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050  4 QA76.9.B45.B855 2017 
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082 04 004.678 
100 1  Soldatos, John. 
245 14 The Building Blocks of IoT Analytics :|bInternet-Of-Things
       Analytics. 
264  1 Aalborg :|bRiver Publishers,|c2016. 
264  4 |c©2017 
300    1 online resource (294 pages). 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  River Publishers Series in Signal, Image and Speech 
       Processing Ser. 
505 0  Front Cover -- Half Title Page -- RIVER PUBLISHERS SERIES 
       IN SIGNAL, IMAGE AND SPEECH PROCESSING -- Full Title Page 
       -- Building Blocks for IoT AnalyticsI nternet-of-Things 
       Analytics -- Copyright Page -- Contents -- Preface -- List
       of Contributors -- List of Figures -- List of Tables -- 
       List of Abbreviations -- PART I IoT Analytics Enablers -- 
       Chapter 1 -- Introducing IoT Analytics -- 1.1 Introduction
       -- 1.2 IoT Data and BigData -- 1.3 Challenges of IoT 
       Analytics Applications -- 1.4 IoT Analytics Lifecycle and 
       Techniques -- 1.5 Conclusions -- References -- Chapter 2 -
       - IoT, Cloud and BigData Integration for IoT Analytics -- 
       2.1 Introduction -- 2.2 Cloud-based IoT Platform -- 2.2.1 
       IaaS, PaaS and SaaS Paradigms -- 2.2.2 Requirements of IoT
       BigData Analytics Platform -- 2.2.3 Functional 
       Architecture -- 2.3 Data Analytics for the IoT -- 2.3.1 
       Characteristics of IoT Generated Data -- 2.3.2 Data 
       Analytic Techniques and Technologies -- 2.4 Data 
       Collection Using Low-power, Long-range Radios -- 2.4.1 
       Architecture and Deployment -- 2.4.2 Low-cost LoRa 
       Implementation -- 2.5 WAZIUP Software Platform -- 2.5.1 
       Main Challenges -- 2.5.2 PaaS for IoT -- 2.5.3 
       Architecture -- 2.5.4 Deployment -- 2.6 iKaaS Software 
       Platform -- 2.6.1 Service Orchestration and Resources 
       Provisioning -- 2.6.2 Advanced Data Processing and 
       Analytics -- 2.6.3 Service Composition and Decomposition -
       - 2.6.4 Migration and Portability in Multi-cloud 
       Environment -- 2.6.5 Cost Function of Service Migration --
       2.6.6 Dynamic Selection of Devices in Multi-cloud 
       Environment -- Acknowledgement -- References -- Chapter 3 
       -- Searching the Internet of Things -- 3.1 Introduction --
       3.2 A Search Architecture for Social and Physical Sensors 
       -- 3.2.1 Search engine for MultimediA enviRonment 
       generated contenT (SMART) -- 3.2.2 Challenges in Building 
       an IoT Search Engine -- 3.3 Local Event Retrieval. 
