Mobility Emulator for DTN and MANET Applications

Hayoung YoonGwangju Institute of Science

and Technology (GIST)Gwanju, 500-712, Korea

hyyoon@nm.gist.ac.kr

JongWon KimGwangju Institute of Science

and Technology (GIST)Gwanju, 500-712, Korea

jongwon@nm.gist.ac.krMaximilian Ott

National ICT AustraliaSydney, Australia

Max.Ott@nicta.com.au

Thierry RakotoariveloNational ICT Australia

Sydney, AustraliaThierry.Rakotoarivelo@nicta.com.au

ABSTRACTRepeatable experiments to study the performance orbehavior of wireless mobile adhoc network (MANET)systems such as a delay (or disruption) tolerant network(DTN) are challenging tasks. One approach to do this isto build a realistic mobile testbed, on which the mobiledevices and test applications or network protocols can bedeployed. This testbed approach, however, often resultsin expensive management and setup costs. Therefore,most of algorithms and applications considering devicemobility as a critical parameter have been tested usingtrace-based analysis and computer simulations. However,these evaluation methods are not sufficient to reflect variousnatures of mobile wireless networks. This paper presentsthe design and implementation of an On/Off-based mobilityemulation method, which virtually migrates applicationsover a static-grid testbed to mimic the device mobility. Thisemulation method assists DTN and MANET developersto evaluate their work in repeatable ways on the lab-scale static-grid testbed. Through extensive experimentalanalysis and a case study using two different testbeds, weshow that the proposed emulation method can successfullyrecreate important characteristics of mobility trace data,such as contact and inter-contact time distributions.

Categories and Subject DescriptorsC.2.2 [Network Protocols]: Protocol Verification; C.4[Performance of Systems]: Design Studies

General TermsAlgorithms, Design, Experimentation, Measurements

KeywordsDTN, MANET, emulation, mobility, testbed, repeatability,and measurement.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.WiNTECH’09, September 21, 2009, Beijing, China.Copyright 2009 ACM 978-1-60558-740-0/09/09 ...$5.00.

1. INTRODUCTIONMany research contributions in the wireless networking

area propose to apply solutions from DTN [8] problemsto MANET-related issues [7, 25, 4, 21, 24, 13, 11, 22,12]. These researches exploit contact opportunities betweentwo mobile nodes (or a mobile node and infrastructure)to maintain better end-to-end connectivity [7, 4] and todistribute contents efficiently [24, 13, 11, 22, 12] in a hostilemobile wireless environment, where exists intermittentwireless infrastructures and irregular bandwidth .

In DTN, mobile nodes may establish on and offconnectivity with their neighbors who are located within aradio range. One of the mostly studied routing algorithm inDTN is two-hop relay [6]. A source node sends a message (ora series of packets) to its neighbors. The message is carriedby neighbors and relayed its destination node when they arein a contact. The period between the time that messageis originated and the time that the message is deliveredto the destination node is considered as an end-to-enddelay. This is one of the important performance measures inDTN. There is a measure of the characterization of devicemobility called inter-contact time, the time period betweentwo successive contacts of same mobile nodes. Earlierstudies revealed that inter-contact time highly relates tothe end-to-end delay performance in DTN [19]. Therefore,recently developed DTN and modern MANET applicationsare simulated and evaluated under various mobility scenarioscapturing different inter-contact time distributions [10, 20].While simulation-based experiments are easy to manage andreproduce experimental environments, it is getting clear thatthey are not able to capture various aspects from mobilecomputing environments as they make inherent models tobe simplified.

Supporting repeatable experimental evaluations for DTNand modern MANET applications are quite challenging.Building and using a realistic mobile testbeds provides themost realistic results [21, 25]. However, it is extremely costlyto setup and hard to maintain because it requires someoneor something to actually carry the devices along the plannedtrajectories. In fact, most analysis and evaluations in thisresearch area are based on small-scale toy scenarios [24, 22,11] and computer simulations [13, 24, 10, 20]. Unfortunatelyfor the DTN and MANET application developers, thereis lack of evaluation process in the middle of unrealistic

51

Signaling or Discovery

Inter-ContactDuration

ContactDuration

time

Data Control

(a)

IDLE Communication

Signaling/Initiation

Link-broken/Time-over/

Finish

SuccessNeighbor Discovery

Failure

(b)

Figure 1: The illustration of a) DTN/MANETapplications operation and b) its modeling.

