Energy Efficient Transmission Strategies for Body Sensor Networks with Energy Harvesting

身体传感器的节能传输

EnergyEf cientTransmissionStrategiesforBodySensorNetworkswith

EnergyHarvesting

AlirezaSeyedi

DepartmentofECE,

UniversityofRochester,Rochester,NYUSA

e-mail:alireza@ece.rochester.edu

BiplabSikdar

DepartmentofECSE,

RensselaerPolytechnicInstitute,Troy,NYUSA

e-mail:sikdab@rpi.edu.

Abstract—Thispaperaddressestheproblemofdevelop-ingenergyef cienttransmissionstrategiesforBodySensorNetworks(BSNs)withenergyharvestingcapabilities.Itisassumedthattwotransmissionmodesthatallowatradeoffbetweentheenergyconsumptionandpacketerrorprobabilityareavailabletothesensors.Decisionpoliciesaredevelopedtodeterminethetransmissionmodetouseatagiveninstantoftimeinordertomaximizethequalityofcoverage.TheproblemisformulatedinaMarkovDecisionProcess(MDP)frameworkandanupperboundontheperformanceofarbitrarypoliciesisdetermined.OurresultsshowthatthequalityofcoverageassociatedwiththeMDPformulationoutperformstheotherpolicies.

haveanenergyharvestingdevicethatcollectsenergyfromambientsourcessuchasvibrationandmotion,light,andheat.However,toimprovetheperformanceofenergyharvestingBSNstoalevelthatcanbewidelyadopted,progressneedstobemadebothinenergyharvestingtechniquesandcommunicationpoliciesandprotocols.HarvestingawarecommunicationtechniquesthattakeintoaccountandexploittheenergyharvestingcharacteristicsareparticularlyneededtooptimizetheoperationoftheBSN.

Thispaperusestheideaoftradingofftheenergyspentoncommunicationswiththecorrespondingrelia-bilitythusachievedfordevelopingadaptivetransmissionpoliciesthataimtomaximizethelikelihoodofsensorsdetectingandcorrectlyreportingeventsofinterest.Thesensorsareassumedtohavetheabilitytochoosefromasetofavailabletransmissionmodesfortransmittingtheirdata,witheachschemeconsumingadifferentamountofenergy.However,eachschemehasapacketerrorprobabilitythatisadecreasingfunctionoftheenergyusedontransmission.Thepoliciesdevelopedexploitthesensor’sknowledgeofitscurrentenergylevelandthestateoftheprocessesgoverningthegenerationofdataandbatteryrechargetoselecttheappropriatetransmissionmodeforagivenstateofthesystem.ThetransmissionschedulingproblemisthenformulatedasaMarkovDecisionProcess.Anupperboundontheperformanceofanyarbitrarypolicyisobtained.Therestofthepaperisorganizedasfollows.SectionIIdescribesthesystemmodel.UpperboundsontheperformanceofarbitrarycommunicationstrategiesaredevelopedinSectionIII.AMarkovDecisionProcessformulationoftheproblemispresentedinSectionIV,simulationresultsarepresentedinSectionVandSectionVIconcludesthepaper.

I.INTRODUCTION

Manyapplicationsandservicesareexpectedtosignif-icantlybene tfromthemonitoringanddatacollectionservicesthatwillbeprovidedbyBodySensorNetworks(BSNs)[13],whichconsistofanumberofnetworkedrealtimesensorsandactuatorsareplacedonorinsidethehumanbody.Theseapplicationsandservicesincludemedicalapplicationssuchasdiagnostictechniques[9],healthandstressmonitoring[7],managementofchronicdiseases[5],andpatientrehabilitation[1],aswellasnonmedicalapplicationsandservicessuchasbiometrics[3],activitymonitoringandlearning[12]andsportsand tnesstracking[4].

OnemajorhurdleforthewideadoptionoftheBSNtechnologyistheenergysupply[11].ThecurrentbatterytechnologydoesnotprovideahighenoughenergydensitytodevelopBSNnodeswithsuf cientlylonglifeandacceptablecostandformfactor.Moreover,therelativelyslowrateofprogressinthebatterytechnology(comparedtocomputingandcommunicationtechnolo-gies)doesnotpromisebatterydrivenBSNnodesinnearfuture[8].ThemostpromisingapproachtodealwiththeenergysupplyproblemforBSNsisenergyharvestingorenergyscavenging[13].Inthisapproachthenodes

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