Internet of Things

Principles and Paradigms
 
 
Morgan Kaufmann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 11. Mai 2016
  • |
  • 378 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-809347-4 (ISBN)
 

Internet of Things: Principles and Paradigms captures the state-of-the-art research in Internet of Things, its applications, architectures, and technologies. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. The Internet of Things (IoT) paradigm promises to make any electronic devices part of the Internet environment. This new paradigm opens the doors to new innovations and interactions between people and things that will enhance the quality of life and utilization of scarce resources.

To help realize the full potential of IoT, the book addresses its numerous challenges and develops the conceptual and technological solutions for tackling them. These challenges include the development of scalable architecture, moving from closed systems to open systems, designing interaction protocols, autonomic management, and the privacy and ethical issues around data sensing, storage, and processing.


  • Addresses the main concepts and features of the IoT paradigm
  • Describes different architectures for managing IoT platforms
  • Provides insight on trust, security, and privacy in IoT environments
  • Describes data management techniques applied to the IoT environment
  • Examines the key enablers and solutions to enable practical IoT systems
  • Looks at the key developments that support next generation IoT platforms
  • Includes input from expert contributors from both academia and industry on building and deploying IoT platforms and applications
  • Englisch
  • San Francisco
  • |
  • USA
Elsevier Science
  • 17,85 MB
978-0-12-809347-4 (9780128093474)
0128093471 (0128093471)
weitere Ausgaben werden ermittelt
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • List of Contributors
  • About the Editors
  • Preface
  • Organization of the book
  • Acknowledgments
  • Part I - IoT Ecosystem concepts and architectures
  • Chapter 1 - Internet of Things: an overview
  • 1.1 - Introduction
  • 1.2 - Internet of Things definition evolution
  • 1.2.1 - IoT emergence
  • 1.2.2 - Internet of Everything
  • 1.2.3 - Industrial IoT
  • 1.2.4 - Smartness in IoT
  • 1.2.5 - Market share
  • 1.2.6 - Human in the loop
  • 1.2.7 - Improving the quality of life
  • 1.3 - IoT architectures
  • 1.3.1 - SOA-based architecture
  • 1.3.2 - API-oriented architecture
  • 1.4 - Resource management
  • 1.4.1 - Resource partitioning
  • 1.4.2 - Computation offloading
  • 1.4.3 - Identification and resource/service discovery
  • 1.5 - IoT data management and analytics
  • 1.5.1 - IoT and the Cloud
  • 1.5.2 - Real-time analytics in IoT and Fog Computing
  • 1.6 - Communication protocols
  • 1.6.1 - Network layer
  • 1.6.2 - Transport and application layer
  • 1.7 - Internet of Things applications
  • 1.7.1 - Monitoring and actuating
  • 1.7.2 - Business process and data analysis
  • 1.7.3 - Information gathering and collaborative consumption
  • 1.8 - Security
  • 1.9 - Identity management and authentication
  • 1.10 - Privacy
  • 1.11 - Standardization and regulatory limitations
  • 1.12 - Conclusions
  • References
  • Chapter 2 - Open source semantic web infrastructure for managing IoT resources in the Cloud
