The Internet of Things

From Data to Insight
 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erscheint ca. am 30. März 2020
  • |
  • 240 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-119-54528-6 (ISBN)
 

Provides comprehensive coverage of the current state of IoT, focusing on data processing infrastructure and techniques

Written by experts in the field, this book addresses the IoT technology stack, from connectivity through data platforms to end-user case studies, and considers the tradeoffs between business needs and data security and privacy throughout. There is a particular emphasis on data processing technologies that enable the extraction of actionable insights from data to inform improved decision making. These include artificial intelligence techniques such as stream processing, deep learning and knowledge graphs, as well as data interoperability and the key aspects of privacy, security and trust. Additional aspects covered include: creating and supporting IoT ecosystems; edge computing; data mining of sensor datasets; and crowd-sourcing, amongst others. The book also presents several sections featuring use cases across a range of application areas such as smart energy, transportation, smart factories, and more. The book concludes with a chapter on key considerations when deploying IoT technologies in the enterprise, followed by a brief review of future research directions and challenges.

The Internet of Things: From Data to Insight

  • Provides a comprehensive overview of the Internet of Things technology stack with focus on data driven aspects from data modelling and processing to presentation for decision making
  • Explains how IoT technology is applied in practice and the benefits being delivered.
  • Acquaints readers that are new to the area with concepts, components, technologies, and verticals related to and enabled by IoT
  • Gives IoT specialists a deeper insight into data and decision-making aspects as well as novel technologies and application areas
  • Analyzes and presents important emerging technologies for the IoT arena
  • Shows how different objects and devices can be connected to decision making processes at various levels of abstraction

The Internet of Things: From Data to Insight will appeal to a wide audience, including IT and network specialists seeking a broad and complete understanding of IoT, CIOs and CIO teams, researchers in IoT and related fields, final year undergraduates, graduate students, post-graduates, and IT and science media professionals.



EDITED BY

JOHN DAVIES, PHD, is Chief Researcher in BT's Research & Innovation Department, UK, where he leads a team focused on Internet of Things technologies. He is a Fellow of the British Computer Society and a Chartered Engineer as well as a Visiting Professor at the Open University and has published over 100 scientific articles.

CAROLINA FORTUNA, PHD, is a Research Fellow at the Jo??ef Stefan Institute, Slovenia. She received her PhD in Computer Science in 2013, was a postdoctoral research associate at Ghent University, 2014-2015 and a Visitor at Stanford University in 2017. She has authored over 60 peer reviewed papers, technically led EU-funded research projects and is a consultant to industry.

  • Englisch
  • Newark
  • |
  • Großbritannien
John Wiley & Sons Inc
  • 5,60 MB
978-1-119-54528-6 (9781119545286)
weitere Ausgaben werden ermittelt
EDITED BY

JOHN DAVIES, PHD, is Chief Researcher in BT's Research & Innovation Department, UK, where he leads a team focused on Internet of Things technologies. He is a Fellow of the British Computer Society and a Chartered Engineer as well as a Visiting Professor at the Open University and has published over 100 scientific articles.

