The “machine to machine” communication paradigm will become a central element for mobile networks. This paradigm can be easily constructed by a contact-based network, notably a disruption/delay tolerant networks (DTN). To characterize a DTN, we can use the Inter-contact time among the nodes. The better understanding of inter-contact time (ICT) has practical applications on the tuning of forwarding strategies, and hence in the quality of the User Experience. Nevertheless, the fine tuning of those parameters is tight to a set of assumptions about the regularity of movement or periodicity of patterns in an usually non complete and cumbersome statistical analysis. That is why in a dynamic environment where we cannot assume any previous information the tuning of parameters is usually overestimated. In this work we study how monitoring can help to adapt those parameters to give a better understanding of both natural evolution of the network and non periodical events

Lochin, Emmanuel

Rakotoarivelo, Thierry

Ramiro, Victor

Sénac, Patrick

English

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 To cite this document: RAMIRO, Victor and SÉNAC, Patrick and LOCHIN, Emmanuel and RAKOTOARIVELO, Thierry. On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocols and the user experience. In: UXwIT 2012 - I Chilean Workshop on User eXperience with Information Technology, 12-16 Nov 2012, Valparaiso, Chile. This is an author-deposited version published in: http://oatao.univ-toulouse.fr/  Eprints ID: 6803 Any correspondence concerning this service should be sent to the repository administrator:staff-oatao@inp-toulouse.fr On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocolsand the user experienceVictor Ramiro, Emmanuel Lochin, Patrick SenacISAE, University of ToulouseToulouse, France{first.second}@isae.frThierry RakotoariveloNational ICT Australia (NICTA)Alexandria, NSW 1435, AustraliaThierry.Rakotoarivelo@nicta.com.auAbstract—The “machine to machine” communicationparadigm will become a central element for mobile networks.This paradigm can be easily constructed by a contact-basednetwork, notably a disruption/delay tolerant networks (DTN).To characterize a DTN, we can use the Inter-contact timeamong the nodes. The better understanding of inter-contacttime (ICT) has practical applications on the tuning of for-warding strategies, and hence in the quality of the UserExperience. Nevertheless, the fine tuning of those parameters istight to a set of assumptions about the regularity of movementor periodicity of patterns in an usually non complete andcumbersome statistical analysis. That is why in a dynamicenvironment where we cannot assume any previous informationthe tuning of parameters is usually overestimated. In this workwe study how monitoring can help to adapt those parametersto give a better understanding of both natural evolution of thenetwork and non periodical events.Keywords-Monitoring, Characterization, Measurement, DTNI. INTRODUCTIONThe capabilities of information processing, storage andcommunication of next generation mobile systems will notonly strengthen the ability of these systems to autonomouslyrealize tasks but also to coordinate themselves to achievecooperative activities. The “machine to machine” communi-cation paradigm thus becomes a central element for mobilenetworks. This new kind of communication paradigm offersa new way to communicate without the need of infrastruc-ture network but with the cost of frequent disconnection.These type of networks, known as disruption/delay tolerantnetworks (DTN), allow to easily exchange message-baseddata. Examples of such data can be subway newspapers,advertisement, public information, time tables or warninginformation.This kind of networks can be easily constructed just byproximity contacts. However, the difficulty of this approachis the diffusion of information. This makes the big differencewith wireless networks which need deployment of infras-tructure, but offer direct connectivity among nodes. In orderto spread information over a DTN the network connectivitymust be characterized. Previous studies [1] showed that thebest way is to assess the inter-contact law between nodes.Having this characterization allows to select both the rightalgorithm (such as epidemic routing [2], Spray and Wait [3]and PRoPHET [4]) and the right configuration parameters(for instance: vaccines, TTL, number of replicates) to diffusethe information on the network.Furthermore, these parameters need to be precisely tunedas they heavily impacts on the quality of experience ofthe user in terms of delivery ratio, memory usage, energyconsumption. The main challenge in order to improve thequality of experience is the tuning of these parameters.Today the tuning of those parameters is tight to a set ofassumptions about the regularity of movement or existenceand periodicity of patterns. That is why in a dynamic envi-ronment where we cannot assume any previous informationusually the tuning of parameters is overestimated.However, The problem of evaluating inter-contact timeamong the nodes is not trivial. Today, the common way toperform such evaluation is to drive a statistical analysis ofcollected inter-contact time data during a certain period oftime between the nodes. Usually this process is cumbersomeand not scalable with the increasing number of nodes inDTNs. Furthermore, the statistical analysis is an averagedview of the network during a given period. Hence, we needa dynamic light way to assess the inter-contact among thenodes.Monitoring over a DTN network would allow/propose afair trade-off between the complexity and the scalability ofthe characterization of the inter-contact. The natural tempo-ral sampling of monitoring provides a method to reduce