无线传感器网络中英文对照外文翻译文献

PAGE1中英文对照外文翻译文献(文档含英文原文和中文翻译)原文：Distributedlocalizationinwirelesssensornetworks:aquantitativecomparisonABSTRACTThispaperstudiestheproblemofdeterminingthenodelocationsinad-hocsensornetworks.Wecomparethreedistributedlocalizationalgorithms(Ad-hocpositioning,Robustpositioning,andN-hopmultilateration)onasinglesimulationplatform.Thealgorithmsshareacommon,three-phasestructure:(1)determinenode–anchordistances,(2)computenodepositions,and(3)optionallyrefinethepositionsthroughaniterativeprocedure.Wepresentadetailedanalysiscomparingthevariousalternativesforeachphase,aswellasahead-to-headcomparisonofthecompletealgorithms.Themainconclusionisthatnosinglealgorithmperformsbest;whichalgorithmistobepreferreddependsontheconditions(rangeerrors,connectivity,anchorfraction,etc.).Ineachcase,however,thereissignificantroomforimprovingaccuracyand/orincreasingcoverage1INTRODUCTIONWirelesssensornetworksholdthepromiseofmanynewapplicationsintheareaofmonitoringandcontrol.Examplesincludetargettracking,intrusiondetection,wildlifehabitatmonitoring,climatecontrol,anddisastermanagement.Theunderlyingtechnologythatdrivestheemergenceofsensorapplicationsistherapiddevelopmentintheintegrationofdigitalcircuitry,whichwillbringussmall,cheap,autonomoussensornodesinthenearfuture.Newtechnologyoffersnewopportunities,butitalsointroducesnewproblems.Thisisparticularlytrueforsensornetworkswherethecapabilitiesofindividualnodesareverylimited.Hence,collaborationbetweennodesisrequired,butenergyconservationisamajorconcern,whichimpliesthatcommunicationshouldbeminimized.Theseconflictingobjectivesrequireunorthodoxsolutionsformanysituations.ArecentsurveybyAkyildizetal.discussesalonglistofopenresearchissuesthatmustbeaddressedbeforesensornetworkscanbecomewidelydeployed.Theproblemsrangefromthephysicallayer(low-powersensing,processing,andcommunicationhardware)allthewayuptotheapplicationlayer(queryanddatadisseminationprotocols).Inthispaperweaddresstheissueoflocalizationinad-hocsensornetworks.Thatis,wewanttodeterminethelocationofindividualsensornodeswithoutrelyingonexternalinfrastructure(basestations,satellites,etc.).Thelocalizationproblemhasreceivedconsiderableattentioninthepast,asmanyapplicationsneedtoknowwhereobjectsorpersonsare,andhencevariouslocationserviceshavebeencreated.Undoubtedly,theGlobalPositioningSystem(GPS)isthemostwell-knownlocationserviceinusetoday.TheapproachtakenbyGPS,however,isunsuitableforlow-cost,ad-hocsensornetworkssinceGPSisbasedonextensiveinfrastructure(i.e.,satellites).Likewisesolutionsdevelopedintheareaofroboticandubiquitouscomputingaregenerallynotapplicableforsensornetworksastheyrequiretoomuchprocessingpowerandenergy.Recentlyanumberoflocalizationsystemshavebeenproposedspecificallyforsensornetworks.Weareinterestedintrulydistributedalgorithmsthatcanbeemployedonlarge-scalead-hocsensornetworks(100+nodes).Suchalgorithmsshouldbe:•self-organizing(i.e.,donotdependonglobalinfrastructure),•robust(i.e.,betoleranttonodefailuresandrangeerrors),•energyefficient(i.e.,requirelittlecomputationand,especially,communication).Theserequirementsimmediatelyruleoutsomeoftheproposedlocalizationalgorithmsforsensornetworks.Wecarriedoutathoroughsensitivityanalysisonthreealgorithmsthatdomeettheaboverequirementstodeterminehowwelltheyperformundervariousconditions.Inparticular,westudiedtheimpactofthefollowingparameters:rangeerrors,connectivity(density),andanchorfraction.Thesealgorithmsdifferintheirpositionaccuracy,networkcoverage,inducednetworktraffic,andprocessorload.Giventhe(slightly)differentdesignobjectivesforthethreealgorithms,itisnosurprisethateachalgorithmoutperformstheothersunderaspecificsetofconditions.Undereachcondition,however,eventhebestalgorithmleavesmuchroomforimprovingaccuracyand/orincreasingcoverage.Themaincontributionsofourworkdescribedinthispaperare:•weidentifyacommon,three-phase,structureinthedistributedlocalizationalgorithms.•weidentifyagenericoptimizationapplicabletoallalgorithms.•weprovideadetailedcomparisononasingle(simulation)platform.•weshowthatthereisnoalgorithmthatperformsbest,andthatthereexistsroomforimprovementinmostcases.Section2discussestheselection,genericstructure,andoperationofthreedistributedlocalizationalgorithmsforlarge-scalead-hocsensornetworks.Thesealgorithmsarecomparedonasimulationplatform,whichisdescribedinSection3.Section4presentsintermediateresultsfortheindividualphases,whileSection5providesadetailedoverallcomparisonandanin-depthsensitivityanalysis.Finally,wegiveconclusionsinSection6.2LOCALIZATIONALGORITHMSBeforediscussingdistributedlocalizationindetail,wefirstoutlinethecontextinwhichthesealgorithmshavetooperate.Afirstconsiderationisthattherequirementforsensornetworkstobeself-organizingimpliesthatthereisnofinecontrolovertheplacementofthesensornodeswhenthenetworkisinstalled(e.g.,whennodesaredroppedfromanairplane).Consequently,weassumethatnodesarerandomlydistributedacrosstheenvironment.Forsimplicityandeaseofpresentationwelimittheenvironmentto2dimensions,butallalgorithmsarecapableofoperatingin3D.Fig.1showsanexamplenetworkwith25nodes;pairsofnodesthatcancommunicatedirectlyareconnectedbyanedge.Theconnectivityofthenodesinthenetwork(i.e.,theaveragenumberofneighbors)isanimportantparameterthathasastrongimpactontheaccuracyofmostlocalizationalgorithms(seeSections4and5).Itcanbesetinitiallybyselectingaspecificnodedensity,andinsomecasesitcanbesetdynamicallybyadjustingthetransmitpoweroftheRFradioineachnode.Insomeapplicationscenarios,nodesmaybemobile.Inthispaper,however,wefocusonstaticnetworks,wherenodesdonotmove,sincethisisalreadyachallengingconditionfordistributedlocalization.Weassumethatsomeanchornodeshaveaprioriknowledgeoftheirownpositionwithrespecttosomeglobalcoordinatesystem.Notethatanchornodeshavethesamecapabilities(processing,communication,energyconsumption,etc.)asallothersensornodeswithunknownpositions;wedonotconsiderapproachesbasedonanexternalinfrastructurewithspecializedbeaconnodes(accesspoints)asusedin,forexample,theGPS-lesslocationsystemandtheCricketlocationsystem.Ideallythefractionofanchornodesshouldbeaslowaspossibletominimizetheinstallationcosts,andoursimulationresultsshowthat,fortunately,mostalgorithmsareratherinsensitivetothenumberofanchorsinthenetwork.Thefinalelementthatdefinesthecontextofdistributedlocalizationisthecapabilitytomeasurethedistancebetweendirectlyconnectednodesinthenetwork.FromacostperspectiveitisattractivetousetheRFradioformeasuringtherangebetweennodes,forexample,byobservingthesignalstrength.Experiencehasshown,however,thatthisapproachyieldspoordistanceestimates.Muchbetterresultsareobtainedbytime-of-flightmeasurements,particularlywhenacousticandRFsignalsarecombined;accuraciesofafewpercentofthetransmissionrangearereported.Oursimulationresultsprovideinsightintotheeffectoftheaccuracyofthedistancemeasurementsonthelocalizationalgorithms.Itisimportanttorealizethatthemainthreecontextparameters(connectivity,anchorfraction,andrangeerrors)aredependent.Poorrangemeasurementscanbecompensatedforbyusingmanyanchorsand/orahighconnectivity.Thispaperprovidesinsightinthecomplexrelationbetweenconnectivity,anchorfraction,andrangeerrorsforanumberofdistributedlocalizationalgorithms.2.1GENERICAPPROACHFromtheknownlocalizationalgorithmsspecificallyproposedforsensornetworks,weselectedthethreeapproachesthatmeetthebasicrequirementsforself-organization,robustness,andenergy-efficiency:•Ad-hocpositioningbyNiculescuandNath,•N-hopmultilaterationbySavvidesetal,and•RobustpositioningbySavareseetal.Theotherapproachesoftenincludeacentralprocessingelement,relyonanexternalinfrastructure,orinducetoomuchcommunication.Thethreeselectedalgorithmsarefullydistributedanduselocalbroadcastforcommunicationwithimmediateneighbors.Thislastfeatureallowsthemtobeexecutedbeforeanymultihoproutingisinplace,hence,theycansupportefficientlocation-basedroutingschemeslikeGAF.Althoughthethreealgorithmsweredevelopedindependently,wefoundthattheyshareacommonstructure.Wewereabletoidentifythefollowinggeneric,three-phaseapproach1fordeterminingtheindividualnodepositions：1.Determinethedistancesbetweenunknownsandanchornodes.2.Deriveforeachnodeapositionfromitsanchordistances.3.Refinethenodepositionsusinginformationabouttherange(distance)to,andpositionsofneighboringnodes.Theoriginaldescriptionsofthealgorithmspresentthefirsttwophasesasasingleentity,butwefoundthatseparatingthemprovidestwoadvantages.First,weobtainabetterunderstandingofthecombinedbehaviorbystudyingintermediateresults.Second,itbecomespossibletomix-and-matchalternativesforbothphasestotailorthelocalizationalgorithmtotheexternalconditions.Therefinementphaseisoptionalandmaybeincludedtoobtainmoreaccuratelocations.Intheremainderofthissectionwewilldescribethethreephases(distance,position,andrefinement)indetail.Foreachphasewewillenumeratethealternativesasfoundintheoriginaldescriptions.Table1givesthebreakdownintophasesofthethreeapproaches.Whenapplicablewealsodiscuss(minor)adjustmentsto(partsof)theindividualalgorithmsthatwereneededtoensurecompatibilitywiththealternatives.Duringoursimulationsweobservedthatweoccasionallyoperated(partsof)thealgorithmsoutsidetheirintendedscenarios,whichdeterioratedtheirperformance.Often,smallimprovementsbroughttheirperformancebackinlinewiththealternatives.2.2PHASE:DISTENCETOANCHORSInthisphase,nodesshareinformationtocollectivelydeterminethedistancesbetweenindividualnodesandtheanchors,sothatan(initial)positioncanbecalculatedinPhase2.NoneofthePhase1alternativesengagesincomplicatedcalculations,sothisphaseiscommunicationbounded.Althoughthethreedistributedlocalizationalgorithmseachuseadifferentapproach,theyshareacommoncommunicationpattern:informationisfloodedintothenetwork,startingattheanchornodes.Anetwork-widefloodbysomeanchorAisexpensivesinceeachnodemustforwardasinformationtoits(potentially)unawareneighbors.Thisimpliesascalingproblem:floodinginformationfromallanchorstoallnodeswillbecomemuchtooexpensiveforlargenetworks,evenwithlowanchorfractions.FortunatelyagoodpositioncanbederivedinPhase2withknowledge(positionanddistance)fromalimitednumberofanchors.Thereforenodescansimplystopforwardinginformationwhenenoughanchorshavebeen‘‘located’’.ThissimpleoptimizationpresentedintheRobustpositioningapproachprovedtobehighlyeffectiveincontrollingtheamountofcommunication(seeSection5.3).Wemodifiedtheothertwoapproachestoincludeafloodlimitaswell.2.2.1SUM-DISTThesimplesolutionfordeterminingthedistancetotheanchorsissimplyaddingtherangesencounteredateachhopduringthenetworkflood.ThisistheapproachtakenbytheN-hopmultilaterationapproach,butitremainednamelessintheoriginaldescription;wenameitSum-distinthispaper.Sum-diststartsattheanchorswhichsendamessageincludingtheiridentity,position,andapathlengthsetto0.Eachreceivingnodeaddsthemeasuredrangetothepathlengthandforwards(broadcasts)themessageifthefloodlimitallowsittodoso.Anotherconstraintisthatwhenthenodehasreceivedinformationabouttheparticularanchorbefore,itisonlyallowedtoforwardthemessageifthecurrentpathlengthislessthanthepreviousone.Theendresultisthateachnodewillhavestoredthepositionandminimumpathlengthtoatleastfloodlimitanchors.2.2.2DV-HOPAdrawbackofSum-dististhatrangeerrorsaccumulatewhendistanceinformationispropagatedovermultiplehops.Thiscumulativeerrorbecomessignificantforlargenetworkswithfewanchors(longpaths)and/orpoorranginghardware.Arobustalternativeistousetopologicalinformationbycountingthenumberofhopsinsteadofsummingthe(erroneous)ranges.ThisapproachwasnamedDV-hopbyNiculescuandNath,andHop-TERRAINbySavareseetal.SincetheresultsofDV-hopwerepublishedfirstwewillusethisname.DV-hopessentiallyconsistsoftwofloodwaves.Afterthefirstwave,whichissimilartoSum-dist,nodeshaveobtainedthepositionandminimumhopcounttoatleastfloodlimitanchors.ThesecondcalibrationwaveisneededtoconverthopcountsintodistancessuchthatPhase2cancomputeaposition.Thisconversionconsistsofmultiplyingthehopcountwithanaveragehopdistance.Wheneverananchora1infersthepositionofanotheranchora2duringthefirstwave,itcomputesthedistancebetweenthem,anddividesthatbythenumberofhopstoderivetheaveragehopdistancebetweena1anda2.Whencalibrating,ananchortakesallremoteanchorsintoaccountthatitisawareof.Nodesforward(broadcast)calibrationmessagesonlyfromthefirstanchorthatcalibratesthem,whichreducesthetotalnumberofmessagesinthenetwork.2.2.3EUCLIDEANAdrawbackofDV-hopisthatitfailsforhighlyirregularnetworktopologies,wherethevarianceinactualhopdistancesisverylarge.NiculescuandNathhaveproposedanothermethod,namedEuclidean,whichisbasedonthelocalgeometryofthenodesaroundananchor.Againanchorsinitiateaflood,butforwardingthedistanceismorecomplicatedthaninthepreviouscases.Whenanodehasreceivedmessagesfromtwoneighborsthatknowtheirdistancetotheanchor,andtoeachother,itcancalculatethedistancetotheanchor.Fig.2showsanode(_Self_)thathastwoneighbors:n1andn2withdistanceestimatestoananchor.Togetherwiththeknownrangesc,d,ande,Euclideanarrivesattwopossiblevalues(r1andr2)forthedistanceofthenodetotheanchor.Niculescudescribestwomethodstodecideonwhich,ifany,distancetouse.Theneighborvotemethodcanbeappliedifthereisathirdneighbor(n3)thathasadistanceestimatetotheanchorandthatisconnectedtoeithern1orn2.Replacingn2(orn1)byn3willagainyieldapairofdistanceestimates.Thecorrectdistanceispartofbothpairs,andisselectedbyasimplevoting.Ofcourse,moreneighborscanbeincludedtomaketheselectionmoreaccurate.Thesecondselectionmethodiscalledcommonneighborandcanbeappliedifnoden3isconnectedtobothn1andn2.Basicgeometricreasoningleadstotheconclusionthattheanchorandn3areonthesameoroppositesideofthemirroringlinen1–n2,andsimilarlywhetherornotselfandn3areonthesameside.Fromthisitfollowswhetherornotselfandtheanchorlayonthesameside.TohandletheuncertaintyintroducedbyrangeerrorsNiculescuimplementsasafetymechanismthatrejectsill-formed(flat)triangles,whichcaneasilyderailtheselectionprocessby‘neighborvote’and‘commonneighbor’.Thischeckverifiesthatthesumofthetwosmallestsidesexceedsthelargestsidemultipliedbyathreshold,whichissettotwotimestherangevariance.Forexample,thetriangleSelf-n1–n2inFig.2isacceptedwhenc+d>(1+2RangeVar)*e.Notethatthesafetycheckbecomesasstrictastherangevarianceincreases.Thisleadstoalowercoverage,definedasthepercentageofnon-anchornodesforwhichapositionwasdetermined2.3PHASE:NODEPOSITIONInthesecondphasenodesdeterminetheirpositionbasedonthedistanceestimatestoanumberofanchorsprovidedbyoneofthethreePhase1alternatives(Sum-dist,DV-hop,orEuclidean).TheAd-hocpositioningandRobustpositioningapproachesuselaterationforthispurpose.N-hopmultilateration,ontheotherhand,usesamuchsimplermethod,whichwenamedMin–max.Inbothcasesthedeterminationofthenodepositionsdoesnotinvolveadditionalcommunication.2.3.1LATERATIONThemostcommonmethodforderivingapositionislateration,whichisaformoftriangulation.Fromtheestimateddistancesandknownpositionsoftheanchorswederivethefollowingsystemofequations:Theunknownpositionisdenotedby.Thesystemcanbelinedbysubtractingthelastequationfromthefirstn_1equationsReorderingthetermsgivesapropersystemoflinearequationsintheformAx=b,whereThesystemissolvedusingastandardleast-squaresapproach.Inexceptionalcasesthematrixinversecannotbecomputedandlaterationfails.Inthemajorityofthecases,however,wesucceedincomputingalocationestimate.WerunanadditionalsanitycheckbycomputingtheresiduebetweenthegivendistancesandthedistancestothelocationestimateAlargeresiduesignalsaninconsistentsetofequations;werejectthelocation^xwhenthelengthoftheresidueexceedstheradiorange.2.3.2MIN-MAXLaterationisquiteexpensiveinthenumberoffloatingpointoperationsthatisrequired.AmuchsimplermethodispresentedbySavvidesetal.aspartoftheN-hopmultilaterationapproach.Themainideaistoconstructaboundingboxforeachanchorusingitspositionanddistanceestimate,andthentodeterminetheintersectionoftheseboxes.Thepositionofthenodeissettothecenteroftheintersectionbox.Fig.3illustratestheMin–maxmethodforanodewithdistanceestimatestothreeanchors.NotethattheestimatedpositionbyMin–maxisclosetothetruepositioncomputedthroughlateration(i.e.,theintersectionofthethreecircles).Theboundingboxofanchoriscreatedbyaddingandsubtractingtheestimateddistancefromtheanchorposition:Theintersectionoftheboundingboxesiscomputedbytakingthemaximumofallcoordinateminimumsandtheminimumofallmaximums:Thefinalpositionissettotheaverageofbothcornercoordinates.Asforlateration,weonlyacceptthefinalpositioniftheresidueissmall.2.4PHASE3:REFINEMENTTheobjectiveofthethirdphaseistorefinethe(initial)nodepositionscomputedduringPhase2.Thesepositionsarenotveryaccurate,evenundergoodconditions(highconnectivity,smallrangeerrors),becausenotallavailableinformationisusedinthefirsttwophases.Inparticular,mostrangesbetweenneighboringnodesareneglectedwhenthenode–anchordistancesaredetermined.TheiterativeRefinementprocedureproposedbySavareseetal.doestakeintoaccountallinternodesranges,whennodesupdatetheirpositionsinasmallnumberofsteps.Atthebeginningofeachstepanodebroadcastsitspositionestimate,receivesthepositionsandcorrespondingrangeestimatesfromitsneighbors,andperformsthePhase2todetermineitsnewposition.Inmanycasestheconstraintsimposedbythedistancestotheneighboringlocationswillforcethenewpositiontowardsthetruepositionofthenode.When,afteranumberofiterations,thepositionupdatebecomessmall,Refinementstopsandreportsthefinalposition.Thebasiciterativerefinementprocedureoutlinedaboveprovedtobetoosimpletobeusedinpractice.Themainproblemisthaterrorspropagatequicklythroughthenetwork;asingleerrorintroducedbysomenodeneedsonlyditerationstoaffectallnodes,wheredisthenetworkdiameter.Thiseffectwascounteredby(1)clippingundeterminednodeswithnon-overlappingpathstolessthanthreeanchors,(2)filteringoutdifficultsymmetrictopologies,and(3)associatingaconfidencemetricwitheachnodeandusingtheminaweightedleast-squaressolution.Thedetails(see)arebeyondthescopeofthispaper,buttheadjustmentsconsiderablyimprovedtheperformanceoftheRefinementprocedure.Thisislargelyduetotheconfidencemetric,whichallowsfilteringofbadnodes,thusincreasingthe(average)accuracyattheexpenseofcoverage.TheN-hopmultilaterationapproachbySavvidesetal.alsoincludesaniterativerefinementprocedure,butitislesssophisticatedthantheRefinementdiscussedabove.Inparticular,theydonotuseweights,butsimplygroupnodesintoso-calledcomputationsubtrees(over-constrainedconfigurations)andenforcenodeswithinasubtreetoexecutetheirpositionrefinementinturninafixedsequencetoenhanceconvergencetoapre-specifiedtolerance.IntheremainderofthispaperwewillonlyconsiderthemoreadvancedRefinementprocedureofSavareseetal.翻译：无线传感器网络分布式定位的定量比较摘要本文研究的问题，在Ad-Hoc传感器网络确定节点位置。在同一仿真平台上比较了3种分布式定位算法。该算法都有一个共同的，用三阶段分布式定位结构体系：（1）确定未知节点到锚节点距离，（2）节点定位，（3）迭代求精。我们提出一个详细分析比较各个方案，为每一个阶段，而且是一个一对一的比较完整的算法。主要的结论是，没有一个单一的算法性能最好，哪一个算法较为可取，取决于条件（范围错误，连接，锚分数等）。在任何情况下都有有很大的空间提高准确性和或增加集中性。关键词：自组网；分布式算法；定位1简介无线传感器网络持有的许多应用在监察和控制方面。例如：目标跟踪，入侵检测，野生动物栖息地监测，气候控制，以及灾害管理。