IoT Based Agriculture as a Cloud and Big Data Service · IoT Based Agriculture as a Cloud and Big Data Service: The Beginning of Digital India Sukhpal Singh Gill, CLOUDS Lab, School
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DOI: 10.4018/JOEUC.2017100101
Journal of Organizational and End User ComputingVolume 29 • Issue 4 • October-December 2017
IoT Based Agriculture as a Cloud and Big Data Service:The Beginning of Digital IndiaSukhpal Singh Gill, CLOUDS Lab, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
Inderveer Chana, Computer Science and Engineering Department, Thapar University, Patiala, India
Rajkumar Buyya, CLOUDS Lab, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
KEywORDSAgriculture as a Service, Autonomic Management, Big Data, Cloud Computing, Internet of Things
1. INTRODUCTION
EmergenceofICT(InformationandCommunicationTechnologies)playsanimportantroleintheagriculture sectorbyproviding services throughcomputer-basedagriculture systems (SinghandChana,2015).Buttheseagriculturesystemsarenotabletofulfilltheneedsoftoday’sgenerationduetoprocessingoflargeamountofdata,lackofimportantrequirementslikeprocessingspeed,datastoragespace,reliability,availability,scalabilityetc.andevenresourcesusedincomputer-basedagriculturesystemsarenotutilizedefficiently.Agriculture-as-a-Service(AaaS)applicationsexhibitBigdatacharacteristics.Forexample,thevolumeofagriculturedatasetcapturedbyenvironmentssuchasOpenGovernmentDataPlatformIndia(data.gov.in,2015),IndiaAgricultureandClimateDataSet(Sanghietal.),andregionallandandclimatemodellinginChina(Shangguanetal.,2012)canbeinorderof1000000recordswithsizeof3.5GB.Thedataiscominginlargedatavarietyandvolumefrombothusersintheformofimageslikedamagedcropimagesduetoweather,insectsetc.anddevicesthroughInternetofThings(IoT)sensorsandsatellites(GPSsystems)thatsendweatherrelatedimages.Asaresultofregularcapturingandcollectionofdatasets,theygrowwiththevelocity
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of80.72KB/minuteormore(data.gov.in,2015).Tosolvetheproblemofexistingagriculturesystems,thereisaneedtodevelopacloud-basedservicethatcaneasilymanagedifferenttypesofagriculturerelated-databasedondifferentdomains(crop,weather,soil,pest,fertilizer,productivity,irrigation,cattle,andequipment)throughthesesteps:i)gatherdatafromvarioussensorsthroughpreconfigureddevices,ii)classifythegathereddata(heterogeneous,highvolumeofbigdata)intovariousclassesthrough analysis, iii) store the classified information in cloud repository for future use, and iv)automaticdiagnosisoftheagriculturestatus.Aslargenumberofusersareusingagriculturesystemsoperatingonlargedatasetssimultaneously,thereisaneedofhighlyscalableandelasticdistributedcomputingenvironmentsuchascloudcomputing.Inaddition,cloud-basedautonomicinformationsystemshouldbeable to identify theQoS(QualityofService)requirementsofuserrequestandresourcesshouldbeallocatedefficientlytoexecutetheuserrequestbasedontheserequirements.
Themainaimofthispaperis todesignarchitectureofAgriculture-as-a-Service(AaaS)thatmanagesvarioustypesofagriculture-relateddatabasedondifferentdomains.Thisisrealizedthroughthefollowingobjectives:i)proposeanautonomicresourcemanagementtechniquewhichisusedtoa)gathertheinformationfromvarioususersthroughpreconfigureddevices,IoTsensors,GPS(GlobalPositioningSystem),etc.b)extract theattributes,c)analyze the informationbycreatingvariousclassesbasedontheinformationreceived,d)storetheclassifiedinformationincloudrepositoryforfutureuseande)diagnosetheagriculturestatusautomaticallyandii)performresourceallocationautomaticallyatinfrastructurelevelafteridentificationofQoSrequirementsofuserrequest.