CAROLINA FORTUNA, PHD, is a Research Fellow at the Jo??ef Stefan Institute, Slovenia. She received her PhD in Computer Science in 2013, was a postdoctoral research associate at Ghent University, 2014-2015 and a Visitor at Stanford University in 2017. She has authored over 60 peer reviewed papers, technically led EU-funded research projects and is a consultant to industry.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • About the Editors
  • List of Contributors
  • Acknowledgments
  • Chapter 1 Introduction
  • 1.1 Stakeholders in IoT Ecosystems
  • 1.2 Human and IoT Sensing, Reasoning, and Actuation: An Analogy
  • 1.3 Replicability and Re-use in IoT
  • 1.4 Overview
  • References
  • Chapter 2 Connecting Devices: Access Networks
  • 2.1 Introduction
  • 2.2 Overview of Access Networks
  • 2.2.1 Existing Technologies Are Able to Cover a Number of IoT Scenarios
  • 2.3 Low-Power Wide Area Network (LPWAN)
  • 2.3.1 Long-Range (LoRa) Low-Power Wide Area Network
  • 2.3.2 Sigfox Low-Power Wide Area Network
  • 2.3.3 Weightless Low-Power Wide Area Network
  • 2.4 Cellular Technologies
  • 2.4.1 Emerging 5G Cellular Technology
  • 2.5 Conclusion
  • References
  • Chapter 3 Edge Computing
  • 3.1 Introduction
  • 3.2 Edge Computing Fundamentals
  • 3.2.1 Edge Compute Strategies
  • 3.2.2 Network Connectivity
  • 3.3 Edge Computing Architecture
  • 3.3.1 Device Overview
  • 3.3.2 Edge Application Modules
  • 3.3.3 IoT Runtime Environment
  • 3.3.4 Device Management
  • 3.3.5 Secure Runtime Environment
  • 3.4 Implementing Edge Computing Solutions
  • 3.4.1 Starter Configuration
  • 3.4.2 Developer Tools
  • 3.4.3 Edge Computing Frameworks
  • 3.5 Zero-Touch Device On-boarding
  • 3.6 Applying Edge Computing
  • 3.7 Conclusions
  • References
  • Chapter 4 Data Platforms: Interoperability and Insight
  • 4.1 Introduction
  • 4.2 IoT Ecosystems
  • 4.3 Context
  • 4.4 Aspects of Interoperability
  • 4.4.1 Discovery
  • 4.4.2 Access Control
  • 4.4.3 Data Access
  • 4.5 Conclusion
  • References
  • Chapter 5 Streaming Data Processing for IoT
  • 5.1 Introduction
  • 5.2 Fundamentals
  • 5.2.1 Compression
  • 5.2.2 Dimensionality Reduction
  • 5.2.3 Summarization
  • 5.2.4 Learning and Mining
  • 5.2.5 Visualization
  • 5.3 Architectures and Languages
  • 5.4 Stream Analytics and Spectrum Sensing
  • 5.4.1 Real-Time Notifications
  • 5.4.2 Statistical Reporting
  • 5.4.3 Custom Applications
  • 5.5 Summary
  • References
  • Chapter 6 Applied Machine Vision and IoT
  • 6.1 Introduction: Machine Vision and the Proliferation of Smart Internet of Things Driven Environments
  • 6.2 Machine Vision Fundamentals
  • 6.3 Overview of Relevant Work: Current Trends in Machine Vision in IoT
  • 6.3.1 Improved Perception for IoT
  • 6.3.2 Improved Interpretation and Learning for IoT
  • 6.4 A Generic Deep Learning Framework for Improved Situation Awareness
  • 6.5 Evaluating the Impact of Deep Learning in Different IoT Related Verticals
  • 6.5.1 Sensing Critical Infrastructures Using Cognitive Drone-Based Systems
  • 6.5.2 Sensing Public Spaces Using Smart Embedded Systems
  • 6.5.3 Preventive Maintenance Service Comparison Based on Drone High-Definition Images
  • 6.6 Best Practice
  • 6.7 Summary
  • References
  • Chapter 7 Data Representation and Reasoning
  • 7.1 Introduction
  • 7.2 Fundamentals
  • 7.3 Semantic IoT and Semantic WoT (SWoT)
  • 7.4 Semantics for IoT Integration
  • 7.4.1 IoT Ontologies and IoT-O
  • 7.4.2 The Digital Twin Approach
  • 7.5 Use Case
  • 7.6 Summary
  • References
  • Chapter 8 Crowdsourcing and Human-in-the-Loop for IoT
  • 8.1 Introduction
  • 8.2 Crowdsourcing
  • 8.3 Human-in-the-Loop
  • 8.4 Spatial Crowdsourcing