  • 2.1 - Introduction
  • 2.2 - Background/related work
  • 2.3 - OpenIoT architecture for IoT/Cloud convergence
  • 2.4 - Scheduling process and IoT services lifecycle
  • 2.5 - Scheduling and resource management
  • 2.6 - Validating applications and use cases
  • 2.7 - Future research directions
  • 2.8 - Conclusions
  • Acknowledgments
  • References
  • Chapter 3 - Device/Cloud collaboration framework for intelligence applications
  • 3.1 - Introduction
  • 3.2 - Background and related work
  • 3.3 - Device/Cloud collaboration framework
  • 3.3.1 - Powerful smart mobile devices
  • 3.3.2 - Runtime Adaptation Engine
  • 3.3.3 - Privacy-protection solution
  • 3.4 - Applications of Device/Cloud collaboration
  • 3.4.1 - Context-aware Proactive Suggestion
  • 3.4.2 - Semantic QA cache
  • 3.4.3 - Image and speech recognition
  • 3.5 - Future work
  • 3.6 - Conclusions
  • Acknowledgments
  • References
  • Chapter 4 - Fog Computing: principles, architectures, and applications
  • 4.1 - Introduction
  • 4.2 - MotivatiNG scenario
  • 4.3 - Definitions and characteristics
  • 4.4 - Reference architecture
  • 4.5 - Applications
  • 4.5.1 - Healthcare
  • 4.5.2 - Augmented reality
  • 4.5.3 - Caching and preprocessing
  • 4.6 - Research directions and enablers
  • 4.6.1 - Programming models
  • 4.6.2 - Security and reliability
  • 4.6.3 - Resource management
  • 4.6.4 - Energy minimization
  • 4.7 - Commercial products
  • 4.7.1 - Cisco IOx
  • 4.7.2 - Data in motion
  • 4.7.3 - LocalGrid
  • 4.7.4 - ParStream
  • 4.7.5 - Prismtech Vortex
  • 4.8 - Case study
  • 4.8.1 - Experiment setup
  • 4.8.1.1 - Network Topology and Data Sources
  • 4.8.2 - Performance evaluation
  • 4.8.2.1 - Average Tuple Delay
  • 4.8.2.2 - Core Network Usage
  • 4.9 - Conclusions
  • References
  • Part II - IoT Enablersand solutions
  • Chapter 5 - Programming frameworks for Internet of Things
  • 5.1 - Introduction
  • 5.2 - Background
  • 5.2.1 - Overview
  • 5.2.2 - Embedded device programming languages
  • 5.2.2.1 - nesC
  • 5.2.2.2 - Keil C
  • 5.2.2.3 - Dynamic C
  • 5.2.2.4 - B#
  • 5.2.3 - Message passing in devices
  • 5.2.3.1 - RPC
  • 5.2.3.2 - REST
  • 5.2.3.3 - CoAP
  • 5.2.4 - Coordination languages
  • 5.2.4.1 - Linda and eLinda
  • 5.2.4.2 - Orc
  • 5.2.4.3 - Jolie
  • 5.2.5 - Polyglot programming
  • 5.3 - Survey of IoT programming frameworks
  • 5.3.1 - Overview
  • 5.3.2 - IoT programming approaches
  • 5.3.3 - Existing IoT frameworks
  • 5.3.3.1 - Mobile Fog
  • 5.3.3.2 - ELIoT (Erlang Language for IoT)
  • 5.3.3.3 - Compose API
  • 5.3.3.4 - Distributed Dataflow Support for IoT
  • 5.3.3.5 - PyoT
  • 5.3.3.6 - Dripcast
  • 5.3.3.7 - Calvin
  • 5.3.3.8 - Simurgh
  • 5.3.3.9 - High-Level Application Development for the Internet of Things
  • 5.3.3.10 - PatRICIA
  • 5.3.4 - Summary
  • 5.4 - Future research directions
  • 5.5 - Conclusions
  • References
  • Chapter 6 - Virtualization on embedded boards as enabling technology for the Cloud of Things
  • 6.1 - Introduction
  • 6.2 - Background
  • 6.2.1 - ARM virtualization extensions
  • 6.2.2 - XEN ARM virtualization
  • 6.2.3 - KVM ARM virtualization
  • 6.2.4 - Container-based virtualization
  • 6.3 - Virtualization and real-time
  • 6.4 - Experimental results
  • 6.4.1 - Reference architecture