The rest of the paper is organized as follows. Section 2 presents related work of existingagriculturessystems.ProposedarchitectureispresentedinSection3.Section4presentsAutonomicResourceManagement.Sections5describetheexperimentalsetupandpresenttheresultsofevaluation.Section6presentsconclusionsandfuturescope.
2.1. Existing Agriculture SystemsRanyaetal.(2013)presentedALSE(AgricultureLandSuitabilityEvaluator)tostudyvarioustypesof land to find theappropriate land fordifferent typesof cropsbyanalyzinggeo-environmentalfactors. ALSE used GIS (Global Information System) capabilities to evaluate land using localenvironmentconditionsthroughdigitalmapandbasedonthisinformationdecisionscanbemade.Raimoetal.(2010)proposedFMIS(FarmManagementInformationSystem)usedtofindtheprecisionagriculturerequirementsforinformationsystemsthroughweb-basedapproach.AuthoridentifiedthemanagementofGISdataisakeyrequirementofprecisionagriculture.Sorensenetal.(2010)studiedtheFMIStoanalyzedynamicneedsoffarmerstoimprovedecisionprocessesandtheircorrespondingfunctionalities.Furthertheyreportedthatidentificationofprocessusedforinitialanalysisofuserneeds ismandatory foractualdesignofFMIS.Zhao (2002)presentedananalysisofweb-basedagriculturalinformationsystemsandidentifiedvariouschallengesandissuesstillpendinginthesesystems.Duetolackofautomationinexistingagriculturesystem,thesystemistakinglongertimeandisdifficulttohandledynamicneedsofuserwhichleadstocustomerdissatisfaction.Sorensenetal.(2011)identifiedvariousfunctionalrequirementsofFMISandinformationmodelispresentedbasedontheserequirementstorefinedecisionprocesses.TheyidentifiedthatcomplexityofFMISisincreasingwithincreaseinfunctionalrequirementsandfoundthatthereisaneedofautonomicsystemtoreducecomplexity.Yuegaoetal.(2004)proposedWASS(Web-basedAgriculturalSupportSystem)andidentifiedfunctionalities(information,collaborativeworkanddecisionsupport)andcharacteristicsofWASS.Basedoncharacteristics,authorsdividedWASSintothreesubsystems:production,research-educationandmanagement.
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AlltheaboveresearchworkshavefocusedondifferentdomainsofagriculturewithdifferentQoSparameters.Noneoftheexistingagriculturesystemsconsidersself-managementofresources.Duetolackofautomationofresourcemanagement,servicesbecomeinefficientwhichfurtherleadsto customer dissatisfaction. The proposed system is a novel QoS-aware cloud based autonomicinformationsystemandconsidersvariousdomainsofagricultureand,allocatesandmanagestheresourcesautomaticallywhichisnotconsideredinotherexistingagriculturesystems.
3. AGRICULTURE-AS-A-SERVICE ARCHITECTURE
Theexistingagriculturesystemsarenotabletofulfilltheneedsoftoday’sgenerationduetolackinginimportantrequirementslikeprocessingspeed,datastoragespace,reliability,availability,scalabilityetc.Evenresourcesusedincomputerbasedagriculturesystemsarenotutilizedefficiently.Tosolvetheproblemofexistingagriculturesystems, there isaneed todevelopacloud-basedautonomicinformation system that delivers Agriculture-as-a-Service. This section presents architecture ofcloud-basedautonomicinformationsystemforagricultureservicecalledAaaSthatmanagesvarious
Table 1. Comparisons of existing agriculture systems with proposed system (AaaS)
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typesofagriculture-relateddatabasedondifferentdomains.ArchitectureofAaaSisshowninFigure1.QoSparameters(executiontimeandcost)mustbeidentifiedbeforetheallocationofresources.AaaSisthekeymechanismthatensuresthattheresourcemanagercanservelargeamountofrequestswithoutviolatingSLAtermsanddynamicallymanagestheresourcesbasedonQoSrequirementsidentifiedbyQoSmanager.TheservicesofAaaShasbeendividedintothreetypes:SaaS(SoftwareasaService),PaaS(PlatformasaService)andIaaS(InfrastructureasaService).InSaaS,auserinterfaceisdesignedinwhichuserscaninteractwithsystem.Anekaisa.NET-basedapplicationdevelopmentPaaS,whichisusedasascalablecloudmiddlewaretomakeinteractionbetweencloudsubsystem and user subsystem. In IaaS, an autonomic resource manager manages the resourceautomaticallybasedontheidentifiedQoSrequirementsofaparticularrequest.ThearchitectureofAaaScomprisesoftwosubsystems:i)userandii)cloud.