APP

MACPHY

MACPHY

MACPHY

T1 T3

T2

(a)

MACPHY

MACPHY

MACPHY

Cross-layerInteractable

T3

APP APP APP

T2T1

(b)

Figure 2: The mobility emulation in a) conventionaland 2) proposed methods.

computer simulations and hardly manageable real testbed-based experiments.

This paper proposes an emulation approach to un-realistic emulations and expensive mobile testbed-basedexperiments. The proposed emulation is based onmobility emulations over a stationary-grid wireless testbed.Specifically, we design and implement an On/Off-basedmobility emulation that virtually migrates applications overwaypoints (i.e., a sequence of static-grid nodes sampled fromthe movement trajectory) on the testbed. The proposedemulation method is done by a series of processes: 1)taking a snapshot of a running application, 2) turning-offthe application, and 3) relaunching the application whichresumes the earlier snapshot at another static-grid node. Byconsecutively conducting these processes at each waypoint,the proposed emulation method virtually migrates the testedapplication along its movement trajectory. It enablesapplications to maintain tight interactions with underlyingnetworking stack like MAC/PHY while they are moving overthe static-grid testbed. Through extensive experimentalanalysis and a case study using two different testbeds,we have verified the this approach successfully regeneratesimportant characteristics of mobility trace data such ascontact and inter-contact time distribution.

This paper is organized as follow. In Section 2, we reviewrelated work and discuss the motivation of this work. InSection 3, we outline the design of proposed On/Off-basedmobility emulator. We then present the its realization, themeasurement results using this approach, and analyze themin Section 4. The case study using mobile p2p application isdescribed in Section 5. Section 6 concludes this paper givingdirections for future work .

2. RELATED WORK AND MOTIVATIONModern MANET applications [24, 13, 11, 4, 22] exploit

the contact opportunity amongst mobile devices carriedby humans for the P2P contents sharing [13, 22, 4] and

distribution [24, 11]. Their behavior can be separated intotwo cases as illustrated in Figure 1(a). When mobile deviceshave no contact, they keep probing to discover appropriateneighbors by either passively listening or actively polling.Upon finding a neighbor to communicate with, they initiatecommunication link and exchange a message (or a seriesof packets). The duration of such data transfer is oftenrestricted below certain predefined thresholds [24, 11, 13]to make it resilient to link breaks due to the devicemobility. Thus, the communication link for message transfermay be reestablished even though the two mobile nodesare still in a contact. This behavior of the DTN andMANET application can be modeled by a state-machine, asillustrated in Figure 1(b). The state-machine shows 3 finitestates for the application, i.e., idle, discovery/initiation, andmessage transfer.

Some of modern MANET applications [24, 11] and DTNtestbeds [7, 21] interact with underlying MAC/PHY andnetwork layers to maximize the utilization of the contact du-ration by effetively sensing neighboring devices and quicklyswitching their device’s operational mode. For instance,Carbernet [7] introduces QuickWiFi that reduces connectioninitiation time including association/athentication, IPaddress inquiring, and connection assessment processes doneat the separate network stacks. QuickWiFi integratesthose processes into the single process at the userlevel application leveraging interaction with underlyingMAC/PHY and network layers. Bluetorrent [11] andMOVi (mobile opportunistic video-on-demand) [24] switcheswireless interface from infrastructure to adhoc modes upondiscovering peers to exchange data with.

There are a few existing studies mobility emulation overthe static-grid topology [17, 9, 15]. The main idea is toconnect the mobile nodes to the testbed, map the generatedtraffic from a mobile application to grid nodes via networktunnels (over high-speed link such as gigabit Ethernet)and transfer the traffic from wireless network interface atgrid nodes. As depicted in Figure 2(a), the mobility ofapplication is emulated by switching the tunneled staticnode along the mobility trajectory. Unfortunately, tunnelingand spatial switching approaches are not favorable to studyDTN and modern MANET applications, as they make ithard for applications to interact with underlying networkstacks. Examples of such interactions across networkstacks could be either by polling layer-specific measurementdata (for example, the received signal strength indicator(RSSI) information with access points (APs)) or triggeringto change the operational mode of the layers. Thoseinteractions are usually done via OS (operating system)supported system calls such as ioctl and /proc file systemsinterfaces [24, 14, 5]. These cross-layer interactions cannot be implemented efficiently when using the emulationmethods proposed by [17, 9, 15], which separate theapplication and underlying network stacks on differentphysical nodes.