505 8  3.3.1 Social Sensors for Local Event Retrieval -- 3.3.2 
       Problem Formulation -- 3.3.3 A Framework for Event 
       Retrieval -- 3.3.4 Summary -- 3.4 Using Sensor Metadata 
       Streams to Identify Topics of Local Events in the City -- 
       3.4.1 Definition of Event Topic Identification Problem -- 
       3.4.2 Sensor Data Collection -- 3.4.3 Event Pooling and 
       Annotation -- 3.4.4 Learning Event Topics -- 3.4.5 
       Experiments -- 3.4.6 Summary -- 3.5 Venue Recommendation -
       - 3.5.1 Modelling User Preferences -- 3.5.2 Venue-
       dependent Evidence -- 3.5.3 Context-Aware Venue 
       Recommendations -- 3.5.4 Summary -- 3.6 Conclusions -- 
       Acknowledgements -- References -- Chapter 4 -- Development
       Tools for IoT Analytics Applications -- 4.1 Introduction -
       - 4.2 RelatedWork -- 4.3 The VITAL Architecture for IoT 
       Analytics Applications -- 4.4 VITAL Development 
       Environment -- 4.4.1 Overview -- 4.4.2 VITAL Nodes -- 
       4.4.2.1 PPI nodes -- 4.4.2.2 System nodes -- 4.4.2.3 
       Services nodes -- 4.4.2.4 Sensors nodes -- 4.4.2.5 
       Observations nodes -- 4.4.2.6 DMS nodes -- 4.4.2.7 Query 
       systems -- 4.4.2.8 Query services -- 4.4.2.9 Query sensors
       -- 4.4.2.10 Query observations -- 4.4.2.11 Discovery nodes
       -- 4.4.2.12 Discover systems nodes -- 4.4.2.13 Discover 
       services nodes -- 4.4.2.14 Discover sensors nodes -- 
       4.4.2.15 Filtering nodes -- 4.4.2.16 Threshold nodes -- 
       4.4.2.17 Resample nodes -- 4.5 Development Examples -- 
       4.5.1 Example #1: Predict the Footfall! -- 4.5.2 Example 
       #2: Find a Bike! -- 4.6 Conclusions -- Acknowledgements --
       References -- Chapter 5 -- An Open Source Framework for 
       IoT Analytics as a Service -- 5.1 Introduction -- 5.2 
       Architecture for IoT Analytics-as-a-Service -- 5.2.1 
       Properties of Sensing-as-a-Service Infrastructure -- 5.2.2
       Service Delivery Architecture -- 5.2.3 Service Delivery 
       Concept -- 5.3 Sensing-as-a-Service Infrastructure Anatomy
       -- 5.3.1 Lifecycle of a Sensing-as-a-Service Instance. 
505 8  5.3.2 Interactions between OpenIoT Modules -- 5.4 
       Scheduling, Metering and Service Delivery -- 5.4.1 
       Scheduler -- 5.4.2 Service Delivery & Utility Manager -- 
       5.5 Sensing-as-a-Service Example -- 5.5.1 Data Capturing 
       and Flow Description -- 5.5.2 Semantic Annotation of 
       Sensor Data -- 5.5.3 Registering Sensors to LSM -- 5.5.4 
       Pushing Data to LSM -- 5.5.5 Service Definition and 
       Deployment Using OpenIoT Tools -- 5.5.6 Visualizing the 
       Request -- 5.6 From Sensing-as-a-Service to IoT-Analytics 
       as-a-Service -- 5.7 Conclusions -- Acknowledgements -- 
       References -- Chapter 6 -- A Review of Tools for IoT 
       Semantics and Data Streaming Analytics -- 6.1 Introduction
       -- 6.2 RelatedWork -- 6.2.1 Linking Data -- 6.2.2 Real-
       time & Linked Stream Processing -- 6.2.3 Logic -- 6.2.4 
       Machine Learning -- 6.2.5 Semantic-based Distributed 
       Reasoning -- 6.2.6 Cross-Domain Recommender Systems -- 
       6.2.7 Limitations of ExistingWork -- 6.3 Semantic 
       Analytics -- 6.3.1 Architecture towards the Linked Open 
       Reasoning -- 6.3.2 TheWorkflow to Process IoT Data -- 
       6.3.3 Sensor-based Linked Open Rules (S-LOR) -- 6.4 Tools 
       & Platforms -- 6.4.1 Semantic Modeling and Validation 
       Tools -- 6.4.2 Data Reasoning -- 6.5 A Practical Use Case 
       -- 6.6 Conclusions -- Acknowledgement -- References -- 
       PART II IoT Analytics Applications and Case Studies -- 
       Chapter 7 -- Data Analytics in Smart Buildings -- 7.1 
       Introduction -- 7.2 Addressing Energy Efficiency in Smart 
       Buildings -- 7.3 RelatedWork -- 7.4 A Proposal of General 
       Architecture for Management Systems of Smart Buildings -- 
       7.4.1 Data Collection Layer -- 7.4.2 Data Processing Layer
       -- 7.4.3 Services Layer -- 7.5 IoT-based Information 
       Management System for Energy Efficiency in Smart Buildings
       -- 7.5.1 Indoor Localization Problem -- 7.5.2 Building 
       Energy Consumption Prediction -- 7.5.3 Optimization 
       Problem -- 7.5.4 User Involvement in the System Operation.