3.1. User SubsystemThissubsystemprovidesauserinterface,inwhichdifferenttypeofusersinteractwithAaaStoprovideandgetusefulinformationaboutagriculturebasedondifferentdomains.Ninetypesofinformationofdifferentdomainsinagriculturehasbeenconsidered:crop,weather,soil,pest,fertilizer,productivity,irrigation, cattle, andequipment.Usersarebasicallyclassified in threecategories: i) agricultureexpert,ii)agricultureofficer,andiii)farmer.TheagricultureexpertsharesprofessionalknowledgebyansweringfarmerqueriesandupdatestheAaaSdatabasebasedonthelatestresearchdoneinthefieldofagriculturewithrespecttotheirdomain.Agricultureofficersarethegovernmentofficialsthatprovidethelatestinformationaboutnewagriculturepolicies,schemes,andrulespassedbythegovernment.FarmerisanimportantentityofAaaSwhocantakemaximumadvantagebyaskinghisqueriesandgettingautomaticreplyafteranalysis.Userscanmonitoranydatarelatedtotheirdomainandgettheirresponsewithoutvisitingtheagriculturehelpcenter.ItintegratesthedifferentdomainsofagriculturewithAaaS.Thequeriesreceivedfromuser(s)areforwardedtocloudrepositoryforupdatesandresponsesendsbacktoparticularuserontheirpreconfigureddevices(tablets,mobilephones,laptopsetc.)viainternet.
3.2. Cloud SubsystemThis subsystemcontains theplatform inwhich agriculture service is hostedon a cloud.Detailsabout users and agriculture information are stored in a cloud repository in different classes fordifferentdomainswithuniqueidentificationnumber.Theinformationismonitored,analyzed,andprocessedcontinuouslybyAaaS.Theanalysisprocessconsistsofvarioussubprocesses:selection,datapreprocessing,transformation,classificationandinterpretationasshowninFigure1.Differentclassesforeverydomainandsubclassesforfurthercategorizationofinformationhavebeendesigned.Instoragerepository,userdataiscategorizedbasedondifferentpredefinedclassesofeverydomain.Thisinformationisfurtherforwardedtoagricultureexpertsandagricultureofficersforfinalvalidationthroughpreconfigureddevices.Further,anumberofuserscanusecloud-basedagricultureservicesotheQoSmanagerandautonomicresourcemanagerincloudsubsystemhavebeenintegrated.QoSmanageridentifiestheQoSrequirementsbasedonthenumberandtypeofuserqueriesasdiscussedinpreviousresearchwork(Jeongetal.,2013;SinghandChana,2015;Singhetal.,2015).BasedonQoSrequirements,autonomicresourcemanageridentifiesresourcerequirementsautomaticallyandallocatesandexecutestheresourcesatinfrastructurelevel.Performancemonitorisusedtoverifytheperformanceofsystemandalsomaintainitautomatically.Ifthesystemwillnotbeabletohandletherequestautomaticallythenthesystemgeneratesanalert.