快速发展一体化的数字电路驱动了传感器的应用，这将为我们带来美小型，廉价，自治区传感器节点在不久的将来。新技术提供新的机遇，但它还介绍了一些新的问题。这一点尤为重要，真正的传感器网络中的个别节点能力是很有限的。因此它们之间需要协作节点，但节省能源是一个重大的问题，这意味着通信应尽量减少。这些目标互有冲突，对于许多的情况需要非传统的解决方案。最近的一项Akyildizetal.等人的调查显示。讨论一长串的开放式研究的问题，必须加以处理前传感器网络部署。问题的范围从物理层（低功率传感，处理和通信硬件）到应用层（查询和发布数据议定书）。在本文中，我们处理自组网传感器网络定位。也就是说，我们要确定位置的个别传感器节点，而不必依赖外部基础设施（基站，卫星等）。自组织问题已得到相当多注意，在过去，由于许多应用需要知道物体或人在哪，并因此建立各种定位服务。毫无疑问，最知名的全球定位系统是今天所使用的位置服务。由全球定位系统实施，但是它不适合低成本，自组传感器网络，原因是全球定位系统是基于广泛的基础设施（即卫星）的。同样的解决方案开发中面积机器人和普适计算一般不适用于传感器网络因为他们需要太多的处理能力和能源。近来，一些定位系统已经被建议专门为传感器网络。我们感兴趣的，真正的分布式算法可以受用于大型Ad-Hoc传感器网络。这种算法应该是：•自组织（即不依赖于全球基础设施）•强大（即容纳节点失败和范围错误）•能源效率（即需要很少计算，特别是通信）这些要求立即排除一些对于传感器网络建议的定位算法。我们进行了一次彻底的分析并且符合上述规定的3种分布式算法，以确定它们在各种不同条件下的反应。特别是，我们研究重点参数如下：范围错误，连通性（密度），及锚节点密度。这些算法不同在于它们位置精确度，网络复盖范围，诱发网络交通和处理器负荷。三种算法基于不同的设计目标，毫不奇怪的每个算法都要根据一套特定的条件实施。在每一个条件下，但是，即使是最好的算法还有很多改进的余地，精确度和连通性。我们的工作描述在这个文件是：•我们可以找出一个共同的，分三个阶段，在结构分布式定位算法•我们可以找出一个通用的优化适用所有算法•我们提供一份详细的比较单一（仿真）平台•显示：不存在算法最好的，在大多数情况下存在着改进的余地第2节论述了选择，通用结构，和运作三个分布式定位算法大规模Ad-Hoc传感器网络。这些算法比较基于同一个模拟仿真平台，它被描述在第3节，第4节介绍中间结果独立描述，而第5条规定，一份详细的总体比较，并进行深入的敏感性分析。最后，我们给出的结论在第6节。2定位算法在详细讨论分布式定位之前，我们先前概要的上下文中，这些算法已经被介绍。第一考虑到传感器网络要求和自组网没有好的控制，安装网络时（例如，当节点分别从飞机下落）。因此，我们假定节点是随机分布在整个环境。为简化和易用性陈述我们限制的环境，以2尺寸，但所有算法是能够在三维。网络有25个节点；一条线连接的两个节点可以直接通信，网络中节点的连通性是一个重要参数，具有很强的准确性的影响对于定位算法。它可以设置初步通过选择一个特定节点密度，并在一些情况下，可设定动态调整发射功率的射频无线电在每个节点。在一些应用情况下，节点可移动。在本文中，但是，我们专注于静态网络，即节点静止，因为这已经是一个具有挑战性的条件分布式定位。我们假定一些锚节点有一个及嫩的了解针对一些全球坐标系统。注这锚节点具有相同的功能（处理，通信，能源消耗，等），与其他所有传感器未知节点；我们不考虑采取各种办法的基础上外部基础设施与专门的锚节点节点（接入点）所用的，举例来说，GPS的不足定位系统和板球定位系统。最理想的小锚节点应尽可能低减少安装成本，而我们的模拟结果表明，说，幸运的是，大部分算法是相当敏感的对于网络中的锚节点。最后一项内容，它定义分布式定位是有能力来衡量在网络中它们之间的距离有直接关连的节点。从成本的角度来看，这是有吸引力的利用射频无线电测量节点之间范围，例如，通过观察信号强度。经验证明，然而这种做法产生恶劣的距离估计。许多更好的成果得到了通过飞行测量表明，尤其是当声波在与射频信号相结合；精度几个百分点的传输范围报道。我们的模拟结果提供精确的距离测量对定位算法的影响。重要的是要意识到主要依靠三个参数（连接，锚分数，和测距误差）。恶劣的范围测量利用许多锚节点或高连通测量范围能得到补偿。本文我们了解到，在分布式定位算法中连通性，锚分数，测距误差的关系。2.1通用方法从已知的对于传感器网络定位算法建议，我们选定三个办法满足网络的基本要求对于自组织性，鲁棒性，能源效率：•Ad-Hocpositioning•N-hopmultilateration•Robustpositioning其他途径通常包括一个中央处理元素，依靠外部基础设施或者导致太多的通信。三个选定的算法是完全分布和利用当地广播利用相邻节点。这最后一项功能使其对任何多跳之前执行路由已经就位，因此，他们可以支持高效率基于位置的路由计划如GAF。尽管三种算法开发独立后，我们发现他们都有一个共同的结构。我们可以找出以下通用的，三个阶段的做法1确定个别节点的职务：1、未知节点到锚节点距离的测量：决定位置节点与锚节点之间的距离2、节点定位：利用第一阶段得出的到锚节点的距离和锚节点的位置信息计算出未知节点的坐标3、迭代求精：利用邻居节点距离信息对节点位置进行求精原来描述的算法目前前两个阶段作为一个单一实体，而是我们发现，他们的分离提供了两个好处。首先，我们得到了更深入的了解该组合行为由研究的结果。第二，它才成为可能，以MIN-MAX匹配备选方案为这两个阶段定制外部条件定位算法。优化阶段是可选的可能会包含要获取更准确的位置。在余下的本节中，我们将描述这三个阶段（测距，定位和优化）详细研究。我们将枚举每个阶段备选方案为原来的说明中找到。提供分类到的阶段三种方法。如果适用我们还讨论调整每个确保所需的算法与备选方案兼容性。在我们模拟操作中观察到，我们偶尔操作（部分）的算法外有意情景，使操作结果恶化。很多时候，这些都是由小的改进带来的。2.2第1期：距离主播在这一阶段，节点的信息共享，以确定个别节点和锚节点的距离，使一个（初始）位置可以计算出来，在第2阶段。没有了第1期替代从事复杂的计算，所以这个阶段的工作是通讯界的。虽然三个分布式定位算法每使用一种不同的方法，他们都有一个共同通信模式：信息泛洪到网络，开始在主播节点。一个网络性的泛洪，网络范围内的大量通过一些标记A是昂贵，因为每个节点必须将转发A的信息到它（可能）不知道邻居节点。这意味着误差问题：泛洪信息从所有主播向所有节点将是太大昂贵对于大型网络，即使使用低锚锚点分数。幸运的是一个好的位置可以导出在阶段2与知识（测距和定位）从一个有限数量的定位标记因此节点可以简单地停止转发信息当足够的锚已位于路由表中时。这个简单的优化提出了在鲁棒定位的做法被证明是高度有效地控制数量的通信。我们修改了其他两个办法包括泛洪为好。2.2.1Sum-dist最简单的解决办法定距离到锚是将距离信息添加到洪泛的每跳通信中。这是所采取的做法的N-Hopmultilateration办法，我们将它命名为Sum-dist在这一文件中。Sum-dist开始从锚节点出发，发出一个信息，锚节点发送包含它们身份和位置信息的消息，路径初始长度设为0。每个接收到该消息的节点将测量到的距离添加到路径长度里面并且当洪泛限制允许时将其转发出去。另一个制约因素是，其中，