We have designed an On/Off-based mobility emulatorwhich migrates a user application running over differentnodes at different times. Instead of the previous tunnelingand switching method, in the proposed scheme, mobilityis emulated by migrating running instance of applicationsalong waypoints of the mobility path as illustrated inFigure 2(b). Our approach allows applications to maintaintight interactions with underlying networking stack like

52

EmulatorServer

Trace

APP1

APP1

EmulatorClient

EmulatorClient

at T0 APP1 on Node1at T1 APP1 on Node2

Snapshot/Off

Snapshot (APP1)

RelaySnapshot (APP1)

ACKT41 :

Node1

Node2T2

1 :

Turn-Off APP1

T11:

On/ResumeSnapshot (APP1)

T31 :

Figure 3: The operational procedure of proposedOn/Off-based mobility emulator.

MAC/PHY, while they are moving over the static-gridtestbed.

3. MOBILITY EMULATOR

3.1 Proposed Emulator OverviewFigure 3 illustrates the operation of the proposed

emulator. There are two logical entities in our mobilityemulator design. Emulator Server (ES) reads the trace filewhich tells the latest location for each application and thentriggers Turn-off/Snapshot, Relay, and Launch/Resumeoperations. Emulator Clients (ECs), running at the currentand next waypoints of each application, perform thoseoperations. As soon as EC receives a Turn-off/Snapshotmessage from ES, EC forwards it to the application runningon the same node. The application itself then takes aSnapshot by encoding internal application status, packetizeit, transfer it to EC, and turn-off itself. When EC receivesthe Snapshot, it simply relays it to ES. ES then forwards itto the EC at the next location of the application with thecommand to launch application which resumes the relayedSnapshot.

The reality performance of the proposed mobilityemulation highly depends on the migration speed. Theshorter the migration latency, the higher the degree of realityperformance. Normally, migration latencies are applicationspecific. If applications tested in mobility emulations havetoo long latency to be launched and the volume of theSnapshot is too large to be relayed for short time, theymight not be properly evaluated by the proposed method.For example, an application downloads a large size filemay require to keep the incompletely downloaded file acrossmigrations. Thus, it might take long time to relay Snapshotincluding the incomplete piece of file. As another example,video streaming applications may accompany a video playerthat normally takes non-trivial startup latency.

However, these applications can be simplified for theemulation experiments as long as developer can track bothperformance and operation of tested applications duringexperiments. For the former downloading applicationcase, the Snapshot could include progress indicator of filedownloads instead of entire incomplete data file. If thedownloading file is indexed as p2p file-sharing applications,including the index of each chunk stored local cache enablesdevelopers to track the progress of file downloading. Forthe latter video streaming application case, there is wellknown performance evaluation methods to estimate video-playout quality without an actual video playout [24]. Inaddition, one may minimize the latency to turn-off and

Algorithm 1 Mobility emulation procedure at ES.

1: AppCnt[] ⇐ 02: OriginalT race[] ⇐ getTrace()3: ApproxTrace[] ⇐ doApproximation(OriginalT race)4: L ⇐ sizeof(ApproxTrace)5: sortInTime(ApproxTrace)6: for i = 1 to L do7: Tnow ⇐ getCurrentTime()8: AppID ⇐ getAppID(ApproxTrace(i))9: Tmig ⇐ getMigrationTime(AppID)

10: LOCnext ⇐ getNextLoc(AppID)11: doSleepFor(Tmig − Tnow)12: if AppID is NOT new then13: LOCcurr ⇐ getCurrentLoc(AppID)14: DestoryApp(AppID,LOCcurr)15: Decrement AppCnt[LOCcurr]16: end if17: if AppCnt[LOCnext] is NOT 0 then18: LOCnext ⇐ reLoad(Tmig, AppID,OriginalT race)19: end if20: LaunchApp(AppID,LOCnext)21: Increment AppCnt[LOCnext]22: end for

relaunch application by replacing them respectively to pauseand resume operations.

3.2 Design of On/Off-based MobilityEmulator

3.2.1 Mobility trace approximationSuppose there are traces containing N mobile devices’

mobility trajectory for experiment time T . Each has aseries of 3-tuple record <t, i, (x, y)>, where t ∈ (0, T )indicates time, i ∈ (1, N) is a unique id of device (orapplication) and (x, y) is the coordinate of i. The mappingis approximating the coordinates of each record for allN and entire T to the predefined static-grid topology<(Xm, Y m), (Dx, Dy)>, where (Xm, Y m) is the size ofterritory and (Dx, Dy) is the dimension of concentration.For the simplicity, we assume that the topology is uniformstatic-grid so Xm = Y m = M and Dx = Dy = D. We alsonote that D is decided by the positive integer approximationfactor A as D = M/A 1. The coordinate approximation(x, y) of (x, y) is done as

if (n − 1)D ≤ x < nD, then x =D

2+ (n − 1)D, and

if (n − 1)D ≤ y < nD, then y =D

2+ (n − 1)D

for all n = 1, ..., A.