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3.2.2. Detailed MethodologyAaaSallowsuserstouploadthedatarelatedtodifferentdomainsofagriculturethroughpreconfigureddevicesandclassifiedthembasedonthedomainsspecifiedindatabase.SubtasksofinformationgatheringandprovidedinAaaSare:i)selection,ii)preprocessing,iii)transformation,iv)classificationandv)interpretation.Inselection,targetdatasetsarecreatedbasedontherelevantinformationthatwillfurtherbeconsideredforanalysisinnextsubprocess.Inpreprocessing,differentusershavedifferentinformationregardingagriculture.Todevelopafinaltrainingset,thereisneedofpreprocessingstepsbecausedatamightcontainsomemissingsampleornoisecomponents.InAaaS,datapreprocessingcontainsfourdifferentsubprocesses:i)datacleaning,ii)dataintegration,iii)dataconversionandiv) data reduction. Data transformation provides an interface between data analysis subprocess(classification)anddatapreprocessing.Afterdatapreprocessing,thisprocessconvertsthelabeleddataintoadequateformatsuitableforclassification.Inclassification,AaaSclassifytheagricultureinformationofdifferentusersofdifferentdomainsbasedontheextracteddata.K-NN(k-NearestNeighbor)classificationmechanismhasbeenused in this researchwork to identify thedifferentclasslabelsofusers.K-NNissupervisedmachinelearningtechniquewhichisusedtoclassifytheunknowndatausingtrainingdatasetgeneratedbyit.K-NNusedtoidentifytheproductivitylevelthroughTrainingInstanceDataset(TID).Figure3describestheK-NNAlgorithm.
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3.2.3.1. QoS ManagerUsersubmitsarequesttoAgri-Infotoretrievesomespecificagriculturerelatedinformation.Agri-InfoidentifiestheQoSparametersrequiredtoprocesstheuserrequestthroughanalysisbasedonuserrequest.BasedonthekeyQoSrequirementsofaparticularuserrequest,theQoS Managerputstheuserrequestintocriticalandnon-criticalqueuesthroughQoSassessment.ForQoSassessment,QoS Managerwillcalculatetheexecutiontimeofuserrequestandfindtheapproximateuserrequestcompletion time. If the completion time is lesser than the desired deadline then it will executeimmediatelywiththeavailableresourcesandreleasetheresource(s)backtoresourcemanagerforanother execution otherwise calculate extra number of resources required and provide from thereservedstockforcurrentexecution.3.2.3.2. Resource ManagerFurther,tworesourceschedulingpolicies(SinghandChana,2015)areusedtoscheduletheresourcesforexecutionofuserqueries:timebasedandcostbasedschedulingpolicy.Time based scheduling policyworksasperfollowing:First,theallocationagentbeginstocomputetheDeadlineTimeoftheuserrequestinthegivenbudget.Allocateresourcesbasedontime,theuserrequestwhichhasshortestDeadlineTimewillexecutefirst.Ifthetworequestshavesamedeadlinetimethenthatrequestwillexecutefirstthathaslesserexecutiontime.TheallocationagentthenschedulesalltherequestswithsmallestexecutiontimerequesttotheresourcesthatprovidehighQoS.TherulesfortimebasedschedulingpolicyaredescribedinTable5alongwiththeirconditions.
Cost based scheduling policyworksasperfollowing:First,theallocationagentbeginstocomputethecostofeachrequestthensort,asthepriorityisgiventotherequestwhichhasmaximumbudget.Ifthetworequestshavesamebudgetthenthatrequestwillexecutefirstthathaslesserexecutiontime.TheallocationagentthenschedulesalltherequestswithhighbudgetrequesttotheresourcesthatprovidehighQoS.Finally,allotherrequestsarescheduledontheavailableresourcesset.TherulesforcostbasedschedulingpolicyaredescribedinTable6alongwiththeirconditions.
4.1. SensorsSensorsgettheinformationaboutperformanceofothernodesusinginthesystemandtheircurrentstate.Firstly,theupdatedinformationfromprocessingnodesistransfertomanagernodethenmanagernode transfers this information to sensors.Updated information includes informationaboutQoSparameters(executiontime,executioncostandresourceutilizationetc.).