505 8  7.6 Evaluation and Results -- 7.6.1 Scenario of 
       Experimentation -- 7.6.2 Evaluation and Indoor 
       Localization Mechanism -- 7.6.3 Evaluation. Energy 
       Consumption Prediction and Optimization -- 7.6.4 
       Evaluation. User Involvement -- 7.7 Conclusions and 
       FutureWork -- Acknowledgments -- References -- Chapter 8 -
       - Internet-of-Things Analytics for Smart Cities -- 8.1 
       Introduction -- 8.2 Cloud-based IoT Analytics -- 8.2.1 
       State of the Art -- 8.3 Cloud-based City Platform -- 8.3.1
       Use Case of Cloud-based Data Analytics -- 8.4 New 
       Challenges towards Edge-based Solutions -- 8.5 Edge-based 
       IoT Analytics -- 8.5.1 State of the Art -- 8.5.2 Edge-
       based City Platform -- 8.5.3 Workflow -- 8.5.4 Task and 
       Topology -- 8.5.5 IoT-friendly Interfaces -- 8.6 Use Case 
       of Edge-based Data Analytics -- 8.6.1 Overview of Crowd 
       Mobility Analytics -- 8.6.2 Processing Tasks and Topology 
       of Crowd Mobility Analytics -- 8.7 Conclusion and 
       FutureWork -- References -- Chapter 9 -- IoT Analytics: 
       From Data Collection to Deployment and Operationalization 
       -- 9.1 Operationalizing Data Analytics Using the VITAL 
       Platform -- 9.1.1 IoT Data Analysis -- 9.1.2 IoT Data 
       Deployment and Reuse -- 9.2 Knowledge Extraction and IoT 
       Analytics Operationalization -- 9.3 A Practical Example 
       based on Footfall Data -- Acknowledgement -- References --
       Chapter 10 -- Ethical IoT: A SustainableWay Forward -- 
       10.1 Introduction -- 10.2 From IoT to a Data Driven 
       Economy and Society -- 10.3 Way Forward with IoT -- 10.4 
       Conclusions -- References -- Epilogue -- Index -- About 
       the Editor -- About the Authors -- Back Cover. 
590    Taylor & Francis|bTaylor & Francis eBooks: Open Access 
650  0 Big data|vCongresses. 
650  7 COMPUTERS|xComputer Literacy.|2bisacsh 
650  7 COMPUTERS|xComputer Science.|2bisacsh 
650  7 COMPUTERS|xData Processing.|2bisacsh 
650  7 COMPUTERS|xHardware|xGeneral.|2bisacsh 
650  7 COMPUTERS|xInformation Technology.|2bisacsh 
650  7 COMPUTERS|xMachine Theory.|2bisacsh 
650  7 COMPUTERS|xReference.|2bisacsh 
650  7 Big data.|2fast|0(OCoLC)fst01892965 
655  0 Electronic books. 
655  7 Conference papers and proceedings.|2fast
       |0(OCoLC)fst01423772 
776 08 |iPrint version:|aSoldatos, John.|tBuilding Blocks of IoT 
       Analytics : Internet-Of-Things Analytics.|dAalborg : River
       Publishers, ©2016|z9788793519039 
830  0 River Publishers Series in Signal, Image and Speech 
       Processing Ser. 
914    on1076652697 
947    MARCIVE Processed 2022/11/04 
994    92|bSTJ