3.2.2 Handling concurrent applicationsWhile the proposed emulator migrates applications over

a set of waypoints, it might be happen to put more thanone applications on the same waypoint. It is commonfor DTN and MANET application developers to assumethat their developing applications will not operate on thesame node simultaneously because they would rather beinterested and focused on how contacted mobile devices

1The choice of optimal A is out of scope in this paper.

53

11 nodes

11 nodes

1 meter

1 meter

[1,1]

[1,11]

[11,1]

[11,11]

Figure 4: ORBIT topology used in experiments.

interact with each other. Though static-grid testbeds mayprovide multiple wireless networking interfaces to each node,the communication between applications running on thesame node is not very natural. Thus, we restrict that morethan one applications shall not be located at the same nodeconcurrently. When it happens, we relocate the applicationto the next closest waypoint which does not carry anyapplication on it. The emulation procedure including thisrelocation executed at ES is briefed in Algorithm 1.

3.2.3 Delayed migrationSuppose that we happen to migrate an application, which

is in the middle of message transfer (i.e., the applicationin the Communication state; see Figure 1(b) in Section 2).If the next waypoint is still in a range of valid contact,carrying application status from one to another is notenough even though the migration has quickly done. Tokeep the transparency, it is indeed required to configure theMAC/IP address exactly same as the one located before.

Configuring network interfaces at each migration is notsuitable for our case because it takes order of seconds toconfigure and raises other troubles such as address conflicts.If we only allow applications to migrate at the IDLE states(see Figure 1(b) in Section 2), however, we can use differentMAC/IP address for the same application while it keepstransparency by relaying application status including aunique ID configured at the application layer. This delayedmigration is seen by neighbors as the one neighboring devicedisappeared after the data transfer and new neighboringdevice owns same application status with disappeared onecontacted.

4. REALIZATION AND VERIFICATIONIn this section, we introduce the experimental results and

those analysis performed in two grid testbeds. We first briefthe experimental setup and show how the proposed mobilityemulator generates the human mobility trace over static-gridtestbed.

4.1 Testbeds and Mobility TraceWe use two different testbeds for experiments. The one

is ORBIT [3], a 20x20 static-grid and uniformly installedwireless testbed hosted at Winlab, Rutgers University [16].Since the ORBIT users are competing to reserve theirexperiment time slots, the allowance for us is boundedto a few hours for a day. To handle the testbed easilywithin a limited experimental time slot, we only utilize11x11 subset of nodes The other testbed is NORBIT,non-uniformly installed wireless testbed at NICTA. TheNORBIT is composed with 40 static nodes spread over 3floors of NICTA building in Sydney.

Both ORBIT and NORBIT are powered by the cOntroland Management Framework (OMF) [2] that is a suite ofsoftware components, which provides control, measurement,and management tools & services to users and operators ofnetworking testbeds.

We obtained human mobility trace from Levy-walk modeltrace generator introduced in [18]. This model has claimedthat the generated traces can sufficiently keep the natureof human walk such as heavy-tail inter contact timedistribution reported in [19]. We have prepared the scenariofor experiments as 1) 1000 x 1000 territory (M = 1000),2) Cr (contact range) = 250m, 3) 10 mobile nodes and4) 1000 sec duration. We keep the mobility model specificparameters as default such as α = 0.99 and scalefactor =10 [18]. It is noted that the trace contains 10 human-drivenmobile nodes which has movement speed at around 1 m/secin average.

4.2 Emulator Realizationand Verification Scenario

We have developed a simple DTN and MANET applica-tion nullApp using C and the modification of MadWiFi [1]and configured with EC and ES implementations. EachnullApp is mapped to each mobile from the generated traceand moved over static-grid topology until the experimentfinishes. Both emulator and nullApp were established as anexperiment on top of OMF.

The nullApp periodically (at every 400 msec) broadcastslink-layer probing messages. The message includes the IPaddress of application, source MAC address and application-level unique ID. For every received probing message at theMAC-layer, the list of neighboring nullApp is updated into4-tupule <SINR,MACaddr, IPaddr,AppUID>, wherethe SINR (signal-to-noise ratio) value is calculated fromreceived probing message. The nullApp periodically (atevery 200msec) gathers the list from MAC-layer via IOCTLand forward to the central measurement repository pointconnected with gigabit ethernet cable to each static-gridnode. Thus, central measurement repository can haverealtime link-quality map over entire of nullApp.