4.2. MonitorInitially, Monitors are used to collect the information from sensors for monitoring continuouslyperformancevariationsbycomparingexpectedandactualperformance,andmonitorsthevalueof
Table 6. Rules of cost based resource scheduling
Request Pending RA > 0 Et > Wd BA > Pr Status
Yes True True True AddResource
Yes False True True AddResource
No - - - Finish
Yes True False True Finish
Yes True True False Finish
RA
= Resource Available, Et
= Estimated Time, Pr
= Resource Price, Wd
= Desired Deadline and BA
= Available Budget. Details of both time and cost based scheduling policy is given in previous research work (Singh and Chana, 2015).
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4.3. Analysis and PlanAnalyzeandplanmodulestartanalyzingtheinformationreceivedfrommonitoringmoduleandmakeaplanforadequateactionsforcorrespondingalert.FollowingformulaisusedtocalculateResourceConsumption(Equation1):
of ResourceConsumption . ismorethan1generallybecause Actual Resource Usage ismorethan Predicted Resource Usage butideallyitwillbe1whenbothareequal.Inthisresearchrk,maximum values for ResourceConsumption has been fixed and that is called threshold value.Followingformulaisusedtocalculatenumberofrequestsmissed Requests
Missed( ) inaparticularperiodoftime(Equation2):
RequestsMissed
= [Number of Requests Executed Successfully – Number of Requests Missed Deadline] (2)
Theaimofthisperformanceevaluationistodemonstratethatitisfeasibletoimplementanddeploytheagricultureasaserviceonrealcloudresources.ToolsusedforsettingupcloudenvironmentforperformanceanalysisareMicrosoftVisualStudio2010(SaaS),Aneka(PaaS),SQLServer2008,andCitrixXenServer(IaaS).Anekahasbeeninstalledalongwithitsrequirementsonallthenodesthatprovidecloudservice.Nodesinthissystemcanbeaddedorremovedbasedontherequirement.AaaSisinstalledonmainserverandtestedonvirtualcloudenvironmentthathasbeenestablishedatCLOUDS Lab, University of Melbourne, Australia.Differentnumberofvirtualmachineshavebeeninstalledondifferentservers,anddeployedtheAaaStomeasurethevariations.Inthisexperimentalsetup,threedifferentcloudplatformsareused:SoftwareasaService(SaaS),PlatformasaService(PaaS)andInfrastructureasaService(IaaS)asshowninFigure5.
5.1. DatasetsDatasets used in this research work are downloaded from the Open Government Data PlatformIndia(data.gov.in,2015),IndiaAgricultureandClimateDataSet(Sanghietal.),andregionallandandclimatemodellinginChina(Sanghietal.)canbeintheorderof1000000records,withsizeof3.5GB.Thedataiscominginlargedatavarietyandvolumefrombothusersintheformofimageslikedamagedcropimagesduetoweather,insectsetc.anddevicesthroughInternetofThings(IoT)sensorsandsatellites(GPSsystems)thatsendweatherrelatedimages.Asaresultofregularcapturingandcollectionofdatasets,theygrowwiththevelocityof80.72KB/minuteormore(Sanghietal.).FivedifferenttablesusedtoprocessthedifferenttypesofdataasdescribedinTable8toTable12.
Table 7. Configuration Details of Cloud Environment
Resource_Id Configuration Specifications Operating System
Number of Virtual Node
Number of ECs
Price (C$/EC Time
Unit)
R1 IntelCore2Duo-2.4GHz
1GBRAMand160GBHDD Windows 6 18 2
R2 IntelCorei5-2310-2.9GHz
1GBRAMand160GBHDD Linux 4 12 3
R3 IntelXEONE52407-2.2GHz
2GBRAMand320GBHDD Linux 2 6 4
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AsshowninFigure8,theexecutiontimeisincreasingwithincreaseinnumberofuserrequests.At 90 user requests, execution time in QoS-aware autonomic resource management techniqueis 24.66% lesser than non-autonomic resource management technique. After 120 user requests,executiontimeincreasesabruptlyinnon-autonomicresourcemanagementtechniquebutQoS-awareautonomicperformsbetter thannon-autonomic technique.Averageexecution time inQoS-awareautonomicis18.960%lesserthannon-autonomicresourcemanagementtechnique.Withincreasingthenumberofuserrequests,thepercentageofresourceutilizationisincreasing.ThepercentageofresourceutilizationinQoS-awareautonomicresourcemanagementtechniqueismoreascomparedtonon-autonomicresourcemanagement(non-autonomic)atdifferentnumberofuserrequestsasshowninFigure9.Themaximumpercentageofresourceutilizationis94.66%at180userrequestsinQoS-awareautonomicbutQoS-awareautonomicperformsbetterthannon-autonomictechnique.AverageresourceutilizationinQoS-awareautonomicis31.96%morethannon-autonomicresourcemanagementtechnique.
