The Building Blocks of IoT Analytics : Internet-Of-Things Analytics.
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizati...
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| Language: | English |
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Aalborg :
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2016.
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| Series: | River Publishers Series in Signal, Image and Speech Processing Ser.
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MARC
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| 100 | 1 | |a Soldatos, John. | |
| 245 | 1 | 4 | |a The Building Blocks of IoT Analytics : |b Internet-Of-Things Analytics. |
| 264 | 1 | |a Aalborg : |b River Publishers, |c 2016. | |
| 264 | 4 | |c ©2017 | |
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| 490 | 1 | |a River Publishers Series in Signal, Image and Speech Processing Ser. | |
| 505 | 0 | |a 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 | |a 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 | |a 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 | |a 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. | |
| 520 | |a Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly 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 role 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 the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI). | ||
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