The following is the list of measurement metrics that weare interested here for the analysis.

1. Mobility trajectories: We measure the mobilitytrajectories for four cases 1) Orig which is use ofpure trace, 2) Apprx which is use of approximatedtrace, 3) Sim which is approximated and resolvestwo applications in the same node events, and 4)Emul which is the output movement trace containingimpacts caused by delayed migrations and relocations.

2. Snapshot relaying and launch latency: This isthe summation of both latencies to relay the Snapshotand to launch new applications. We vary the volumeof Snapshot and observe the latency variation. Thevolume size is set to as multiple of Ethernet maximumtransmission unit (MTU) size (i.e., 1500 bytes). Weobserve more than 5,000 samples of migration foreach configuration. For each sample, we measure thetime that the first Snapshot packet is received at ES(TRelayStart) from current waypoint and the time thatACK received at ES (TACK) from next waypoint. Thelatency is calculated by subtracting TRelayStart fromTACK .

54

N1-OrigN10-Orig

(a)

N1-ApprxN10-Apprx

(b)

N1-SimN10-Sim

(c)

N1-EmulN10-Emul

(d)

Figure 5: Trajectories of mobile node movementover 1000 x 1000 field for 1000 sec from (a)original from Levy walk model, (b) grid-mapped,(c) simulation and (d) experiment.

3. Contact & Inter-contact time distribution: Thecontact and inter-contact time distribution are thekey parameters affecting to the performance of DTNand MANET applications [19]. We measure thecontact and inter-contact time distribution for fourcases above. For the case 4) Emul, we also conduct theSINR-based contact and inter-contat time distributionmeasurement. It is more realistic for the runningapplication’s point of view.

4.3 Verification Results

4.3.1 Mobility trajectoriesFigure 5(a) to Figure 5(d) show the mobility trajectories

for selected two nodes (only intended for the better graphicalrepresentation) from the Levy-walk model. By comparingFigure 5(a) to Figure 5(d), we can find the major perceptualdifferences in the shape of trajectories caused by theapproximation step. Similar degree of perceptual distortionin trajectories from both Figure 5(c) and Figure 5(d)respectively is compared to Figure 5(b) and Figure 5(c).But the loss is not as much as high for the approximationstep. We have conducted the same measurements for RWP(random waypoint)-model trace case, and they show thesame tendency (α = 0 generates RWP trace [18]). It is notedthat the average migration interval for each mobile node ismeasured about 50 sec given human mobility traces fromLevy-walk model. For RWP trace, more diffusive than Levy-walk trace, the average migration interval for each mobilenode is measured around 18 sec.

4.3.2 Snapshot relaying and launch latencyFigure 6 shows the summation of both latencies to relay

the Snapshot and to launch new applications with respect tothe volume of Snapshot. This measurement indicates thatthe latency increases as the volume of Snapshot increases.The latency increment for 70.5 Kbytes case compared to 4.5

10

15

20

25

30

35

40

45

50

55

4.5 10.516.522.528.534.540.546.552.558.564.570.5

average delay of status message relay (msec)

size of status message (Kbytes)

STDV

Figure 6: Summation of the Snapshot relaying andrelaunch latency.

10-2

10-1

100

100 101 102 103

P (X>x)

inter contact time (sec)

OrigApprx

SimEmul

(a)

10-2

10-1

100

100 101 102 103

P (X>x)

contact duration (sec)

OrigApprx

SimEmul

(b)

Figure 7: The CDF of a) inter-contact time and b)contact time from 10 nodes Levy-model trace withspatial contact evaluations.

Kbytes one is about double. It indicates that the relayingand launch latency is not highly sensitive to the size ofSnapshot. Since the Snapshot is relayed via 1Gbit ethernetlink, the latency is mainly due to the link propagation delayand launching/resuming a new nullApp.

The maximum size of Snapshot is set to 70.5 Kbytes usedin the experiments. It may not be enough to carry the wholeinternal status of an emulated application. For the mobilep2p applications, for instance, the cached data volumes mayeasily excess 70.5 Kbytes. However, relaying whole cacheddata is waste for the emulation experiments (see Section 3.If we encode the storage status into the bitmap and assumethat the unit of transfer is a1420 bytes size packet, 70.5Kbytes (in a bitmap) Snapshot can represent about 800Mbytes (1420 × 8 × 70.5k) data [24].