Scalability ismeasured in termsof throughput.Numberof software,networkandhardwarefaults(faultpercentage)hasbeeninjectedtoverifythethroughputoftheproposedsystemwith100userrequests.Figure10showsthecomparisonofthroughputofbothQoS-awareautonomicresourcemanagementapproachandnon-QoSbasedresourcemanagementtechnique(non-autonomic)at100userrequestsanditisclearlyshownthatQoS-awareautonomicperformsbetterthannon-autonomic.Inthisexperiment,ithasbeenfoundthemaximumvalueofthroughputatfaultpercentage45%i.e.QoS-awareautonomichas26%morethroughputthannon-autonomic.Detectionrateincreaseswithrespecttotimeanditconsidersthenumberofblockedanddetectedattacks.Fornewattackorintrusiondetection,databaseisupdatedwithnewsignaturesandnewpolicesandrulesaregeneratedtoavoid
Figure 7. Effect of change in number of user requests on execution cost
Oneoftheauthors,Dr.SukhpalSinghGill[PostDoctorateFellow],gratefullyacknowledgestheCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne,Australia,forawardinghimtheFellowshiptocarryoutthisresearchwork.
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Sukhpal Singh Gill joined Computer Science and Engineering Department of Thapar University, Patiala, India, in 2016 as a Faculty. Presently, Dr. Gill is working as Post Doctorate Fellow at CLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia. Dr. Gill obtained the Degree of Master of Engineering in Software Engineering from Thapar University, as well as a Doctoral Degree specialization in “Autonomic Cloud Computing” from Thapar University. Dr. Gill received the Gold Medal in Master of Engineering in Software Engineering. Dr. Gill is a DST Inspire Fellow [2013-2016] and worked as a SRF-Professional on DST Project, Government of India. He has done certifications in Cloud Computing Fundamentals, including Introduction to Cloud Computing and Aneka Platform (US Patented) by ManjraSoft Pty Ltd, Australia and Certification of Rational Software Architect (RSA) by IBM India. His research interests include Software Engineering, Cloud Computing, Internet of Things and Fog Computing. He has more than 40 research publications in reputed journals and conferences.
Inderveer Chana joined Computer Science and Engineering Department of Thapar University, Patiala, India, in 1997 as Lecturer and is presently serving as Professor in the department. She is Ph.D. in Computer Science with specialization in Grid Computing, M.E. in Software Engineering from Thapar University and B.E. in Computer Science and Engineering. Her research interests include Grid and Cloud computing and other areas of interest are Software Engineering and Software Project Management. She has more than 100 research publications in reputed Journals and Conferences. Under her supervision, more than 40 ME thesis and seven Ph.D thesis have been awarded and five Ph.D. thesis are on-going. She is also working on various research projects funded by Government of India.
Rajkumar Buyya is a Fellow of IEEE, Professor of Computer Science and Software Engineering and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercialising its innovations in Cloud Computing. He has authored over 500 publications and four text books. He is one of the highly cited authors in computer science and software engineering worldwide (h-index 110+, 60000+ citations). He has served as the founding Editor-in-Chief (EiC) of IEEE Transactions on Cloud Computing and now serving as Co-EiC of Journal of Software: Practice and Experience.