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1、Location-basedDataDisseminationwithHumanMobilityUsingOnlineDensityEstimationVietDucLe,HansScholtenandP.J.MHavingaHungNgoPervasiveSystems,UniversityofTwenteIDSIA,UniversityofLugano,SUPSI7522NBEnschede,TheNetherlands6928Manno-Lugano,SwitzerlandEmail:{v.d.le,hans.scholten,p.j.m.having
2、a}@utwente.nlEmail:hung@idsia.chAbstractTheemergingwaveoftechnologyinhuman-centriconopportunisticcontacts.Oracle-basedalgorithmsrequiredevicessuchassmartphones,tablets,andothersmallwearablenetworkinformationsuchascontacttimes,messageutility,sensormodulesfacilitatespervasivesystemsa
3、ndapplicationstoandrouteschedule.beeconomicallydeployedonalargescalewithhumanpartici-Inthispaper,weexploittheimpactofhumanmobilityonpation.Toexploitsuchenvironment,datagatheringanddissem-inationbasedonopportunisticcontacttimesamonghumansisamessagedeliveryinopportunisticmobilephones
4、sensornet-fundamentalrequirement.Totacklethelackofcontemporaneousworks.Inparticular,weconsideradelay-tolerantnetworkofend-to-endconnectivityinDelay-tolerantNetworks(DTNs),mosthuman-centricnodes.ConventionalDTN’sroutingprotocolscurrentalgorithmsassesstheprobabilityofthecontacttimesh
5、aveattemptedtofindtheprobabilityofmessagedeliverytograduallyconveyamessagetowardsitsdestination.Thesebasedoncontacttimes,whichindicatehowfrequentlyapaircontact-basedapproachesdonotperformwellwhenhistoricallocationsofnodeshavemixturedistribution.Inthispaper,ofdevicesisinconnection.Th
6、oughhavingshowedgoodweformulateroutingproblemsinspatialandspatiotemporalperformanceonmessagedelivery,contact-basedalgorithmsdomainsasanonlineunsupervisedlearningproblemgivendonotperformwellwheneachdevicefrequentlyappearsatlocationdata.Thekeyinsightisthatnodesfrequentlyappearingdiff
7、erentregions,asmostpeopledailydo.Thereasonisthatnearerthemessagedestinationsareregardedaspossessinghighergeographiccoordinatesofnodeshavelittleornocorrelationdeliveryprobabilityeveniftheyhavelowcontacttimes.Weshowhowtosolvetheformulatedproblemswithtwobasicalgorithms,withtheircontac
8、ttimes.Forexample,assumeth