4.3.3 Contact & Inter-contact time distribution:Spatial contact evaluation

Figure 7(a) and Figure 7(b) show the measurement ofinter-contact and contact time distributions, respectively.The evaluation of contact is done by spatial contactthreshold. Note that we have randomly chosen one mobilitytrace, conducted measurements via computer simulations,and then interpolated them to draw CDF for the Orig,Apprx and Sim cases. The measurement for Emul caseis obtained from 15 rounds of experiments using nullAppat ORBIT. Similar to the movement trajectory cases, themajor loss in inter-contact and contact time distributionhappens in the approximation step. The Apprx, Sim andEmul cases all show similar distributions. It indicates thatthe delayed migrations and relocations (see Section 3) donot have much affect.

55

Table 1: 5, 25, 50, 75 and 95 percentiles of inter-contact (ICT) time depicted in Figure 7(a) andcontact time (CT) depicted in Figure 7(b).

ICT-percentiles 5 25 50 75 95Orig 20.0 63.5 124.5 413.5 634.0

Apprx 3.25 17.5 49.0 196.5 608.0Sim 3.25 18.0 49.5 205.5 645.0

Emul 5.0 17.0 50.0 207.0 646.0CT-percentiles 5 25 50 75 95

Orig 14.5 40.5 111.5 284.0 394.5Apprx 4.25 27.5 65.0 191.5 369.5Sim 3.25 22.0 50.5 191.5 467.0

Emul 6.25 20.0 66.5 181.5 467.0

15

20

25

30

35

40

45

50

1 2 3 4 5

SIN

R (

dB)

distance

([i, j], [i+hops, j])([i, j], [i, j+hops])

([i, j], [i+hops, j+hops])

Figure 8: The average link quality measurement inSINR between a pair of nodes in ORBIT.

Table 1 summarizes the results by taking 5th, 25th, 50th(median), 75th and 95th percentiles of distributions. Mainlythe loss is due to the over-generated short-term contacts andinter-contacts after the approximation of the original trace.For 5th percentile, the approximation shortens the inter-contact time 4 times less and contact time 3 times less. For50th and 75th percentiles, the differences are reduced toaround 2 times less for both cases. For 95th percentile, theinter-contact and contact time differences respectively fallinto 10% and 20% range.

4.3.4 Contact & Inter-contact time distribution:SINR-based contact evaluation

For real DTN and MANET applications, the evaluation ofcontact is done by comparing the received probing packet’sSINR value with predefined threshold Cr. If SINR is largerthan Cr, the sender nullApp and receiver nullApp have acontact. To decide appropriate Cr in SINR, we measurethe link quality in terms of SINR over all pairs of N hop(physical distance) in ORBIT.

Figure 8 shows the link quality in SINR measured bynullApp. The horizontal axis represents the physicaldistance between two nodes on arbitrary links on thetestbed. The measured SINR for the links, for example,[(1, 2), (1, 3)] and [(10, 9), (10, 10)] are dealt into samecategory as [(i, j), (i, j+hops)], wher hops equals to 1.The measurement results show that SINR decreases as thedistance between two nodes increases. For both [(i, j), (i,j+hops)] and [(i, j), (i+hops, j)] cases, the measured averageSINR drops in almost similar tendency as the distanceincreases. The measured average SINRs over [(i, j), (i+hops,j+hops)] links are always smaller than the one of both [(i, j),(i, j+hops)] and [(i, j), (i+hops, j)]. The reason is that the

10-2

10-1

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100 101 102 103

P (X>x)

inter contact time (sec)

SimEmul (Cr=30 dB)

(a)

10-2

10-1

100

100 101 102 103

P (X>x)

contact duration (sec)

SimEmul (Cr=30 dB)

(b)

Figure 9: The CDF of a) inter-contact time and b)contact time from 10 nodes Levy-model trace withSINR-based contact evaluations.

Table 2: 5, 25, 50, 75 and 95 percentiles of inter-contact time depicted in Figure 9(a) and contacttime depicted in Figure 9(b).

ICT-percentiles 5 25 50 75 95Sim 3.25 18.0 49.5 205.5 645.0

Cr = 30dB 7.0 32.5 74.25 208.0 708.25CT-percentiles 5 25 50 75 95

Sim 3.25 22.0 50.5 191.5 467.0Cr = 30dB 6.0 15.25 33.75 58.5 181.25

distance between [(i, j), (i+hops, j+hops)] is always longerthan for link [(i, j), (i+hops, j)] or [(i, j), (i+hops, j)] inthe ORBIT. Thus, the received signal strength of probingpacket for [(i, j), (i+hops, j+hops)] links is likely smallerthan for links [(i, j), (i+hops, j)] or [(i, j), (i+hops, j)].

