River Publishers Series in Signal, Image and Speech Processing I B n Building Blocks for IoT Analytics t u e i rn ld Internet-of-Things Analytics e in t - g Building Blocks for IoT Analytics o f B - T l John Soldatos (Editor) o h Internet-of-Things Analytics i c n k g s s Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, f A o as they provide the means to extract knowledge, drive actuation services and n r a I optimize decision making. IoT analytics will be a major contributor to IoT business ly o John Soldatos (Editor) value in the coming years, as it will enable organizations to process and fully tic T A leverage large amounts of IoT data, which are nowadays largely underutilized. s   n   Building Blocks for IoT Analytics is devoted to the presentation of the main   a l technology building blocks that comprise advanced IoT analytics systems. It y t introduces IoT analytics as a special case of BigData analytics and accordingly ic s presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the roles of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with main building blocks for IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these building blocks in the scope of pragmatic contexts. Technical topics discussed in the book include: J o h •  Cloud Computing and BigData for IoT analytics n •  Searching the Internet of Things S o •  Development Tools for IoT Analytics Applications l d •  IoT Analytics-as-a-Service a •  Semantic Modelling and Reasoning for IoT Analytics t o •  IoT analytics for Smart Buildings s     •  IoT analytics for Smart Cities   •  Operationalization of IoT analytics •  Ethical aspects of IoT analytics Building Blocks for IoT Analytics Internet-of-Things Analytics RIVER PUBLISHERS SERIES IN SIGNAL, IMAGE AND SPEECH PROCESSING SeriesEditors MONCEFGABBOUJ THANOSSTOURAITIS TampereUniversityofTechnology UniversityofPatras Finland Greece The “River Publishers Series in Signal, Image and Speech Processing” is a series of comprehensive academic and professional books which focus on all aspects of the theory and practice of signal processing. Books published in the series include research monographs, edited volumes, handbooks and textbooks. The books provide professionals, researchers, educators, and advancedstudentsinthefieldwithaninvaluableinsightintothelatestresearch anddevelopments. Topicscoveredintheseriesinclude,butarebynomeansrestrictedtothe following: • SignalProcessingSystems • DigitalSignalProcessing • ImageProcessing • SignalTheory • StochasticProcesses • DetectionandEstimation • PatternRecognition • OpticalSignalProcessing • Multi-dimensionalSignalProcessing • CommunicationSignalProcessing • BiomedicalSignalProcessing • AcousticandVibrationSignalProcessing • DataProcessing • RemoteSensing • SignalProcessingTechnology • SpeechProcessing • RadarSignalProcessing Foralistofotherbooksinthisseries,visitwww.riverpublishers.com Building Blocks for IoT Analytics Internet-of-Things Analytics Editor John Soldatos AthensInformationTechnology Greece Published,soldanddistributedby: RiverPublishers Alsbjergvej10 9260Gistrup Denmark RiverPublishers LangeGeer44 2611PWDelft TheNetherlands Tel.:+45369953197 www.riverpublishers.com ISBN:978-87-93519-03-9(Hardback) 978-87-93519-04-6(Ebook) ©2017RiverPublishers Allrightsreserved.Nopartofthispublicationmaybereproduced,storedin aretrievalsystem,ortransmittedinanyformorbyanymeans,mechanical, photocopying,recordingorotherwise,withoutpriorwrittenpermissionof thepublishers. Contents Preface xiii ListofContributors xix ListofFigures xxi ListofTables xxv ListofAbbreviations xxvii PARTI: IoTAnalyticsEnablers 1 IntroducingIoTAnalytics 3 JohnSoldatos 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 IoTDataandBigData . . . . . . . . . . . . . . . . . . . . . 3 1.3 ChallengesofIoTAnalyticsApplications . . . . . . . . . . 5 1.4 IoTAnalyticsLifecycleandTechniques . . . . . . . . . . . 7 1.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 10 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 IoT,CloudandBigDataIntegrationforIoTAnalytics 11 AbdurRahimBiswas,CorentinDupontandCongducPham 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Cloud-basedIoTPlatform . . . . . . . . . . . . . . . . . . 12 2.2.1 IaaS,PaaSandSaaSParadigms . . . . . . . . . . . 12 2.2.2 RequirementsofIoTBigDataAnalytics Platform . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 FunctionalArchitecture . . . . . . . . . . . . . . . . 15 2.3 DataAnalyticsfortheIoT . . . . . . . . . . . . . . . . . . 15 v vi Contents 2.3.1 CharacteristicsofIoTGeneratedData . . . . . . . . 15 2.3.2 DataAnalyticTechniquesandTechnologies . . . . . 17 2.4 DataCollectionUsingLow-power,Long-rangeRadios . . . 20 2.4.1 ArchitectureandDeployment . . . . . . . . . . . . 20 2.4.2 Low-costLoRaImplementation . . . . . . . . . . . 21 2.5 WAZIUPSoftwarePlatform . . . . . . . . . . . . . . . . . 23 2.5.1 MainChallenges . . . . . . . . . . . . . . . . . . . 23 2.5.2 PaaSforIoT . . . . . . . . . . . . . . . . . . . . . 24 2.5.3 Architecture . . . . . . . . . . . . . . . . . . . . . 25 2.5.4 Deployment . . . . . . . . . . . . . . . . . . . . . . 26 2.6 iKaaSSoftwarePlatform . . . . . . . . . . . . . . . . . . . 27 2.6.1 ServiceOrchestrationandResources Provisioning . . . . . . . . . . . . . . . . . . . . . 30 2.6.2 AdvancedDataProcessingandAnalytics . . . . . . 30 2.6.3 ServiceCompositionandDecomposition . . . . . . 31 2.6.4 MigrationandPortabilityinMulti-cloud Environment . . . . . . . . . . . . . . . . . . . . . 33 2.6.5 CostFunctionofServiceMigration . . . . . . . . . 35 2.6.6 DynamicSelectionofDevicesinMulti-cloud Environment . . . . . . . . . . . . . . . . . . . . . 35 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . 36 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3 SearchingtheInternetofThings 39 RichardMcCreadie,DyaaAlbakour,JaranaManotumruksa, CraigMacdonaldandIadhOunis 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 ASearchArchitectureforSocialandPhysical Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.1 SearchengineforMultimediAenviRonment generatedcontenT(SMART) . . . . . . . . . . . . . 41 3.2.2 ChallengesinBuildinganIoTSearchEngine . . . . 46 3.3 LocalEventRetrieval . . . . . . . . . . . . . . . . . . . . . 48 3.3.1 SocialSensorsforLocalEventRetrieval . . . . . . 48 3.3.2 ProblemFormulation . . . . . . . . . . . . . . . . . 49 3.3.3 AFrameworkforEventRetrieval . . . . . . . . . . 51 3.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . 53 3.4 UsingSensorMetadataStreamstoIdentifyTopicsofLocal EventsintheCity . . . . . . . . . . . . . . . . . . . . . . . 54 Contents vii 3.4.1 DefinitionofEventTopicIdentification Problem . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4.2 SensorDataCollection . . . . . . . . . . . . . . . . 56 3.4.3 EventPoolingandAnnotation . . . . . . . . . . . . 57 3.4.4 LearningEventTopics . . . . . . . . . . . . . . . . 59 3.4.5 Experiments . . . . . . . . . . . . . . . . . . . . . 61 3.4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . 63 3.5 VenueRecommendation . . . . . . . . . . . . . . . . . . . 63 3.5.1 ModellingUserPreferences . . . . . . . . . . . . . 65 3.5.2 Venue-dependentEvidence . . . . . . . . . . . . . . 67 3.5.3 Context-AwareVenueRecommendations . . . . . . 70 3.5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . 72 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 74 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4 DevelopmentToolsforIoTAnalyticsApplications 81 JohnSoldatosandKaterinaRoukounaki 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.3 TheVITALArchitectureforIoTAnalyticsApplications . . . 84 4.4 VITALDevelopmentEnvironment . . . . . . . . . . . . . . 87 4.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 87 4.4.2 VITALNodes . . . . . . . . . . . . . . . . . . . . . 87 4.4.2.1 PPInodes . . . . . . . . . . . . . . . . . 88 4.4.2.2 Systemnodes . . . . . . . . . . . . . . . 88 4.4.2.3 Servicesnodes . . . . . . . . . . . . . . . 89 4.4.2.4 Sensorsnodes . . . . . . . . . . . . . . . 89 4.4.2.5 Observationsnodes . . . . . . . . . . . . 89 4.4.2.6 DMSnodes . . . . . . . . . . . . . . . . 89 4.4.2.7 Querysystems . . . . . . . . . . . . . . . 89 4.4.2.8 Queryservices . . . . . . . . . . . . . . . 89 4.4.2.9 Querysensors . . . . . . . . . . . . . . . 89 4.4.2.10 Queryobservations . . . . . . . . . . . . 90 4.4.2.11 Discoverynodes . . . . . . . . . . . . . . 90 4.4.2.12 Discoversystemsnodes . . . . . . . . . . 