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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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Aalborg :
River Publishers,
2016.
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| Series: | River Publishers Series in Signal, Image and Speech Processing Ser.
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Table of Contents:
- 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.
- 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.
- 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.
- 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.