Figure 9(a) and Figure 9(b) depict the measurementof inter-contact time and contact time distributions,respectively. We have set Cr to 30 dB so that thetwo nullApps located within 3 hops apart can get acontact (see Figure 8). Note that this Cr configurationis also intended to separate the grid topology similar tospatial contact evaluation case that partitions 1000mx1000mterritory with 250m contact range. As summarized inTable 2, the intended inter-contact time distribution fromSim is successfully regenerated in the experiments. For75th and 95th percentiles, the difference between Sim andEmul falls into 10 % range. For 5th, 25th and 50th, theinter-cntact time of Emul case is about two times longerthan Sim case. Unlike to the inter-contact distribution,Emul case achieves much less contact-duration comparedto the Sim case all over the percentiles except 5th one.We have found that this shorter contact time duration isdue to the link SINR variation. Even though the twonullApps spatially contacted, the measured SINR betweenthem occasionally falls down less than Cr for short timeperiod. This indeed increases the number of short-termedinter-contact time while reduces the number of long-termedcontact time.

5. CASE STUDYIn this section, we introduce the case study to verify

how the proposed emulation method combines with the realapplications. Note that the results in this case study mightbe specific to the tested applications and not be generalizedfor the other types of applications.

56

1 meter/sec

20 m

MC1 MC2

(a)

1 meter[1, 1] [1, 20]

MC1 MC1 MC2MC2

(b)

Figure 10: The scenario for evaluation of a) actualmovement and b) emulation.

5.1 Mobile P2P Video Streaming: study setupWe have performed case study using MOVi [24] (Mobile

Opportunistic Video-on-demand), a mobile p2p videostreaming application that selectively utilizes downlink path(AP→STA) and p2p path (STA→STA) for data delivery.MOVi comprises of two logical components: a mobile clientnode called MOVi Client (MC) and a network of serversin the back-haul known collectively as MOVi Server (MS).MOVi Server maintains three key functions. It derives aconnectivity map of link quality between MCs (i.e., therelative ‘location’ of MCs), schedules the direct transferof content segments between pairs of MCs based on themap, and tracks the content delivery and caching statusof all MCs within its domain. In contrast, MC maintainstwo key functions. It serves as a temporal cache tohelp content diffusion inside the MOVi network, and actsas a channel state monitor by periodically observing linkquality to its neighboring MCs. MCs implement the formerfunction at the user-level application running in generalLinux environments while perform the latter function atthe device driver level by modifying MadWiFi [1]. Theapplication part of MC periodically updates the observedlink quality to its neighbors. When the p2p transfer isscheduled by MS, MCs trigger iDLS [23] (inter-BSS direct-link setup) to smoothly change their WiFi device’s operationmode to transfer data directly to the neighboring MCwithout help of AP.

We conduct experiments that compares the behaviorof MOVi under the real human-driven mobility with theone under the emulated mobility. We have customizedMOVi to interact with EC. Specifically, we modified MOViimplementation [24] so that it can take a Snapshot andresume with relayed Snapshot. We install and launch MS(configured as AP) to one of the node and two MCs ontwo of nodes in ORBIT and hire two student volunteers atWinlab, Rutgers University who carry them and move alongthe predefined path over the ORBIT testbed at the speedof 1 meter/sec as depicted in Figure 10. Both departs atboth ends of a row and move toward to the other ends andhave a contact in the middle. Once they reach to the otherend, the experiment finished. One MC has downloaded thecontents completely and stored it at the local cache beforethe beginning of the experiment. Thus, MS can schedulep2p transfer as soon as they contact.

Cr is set to 30 dB which is the same as the previousexperiments in Section 4. The unit of transfer is a segmentwhich is 200 number packets, and each packet size is 1400bytes. We configure the MOVi specific parameters to restricteach p2p transfer can not be continued no more than 150msec. If there is no data reception for last 200 msec,MCs request data transfer to MS. MCs broadcast probingmessages at every 200 msec for the neighbor discovery. MCs

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regard neighbors as contacted neighbors if the link quality tothem is more than Cr and they have sensed at least one timefor last 500 msec. If there is no valid contacted neighbor, asegment transfered via the downlink path (AP→STA).