  • 8.5 Participatory Sensing
  • 8.6 Conclusion
  • References
  • Chapter 9 IoT Security: Experience Is an Expensive Teacher
  • 9.1 Introduction
  • 9.2 Why Is IoT Security Different from IT Security?
  • 9.3 What Is Being Done to Address IoT Security Challenges?
  • 9.3.1 Governments
  • 9.3.2 Standards Bodies
  • 9.3.3 Industry Groups
  • 9.4 Picking the Low-Hanging Fruit
  • 9.4.1 Basic Hygiene Factors
  • 9.4.2 Methodologies and Compliance Frameworks
  • 9.4.3 Labeling Schemes and Consumer Advice
  • 9.5 Summary
  • References
  • Chapter 10 IoT Data Privacy
  • 10.1 Introduction
  • 10.2 Basic Concepts in IoT Data Privacy
  • 10.2.1 What Is Personal Data?
  • 10.2.2 General Requirements for Data Privacy
  • 10.2.3 Personal Data and IoT
  • 10.2.4 Existing Privacy Preservation Approaches
  • 10.2.5 Toward a Standards-Based Approach in Support of PIMS Business Models
  • 10.3 A Data Handling Framework Based on Consent Information and Privacy Preferences
  • 10.3.1 A Data Handling Framework
  • 10.3.2 Privacy Preference Manager (PPM)
  • 10.3.3 Implementation of the Framework
  • 10.4 Standardization for a User-Centric Data Handling Architecture
  • 10.4.1 Introduction to oneM2M
  • 10.4.2 PPM in oneM2M
  • 10.5 Example Use Cases
  • 10.5.1 Services Based on Home Energy Data
  • 10.5.2 HEMS Service
  • 10.5.3 Delivery Service
  • 10.6 Conclusions
  • References
  • Chapter 11 Blockchain: Enabling Trust on the Internet of Things
  • 11.1 Introduction
  • 11.2 Distributed Ledger Technologies and the Blockchain
  • 11.2.1 Distributed Ledger Technology Overview
  • 11.2.2 Basic Concepts and Architecture
  • 11.2.2.1 Consensus Algorithm
  • 11.2.3 When to Deploy DLT
  • 11.3 The Ledger of Things: Blockchain and IoT
  • 11.4 Benefits and Challenges
  • 11.5 Blockchain Use Cases
  • 11.6 Conclusion
  • References
  • Chapter 12 Healthcare
  • 12.1 Internet of Things in Healthcare Settings
  • 12.1.1 Monitoring Patient Status in Hospitals
  • 12.1.2 IoT from Healthcare to Everyday Life
  • 12.1.3 Systems Interoperability
  • 12.2 BigEHR: A Federated Repository for a Holistic Lifelong Health Record
  • 12.2.1 Why a Federated Design?
  • 12.2.2 System Architecture
  • 12.3 Gathering IoT Health-Related Data
  • 12.3.1 From Inside the Hospitals
  • 12.3.2 Feeding Data from Outside Sources
  • 12.4 Extracting Meaningful Information from IoT Data
  • 12.4.1 Privacy Concerns
  • 12.4.2 Distributed Reasoning
  • 12.5 Outlook
  • Acknowledgments
  • References
  • Chapter 13 Smart Energy
  • 13.1 Introduction
  • 13.2 Use Case Description
  • 13.2.1 The Role of 5G in the Smart Grid IoT Context
  • 13.3 Reference Architecture
  • 13.4 Use Case Validation
  • 13.4.1 AMI-Based Continuous Power Quality Assessment System
  • 13.5 Conclusion
  • Acknowledgment
  • References
  • Chapter 14 Road Transport and Air Quality
  • 14.1 Introduction
  • 14.2 The Air Pollution Challenge
  • 14.3 Road Traffic Air Pollution Reduction Strategies
  • 14.4 Monitoring Air Pollution Using IoT
  • 14.5 Use Case: Reducing Emissions Through an IoT-Based Advanced Traffic Management System
  • 14.6 Limitations of Average Speed Air Quality Modeling
  • 14.7 Future Roadmap and Summary
  • References
  • Chapter 15 Conclusion
  • 15.1 Origins and Evolution
  • 15.2 Why Now?
  • 15.2.1 Falling Costs and Miniaturization
  • 15.2.2 Societal Challenges and Resource Efficiency
  • 15.2.3 Information Sharing Comes of Age
  • 15.2.4 Managing Complexity
  • 15.2.5 Technological Readiness
  • 15.3 Maximizing the Value of Data
  • 15.4 Commercial Opportunities
  • 15.5 A Glimpse of the Future
  • References
  • Index
  • EULA

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