90 4.4.2.13 Discoverservicesnodes . . . . . . . . . . 90 4.4.2.14 Discoversensorsnodes . . . . . . . . . . 90 4.4.2.15 Filteringnodes . . . . . . . . . . . . . . . 90 viii Contents 4.4.2.16 Thresholdnodes . . . . . . . . . . . . . . 90 4.4.2.17 Resamplenodes . . . . . . . . . . . . . . 91 4.5 DevelopmentExamples . . . . . . . . . . . . . . . . . . . . 91 4.5.1 Example#1:PredicttheFootfall! . . . . . . . . . . 91 4.5.2 Example#2:FindaBike! . . . . . . . . . . . . . . 91 4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 96 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5 AnOpenSourceFrameworkforIoTAnalyticsasaService 99 JohnSoldatos,NikosKefalakisandMartinSerrano 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2 ArchitectureforIoTAnalytics-as-a-Service . . . . . . . . . 101 5.2.1 PropertiesofSensing-as-a-Service Infrastructure . . . . . . . . . . . . . . . . . . . . . 101 5.2.2 ServiceDeliveryArchitecture . . . . . . . . . . . . 102 5.2.3 ServiceDeliveryConcept . . . . . . . . . . . . . . 105 5.3 Sensing-as-a-ServiceInfrastructureAnatomy . . . . . . . . 106 5.3.1 LifecycleofaSensing-as-a-ServiceInstance . . . . 106 5.3.2 InteractionsbetweenOpenIoTModules . . . . . . . 108 5.4 Scheduling,MeteringandServiceDelivery . . . . . . . . . 112 5.4.1 Scheduler . . . . . . . . . . . . . . . . . . . . . . . 112 5.4.2 ServiceDelivery&UtilityManager . . . . . . . . . 118 5.5 Sensing-as-a-ServiceExample . . . . . . . . . . . . . . . . 122 5.5.1 DataCapturingandFlowDescription . . . . . . . . 122 5.5.2 SemanticAnnotationofSensorData . . . . . . . . . 123 5.5.3 RegisteringSensorstoLSM . . . . . . . . . . . . . 124 5.5.4 PushingDatatoLSM . . . . . . . . . . . . . . . . . 125 5.5.5 ServiceDefinitionandDeploymentUsing OpenIoTTools . . . . . . . . . . . . . . . . . . . . 126 5.5.6 VisualizingtheRequest. . . . . . . . . . . . . . . . 131 5.6 FromSensing-as-a-ServicetoIoT-Analytics-as-a-Service. . 134 5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 137 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6 AReviewofToolsforIoTSemanticsandDataStreaming Analytics 139 MartinSerranoandAmelieGyrard 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Contents ix 6.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . 141 6.2.1 LinkingData . . . . . . . . . . . . . . . . . . . . . 141 6.2.2 Real-time&LinkedStreamProcessing . . . . . . . 142 6.2.3 Logic . . . . . . . . . . . . . . . . . . . . . . . . . 142 6.2.4 MachineLearning . . . . . . . . . . . . . . . . . . 143 6.2.5 Semantic-basedDistributedReasoning . . . . . . . 145 6.2.6 Cross-DomainRecommenderSystems . . . . . . . 146 6.2.7 LimitationsofExistingWork . . . . . . . . . . . . 146 6.3 SemanticAnalytics . . . . . . . . . . . . . . . . . . . . . . 147 6.3.1 ArchitecturetowardstheLinkedOpen Reasoning . . . . . . . . . . . . . . . . . . . . . . . 148 6.3.2 TheWorkflowtoProcessIoTData . . . . . . . . . . 149 6.3.3 Sensor-basedLinkedOpenRules(S-LOR) . . . . . 152 6.4 Tools&Platforms . . . . . . . . . . . . . . . . . . . . . . . 152 6.4.1 SemanticModellingandValidationTools . . . . . . 152 6.4.2 DataReasoning . . . . . . . . . . . . . . . . . . . 154 6.5 APracticalUseCase . . . . . . . . . . . . . . . . . . . . . 156 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . 157 References. . . . . . . . . . . . . . . . . . . . . . . . . . . 158 PARTII: IoTAnalyticsApplicationsandCaseStudies 7 DataAnalyticsinSmartBuildings 167 M.VictoriaMoreno,FernandoTerroso-Sáenz, AuroraGonzález-VidalandAntonioF.Skarmeta 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 167 7.2 AddressingEnergyEfficiencyinSmartBuildings . . . . . . 169 7.3 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . 174 7.4 AProposalofGeneralArchitectureforManagement SystemsofSmartBuildings. . . . . . . . . . . . . . . . . . 179 7.4.1 DataCollectionLayer . . . . . . . . . . . . . . . . 179 7.4.2 DataProcessingLayer . . . . . . . . . . . . . . . . 180 7.4.3 ServicesLayer . . . . . . . . . . . . . . . . . . . . 181 7.5 IoT-basedInformationManagementSystemforEnergy EfficiencyinSmartBuildings . . . . . . . . . . . . . . . . . 181 7.5.1 IndoorLocalizationProblem . . . . . . . . . . . . . 185 7.5.2 BuildingEnergyConsumptionPrediction . . . . . . 190