We observe the behavior of MOVi and measure especiallywhen and how many p2p transfers happen. We then regener-ate the same scenario on the same route using the proposedemulator. We measure the same set of indicators withrespect to the different granularity of emulation. Given thesetup described above, the best granularity emulation, calledEM 1, is achieved by moving MC over one-hop distanceneighbors for example [1,1]→[1,2]→[1,3]→...→[1,20] at 1 sec.We have also prepared the lower granularity emulationsEM N that migrates MC at every N-hop neighbors as[1,1]→[1,1+N ]→[1,1+2N ]→...→[1,20] at every N sec. Wenote that the following results are obtained from 20 roundsof experiments.

5.2 Mobile P2P Video Streaming:Measurement Results

Figure 11(a) depicts the average contact duration fromboth real mobility and emulation experiments. The contactduration is the time difference between the first and lastp2p transfers. For the real mobility experiments, both MCshave contacts for 7 sec out of 20 sec of experiment time. ForEM 1, the measured contact duration is less than 2 sec. Thereason is due to too fast and frequent application migrations.As depicted in Figure 11(d), the migration latency takes140 msec in average, and about 30% of migration takemore than 140 msec delay mostly due to the non-restricteddownlink segment transfer from AP. Since MC is moved tonext waypoint at every 1 sec, the expected time to run MC isabout 860 msec for each migration. From the measurement,we confirm that almost every neighbor discovery has failedin EM 1 case. For EM 2 case, the average contact durationis almost the same with real mobility case. However, the

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number of segment transfered is 15% smaller than the realmobility case as it is shown in Figure 11(b). This is alsocaused by the migration latency. Since it is impossible tomake a p2p transfer during the migration, the utilization ofcontact duration for data transfer is limited.

The average contact duration is decreasing as thegranularity decreases until EM 5. The interesting thing,however, is that the number of p2p data transfer increasedfor EM 3 and EM 4 cases. The reason is that both EM 3and EM 4 enable MCs to do p2p transfers for the entirecontact duration. For real mobility case, as seen fromFigure 11(c), the p2p transfer suffers around 5% packetloss within a segment transfer which reduces the utilizationof the contact duration. For EM 6 and EM 7 cases, weconfirm that most contacts happen between relatively long-distanced pair of nodes (4 hopes, 6 hops and rarely 8 hops).The measurements report that those long distanced contactssuffer some packet losses during p2p transfer as depicted inFigure 11(c).

For all emulations, the average successful p2p transferratio achieve more than 99% while it is around 94.7% andeven highly varying in real mobility case. The major sourceof higher packet loss for real mobility case is due to theimpact of link-retransmission. We have observed that someof packets are not recovered by the link-layer retransmission.Even though the link-layer retry secure the reliability, thedelay produced makes the p2p transfer can not be finishedwithin 150msec boundary so dropped at the p2p source. Foremulation case, those impact due to the channel variationcaused by mobility can not be regenerative because wediscretize the mobility so all p2p transfer happen to bebetween two static nodes.

6. SUMMARYThis paper proposes a novel approach to emulate mobility

of DTN and MANET applications over static-grid testbed.This method virtually emulates mobility by migratingapplications along the waypoints of their actual movementtrajectories. This approach allows realistic evaluations forthe DTN and modern MANET applications, which requirestransparent interactions to the underlying MAC/PHY-layers.

The proposed method has evaluated through extensive ex-periments conducted over two different static-grid testbedsat Rutgers university (US) and National ICT Australia.This evaluation allows us to conclude that:

• The proposed method maintains the tendency of inter-contact time distribution from the input mobility tracefor both spatial and SINR-based contact evaluations.The tendency of contact time distribution is hardlyregenerative in the SINR-based contact evaluation (seeSection 4.3.4).

• The channel quality variation due to the devicemobility can not be properly emulated by the proposedapproach (see Section 5.2).

• There is a tradeoff between the granularity of themobility emulation and the realism of the obtainedresult. Higher the granularity produces more realisticresults. However, emulations with too high granularitymay disturb to operate tested applications due tofrequent migrations (see Section 5.2).

As a part of our future work, we are planning toemulate various mobility models and patterns over largerscale grid testbed configuration (20x20) with different typesof applications. We will also design and evaluate moreefficient approximation algorithm to reduce the gap betweenemulation and real-world environments.

AcknowledgmentThis research was sponsored by GIST Technology Initiative.We appreciate Murium and Yuriy from WINLAB for helpingus to do mobile experiments. We also thanks to anonymousreviewers for their valuable comments.

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