry between them isbroken. These extensions are subject of future work.

7. DEPLOYMENT ASPECTS OF ViFi

In this section, we comment briefly on the deploymentrelated aspects of ViFi. ViFi requires changes to BSes andclients that may create an initial barrier to adoption but webelieve that these barriers are surmountable. In the caseof city-wide mesh networks [38, 39] operated by a singleadministrative entity, operators can unilaterally choose todeploy ViFi. In the case of organic deployments in individualresidences and offices, service models pioneered by Fon [16],where a service provider supplies BSes for shared access, canpave an effective deployment path.

A different issue is whether WiFi deployments would bebroad enough to enable more than a few city blocks of con-tiguous coverage; the lack of coverage between WiFi islandscan render interactive applications unusable. However, a mixof ViFi and cellular modes can be used to maintain connectiv-ity in such areas. Client devices can use ViFi — the cheaperoption — where available and use cellular elsewhere. Somecellular providers already let users switch between WiFi andcellular to save the more expensive cellular minutes [32].

Finally, ViFi is beneficial only if clients often hear multipleBSes on the same channel. While already true of organicdeployments (Section 4.4.1; CarTel [8]), this may not holdby default for city-wide meshes if they are engineered in acellular pattern with neighboring BSes on different channels.In this setting, BSes can be equipped with an auxiliary radiosuch that neighbors of a BS are tuned to the same channel asthe BS. These auxiliary neighbors interfere only minimallybecause they do not transmit often on the BS-client channel.They transmit data on that channel only when a downstreampacket is overheard but the acknowledgment is not heard.Upstream packets are not relayed on the BS-client channel.

8. RELATED WORKOur work benefits from and builds upon a large body of

work in wireless handoffs and routing. What sets it apart isits goals and the unique constraints of its target environment:enabling common interactive applications from moving vehi-

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cles using WiFi. We divide prior work into four categoriesand contrast our work with examples of each.

Using multiple BSes ViFi is inspired by the successfuluse of macrodiversity in cellular networks [37], where multi-ple BSes act in concert to improve client performance.2 Thecellular methods, however, require tight integration with thephysical layer and strict timing across BSes. These abilitiesneed expensive BS hardware that is not suitable for commod-ity wireless deployments. ViFi is a macrodiversity methodbuilt on top of off-the-shelf WiFi radios.

In the WiFi context, Distributed Radio Bridges [23], Di-vert [25], and MRD [26] also use multiple BSes to improveclient performance in enterprise WLAN deployments. TheBS coordination mechanism in these systems assumes thata high-capacity LAN is available. For instance, in MRD,BSes coordinate by sending all received frames to a centralcontroller that is responsible for forwarding only one copy tothe Internet. Thus, if clients typically reach three BSes, therequired LAN capacity is at least three times the cumulativesending rate of all clients. Because a high-speed backplane istypically not available in our setting, the coordination mecha-

2In contrast, microdiversity (e.g., MIMO) improves direct commu-nication between two nodes. It brings complementary gains [13]and can be used in our setting as well.

nism of ViFi imposes little additional load on the backplane.MultiNet [10], FatVAP [22], and PERM [36] enable clients

to associate with more than one nearby BS, for instance, toincrease throughput if the wireless capacity is greater thanthe capacity of individual wired links behind the BSes. Thesesystems do not focus on improving connectivity of the client-BS communication, which is the focus of our paper.

Opportunistic routing in static mesh networks Pro-tocols such as ExOR [5] and MORE [9] share our goal andchallenge in leveraging opportunistic receipt of packets withlow coordination overhead. Their approach is to batch pack-ets to amortize overhead across the batch; the authors rec-ommend using a batch size of at least around ten. Batching,however, is unsuitable for most interactive uses. For instance,VoIP cannot afford the delay associated with waiting for tenpackets. For short TCP transfers, the sender’s congestionwindow will frequently be smaller than the batch size. Evenfor bigger transfers, batching may interact poorly with TCP’srate control, as mentioned by the authors of ExOR. ViFi, incontrast, uses a novel probabilistic coordination mechanismthat operates on individual packets. In the future, we plan tostudy its performance in static mesh scenarios as well.

Network access from moving vehicles Early workson WiFi performance for vehicular access are based on con-

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trolled settings, with near line-of-sight connectivity and littleinterference [29, 17]. They find a relatively benign environ-ment. Our study of more realistic settings, with WiFi andnon-WiFi interferers and obstacles such as trees and build-ings, reveals a challenging radio environment with frequentdisruptions.

Several works consider the problem of transferring datausing TCP through individual BSes as the vehicle drives bythem, without maintaining connections across BSes [8, 19,14, 18]. They find that performance in this setting is severelyhindered by overheads at several layers, such as DHCP andaggressive TCP backoffs due to losses, and propose meth-ods to lower these overheads. We investigate the possibilityof continuous connectivity across BSes. We find that evenif some of the overheads they observe (e.g., DHCP) are re-moved completely, the basic link layer connectivity remainsproblematic, especially for interactive applications. An inter-esting avenue for future work is to investigate the extent towhich some of the methods proposed by these works (e.g.,for aggressive TCP backoffs) are needed when the underlyinglink-layer connectivity is improved using ViFi.

MobiSteer shows that equipping vehicles with directionalantennae can significantly improve performance [28]. Ourwork is based on omnidirectional antennae because, given thehigh cost and large form factor of directional antennae, typicalclients (e.g., laptops, PDAs) are likely to have omnidirectionalantennae. Further, while directional antennae extend reach,they do not prevent connectivity disruptions which we showcan occur even close to BSes. ViFi can complement the gainsfrom directionality when multiple BSes are visible in thecurrent sector of the antenna.

Rodriguez et al. study the performance of vehicular clientswhile transferring data using cellular networks [33]. WiFi,the focus of our work, merits an independent examination. Itdiffers from cellular in many ways, has a much shorter range,and operates in unlicensed spectrum. We find it interestingthat even though the cellular technology is expensive andthe networks carefully planned, like us, these authors find achallenging radio environment with unpredictable and sharpdrops in connection quality.

Fast Handoffs There is a large body of work on min-imizing the delay associated with handoffs in wireless net-works [31, 2, 34, 6, 20]. This delay can be a major sourceof disruption in networks that otherwise have good wirelessconnectivity. Our work instead is focused on improving thebasic connectivity itself which is quite challenging even ifthe handoff delays are minimal.

9. CONCLUSIONSOur work improves WiFi performance for interactive appli-

cations. Using measurements from testbeds in two differentcities, we showed that hard handoff methods that are usedby WiFi clients today are poorly suited for the vehicular en-vironment. These methods lead to frequent disruptions inconnectivity. We also showed that methods that leverage

basestation diversity are effective because they mask manydisruptions.

We then designed, ViFi, a practical and efficient handoff pro-tocol that exploits opportunistic receptions by nearby BSes tominimize disruptions for clients. The key to its effectivenessis a decentralized probabilistic algorithm that obviates per-packet coordination. Based on a two-month long deploymentand trace-driven simulations, we showed that ViFi has closeto ideal link-layer performance and significantly improvesinteractive experience. Our deployed prototype doubled thenumber of successful TCP transfers and doubled the lengthof disruption-free VoIP calls compared to a hard handoffprotocol.

Acknowledgments We thank Victoria Poncini and BrianZill for help with deploying the VanLAN testbed and thankBrian Lynn for help with collecting data on DieselNet. Thiswork was supported in part by the NSF Grant CNS-0519881and ARO award W911NF-07-1-0281.

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