thesignificant amount of data that is needed to construct theapproximate network characterization.However, in a dynamic ambient like DTNs several ques-tions arise. First we need to determine if it is possibleto select a set of representative DTN nodes. Given themobility of nodes, we need to define where to monitorand how the monitors should behave in relation with othernodes movement. We also need to assess the sampling timeduration and estimate the relative error when the samplingperiod is too short.Hence, monitoring and adapting those parameters overtime can help to address a better understanding of bothnatural evolution of the network model and non periodicalevents, providing a bigger resilience to changes.In this work, we discuss on the feasibility of performsuch monitoring based on the random waypoint movement(RWP) model. Basically, we weight up the trade-off betweenthe complete statistical analysis and the approximation givenby the monitoring model and study the impact of thischaracterization over the fine tuning of the most importantrouting protocols available on DTN.II. MODEL BASICSIn this section we present the model basics to characterizethe inter-contact time. We define some basic notation andthen explain how monitoring to create the approximatecharacterization of the inter-contact time.For each pair of nodes (i, j) we denote their k-proximitycontact as (csk, cfk), where csk, cfk are the start and end timerespectively. The inter-contact time is usually characterizedas a frequency table ICTk for each pair of nodes (i, j) as:ICTk = {(t1, f1), (t2, f2), . . . , (tnk , fnk)} (1)Where tl is the time elapsed between the end of the lastcontact cfr−1 to the start of a new contact csr between thenodes (i, j)tl ={cfr−1 − csr r > 00 ∼ (2)and fl the number of repetitions of tl over time.With all the pairs we construct the global frequencytable that leads to the empirical distribution function. Animportant remark is that the set defined by (i, j) is notnecessarily the same defined by (j, i). This is due the in-herent behavior of some wireless protocols or by measuringproblems. For the purpose of this work we assume that thepairwise distribution is the same.Hence the global inter-contact time is defined asICT = ∪kICTk (3)Where the union represents the sum of the frequency whenthe time index tl is equal. We can see that to characterizethe global ICTk we need to know all the pairwise ICTkdistributions. Due to the difficulty to gather all the pairwisedistributions, we propose to select a subset of nodes that canapproximate the global ICT distribution.Selecting the right placement of monitors in space andtime is key to approximate the global ICT distribution. Forinstance, in Figure 1 we can see a typical static picture of aDTN. Let us assume that each monitor (depicted in blue inthe figure) can connect at the same time with all the nodesat a given distance (usually the maximum distance is definedby the underlaying wireless connectivity). We can see thateach monitor define an area of monitoring as the subset ofnodes that are monitored at that time (depicted by the redFigure 1: Monitor sampling scenariocircle in the figure). We observe the following cases: (i) anode is inside the range of the monitor, (ii) a node is insidethe range of one or more monitors or (iii) a node is outsideof any monitored area. Notice that also can be the case thatmonitors are in contact are able to share information. If weadd that DTN are inherently rapid time changing, we haveto also determine how our monitors should move along thespace.III. SIMULATIONSIn order to explore and answer some questions related tothe feasibility and positive impact that monitoring can haveunder the fine tuning of DTN routing protocols, we performan evaluation of several scenario. We run simulations in thewell known The One simulator [5]. We use the randomwaypoint movement (RWP) model for both normal nodesand monitors. We setup the density of nodes to reflect a usualday scenario in a medium size city with 100 nodes/m2. Inorder to determine the impact of the number of monitorsspread among the normal nodes we define a series ofscenarios with increasing number of monitors from 1% ofthe sample to 10%. Finally, to determine the impact of thetime window used to sample we perform increasing runsfrom 1 minute to 1 hour (increasing in 1 minute each time).Our initial results indicate that the ICT probability distri-bution function is similar for every scenario (with differentparameters though). The smaller the monitoring period, thesmaller the number of contacts, which provides a coarse-grained ICT estimation from the monitors. If we increasethe number of monitors, the number of contacts increaseaccordingly, which results in an improved ICT estimation.As overall conclusion: In the random waypoint scenario, wecan monitor a group of nodes using a subset of monitors. Thekey parameter to take into account is the number of contacts,that can be regulated either increasing the time sampling orby increasing the number of nodes in the space.IV. RELATED WORKGuerrieri et al. [6] present an extension off well knownaggregation algorithms for connected networks. Specificallythe notion of pairwise averaging and populations protocolsto the DTN scenario. However this work does not providesa mean to measure the error of the estimation. Instead theestimation is just performed by a given amount of time orby a given number of contacts desired, assuming that themore contacts you have, the better the estimation will be.Noticeably, [7], [8] showed that the global inter-contacttime distribution for real human traces follows a power lawwith an exponential decay. This impacts on the possibilityto deliver a message and gives a superior bound for theexpected packet delivery.Most of the works that characterize a DTN are basedin the global estimation of inter-contact time. Passarellaet al. [9] present an analytical model which derives frompairwise inter-contact time distribution the aggregated dis-tribution of the group. Remarkably, this study shows thatthere is no exact mirror between the pairwise connectionwith the aggregated distribution. If we assume that pairwisedistributions follows an exponential law, then the aggregateddistribution will follow a power law.Pheneau et al. present a fine grained inter-contact timecharacterization in [10]. The main idea is to add an extralevel of connectivity based in favorable inter-contact by n-hops of distance. The trace analysis under this model revealsnew communication opportunities. Later, [11] present avicinity study to characterize the behavior of the DTN. Inthis work is present the concept of k-vicinity as all the nodesthat are at most at k hops from a given node and the k-inter-contact which is the time while a node left and come backto a given k-vicinity. Trace analysis shows that k-vicinitiesinter-contact time follows power laws with a exponentialdecay after a given time. Moreover, the k-vicinity of sizek={2,3} gives already enough awareness to a node of itssurrounding. This assumption is supported in the existenceof groups in the node movements.V. CONCLUSIONSIn this work we present a first study on the feasibility ofperform monitor over a DTN network and its impact in theuser experience. This impact is measured by the fine tuningthat it is possible to perform over certain routing algorithms.As future work we plan to extend our experiments withreal traces such as those available on Crawdad. We also willexplore different ways to measure the estimated error of theapproximation. Also we will try different mobility modelsfor both nodes and monitors.ACKNOWLEDGMENTSThis work is partially founded by CONICYT Chile (BecasChile PhD program)REFERENCES[1] V. Conan, J. Leguay, and T. Friedman, “Characterizingpairwise inter-contact patterns in delay tolerant networks,”in Proceedings of the 1st international conference onAutonomic computing and communication systems, ser.Autonomics ’07. ICST, Brussels, Belgium, Belgium:ICST (Institute for Computer Sciences, Social-Informaticsand Telecommunications Engineering), 2007, pp. 19:1–19:9. [Online]. Available: http://dl.acm.org/citation.cfm?id=1365562.1365588[2] A. Vahdat and D. Becker, “Epidemic Routing for PartiallyConnected Ad Hoc Networks,” 2000. [Online]. Available:http://issg.cs.duke.edu/epidemic/epidemic.pdf[3] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Sprayand wait: an efficient routing scheme for intermittentlyconnected mobile networks,” in Proceedings of the 2005ACM SIGCOMM workshop on Delay-tolerant networking,ser. WDTN ’05. New York, NY, USA: ACM, 2005,pp. 252–259. [Online]. Available: http://doi.acm.org/10.1145/1080139.1080143[4] A. Lindgren, A. Doria, and O. Schelen, “Probabilistic routingin intermittently connected networks,” in SIGMOBILE MobileComputing and Communication Review, 2004, p. 2003.[5] A. Kera¨nen, J. Ott, and T. Ka¨rkka¨inen, “The ONE Simulatorfor DTN Protocol Evaluation,” in SIMUTools ’09: Proceed-ings of the 2nd International Conference on Simulation Toolsand Techniques. New York, NY, USA: ICST, 2009.[6] A. Guerrieri, I. Carreras, F. De Pellegrini, A. Montresor, andD. Miorandi, “Distributed estimation of global parametersin delay-tolerant networks,” in World of Wireless, Mobileand Multimedia Networks Workshops, 2009. WoWMoM 2009.IEEE International Symposium on a, june 2009, pp. 1 –7.[7] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass,and J. Scott, “Impact of human mobility on the design ofopportunistic forwarding algorithms,” in INFOCOM 2006.25th IEEE International Conference on Computer Commu-nications. Proceedings, april 2006, pp. 1 –13.[8] T. Karagiannis, J.-Y. Le Boudec, and M. Vojnovi and, “Powerlaw and exponential decay of intercontact times betweenmobile devices,” Mobile Computing, IEEE Transactions on,vol. 9, no. 10, pp. 1377 –1390, oct. 2010.[9] A. Passarella and M. Conti, “Characterising aggregate inter-contact times in heterogeneous opportunistic networks,” inNETWORKING 2011, ser. Lecture Notes in Computer Sci-ence, J. Domingo-Pascual, P. Manzoni, S. Palazzo, A. Pont,and C. Scoglio, Eds. Springer Berlin / Heidelberg, 2011,vol. 6641, pp. 301–313.[10] T. Phe-Neau, M. Dias de Amorim, and V. Conan, “Fine-grained intercontact characterization in disruption-tolerantnetworks,” in IEEE Symposium on Computers and Commu-nications, June 2011.[11] ——, “Vicinity-based DTN characterization,” in Proceedingsof the third ACM international workshop on Mobile Oppor-tunistic Networks, ser. MobiOpp ’12. New York, NY, USA:ACM, 2012, pp. 37–44.

Characterising aggregate intercontact times in heterogeneous opportunistic networks,”

Characterizing pairwise inter-contact patterns in delay tolerant networks,”

Distributed estimation of global parameters in delay-tolerant networks,”

DTN characterization,”

Epidemic Routing for Partially Connected Ad Hoc Networks,”

Finegrained intercontact characterization in disruption-tolerant networks,”

Impact of human mobility on the design of opportunistic forwarding algorithms,”

Power law and exponential decay of intercontact times between mobile devices,” Mobile Computing,

Probabilistic routing in intermittently connected networks,”

Spray and wait: an efﬁcient routing scheme for intermittently connected mobile networks,”

On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocols and the user experience

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On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocols and the user experience

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