  • 6.4.2 - Benchmarking tools
  • 6.4.3 - Discussion
  • 6.4.3.1 - CPU Performance Analysis
  • 6.4.3.2 - Memory Performance Analysis
  • 6.4.3.3 - Memory Footprint Analysis
  • 6.4.3.4 - I/O Performance Analysis
  • 6.4.3.5 - Network Performance Analysis
  • 6.5 - Future research directions
  • 6.6 - Conclusions
  • References
  • Chapter 7 - Micro Virtual Machines (MicroVMs) for Cloud-assisted Cyber-Physical Systems (CPS)
  • 7.1 - Introduction
  • 7.2 - Related work
  • 7.2.1 - Virtual machines and Micro Virtual Machines
  • 7.2.2 - Other architectures
  • 7.3 - Architecture for deploying CPS in the Cloud and the expansion of the IoT
  • 7.4 - Extending the possibilities of the IoT by Cloud Computing
  • 7.5 - Micro Virtual Machines with the Sensor Observation Service, the path between smart objects and CPS
  • 7.5.1 - Virtual machines and Sensor Observation Service
  • 7.5.2 - Implementation
  • 7.6 - IoT architecture for selected use cases
  • 7.6.1 - eHealth
  • 7.6.2 - Precision Agriculture
  • 7.6.3 - Domotic
  • 7.7 - Future research directions
  • 7.8 - Conclusions
  • References
  • Part III - IoT Data andknowledgemanagement
  • Chapter 8 - Stream processing in IoT: foundations, state-of-the-art, and future directions
  • 8.1 - Introduction
  • 8.2 - The foundations of stream processing in IoT
  • 8.2.1 - Stream
  • 8.2.2 - Stream processing
  • 8.2.3 - The Characteristics of stream data in IoT
  • 8.2.3.1 - Timeliness and Instantaneity
  • 8.2.3.2 - Randomness and Imperfection
  • 8.2.3.3 - Endlessness and Continuousness
  • 8.2.3.4 - Volatility and Unrepeatability
  • 8.2.4 - The general architecture of a stream-processing system in IoT
  • 8.3 - Continuous Logic Processing System
  • 8.4 - Challenges and future directions
  • 8.4.1 - Scalability
  • 8.4.2 - Robustness
  • 8.4.3 - SLA-compliance
  • 8.4.4 - Load balancing
  • 8.5 - Conclusions
  • References
  • Chapter 9 - A framework for distributed data analysis for IoT
  • 9.1 - Introduction
  • 9.2 - Preliminaries
  • 9.3 - Anomaly detection
  • 9.4 - Problem statement and definitions
  • 9.4.1 - Hyperellipsoidal anomaly detection
  • 9.5 - Distributed anomaly detection
  • 9.5.1 - Clustering ellipsoids
  • 9.5.2 - Experimental results
  • 9.6 - Efficient incremental local modeling
  • 9.6.1 - Incremental updates
  • 9.6.2 - Implementation of incremental updates
  • 9.6.3 - Experimental results
  • 9.7 - Summary
  • References
  • Part IV - IoT Reliability,security, andprivacy
  • Chapter 10 - Security and privacy in the Internet of Things
  • 10.1 - Concepts
  • 10.1.1 - IoT reference model
  • 10.1.2 - IoT security threats
  • 10.1.3 - IoT security requirements
  • 10.1.3.1 - Scale
  • 10.1.3.2 - IP Protocol-Based IoT
  • 10.1.3.3 - Heterogeneous IoT
  • 10.1.3.4 - Lightweight Security
  • 10.2 - IoT security overview
  • 10.2.1 - IoT protocols
  • 10.2.2 - Network and transport layer challenges
  • 10.2.3 - IoT gateways and security
  • 10.2.4 - IoT routing attacks
  • 10.2.5 - Bootstrapping and authentication
  • 10.2.6 - Authorization mechanisms
  • 10.2.6.1 - Resource Owner
  • 10.2.6.2 - Resource Server (Service Provider, SP)
  • 10.2.6.3 - Client (Service Consumer, SC)
  • 10.2.6.4 - Authorization Server
  • 10.2.7 - IoT OAS
  • 10.3 - Security frameworks for IoT
  • 10.3.1 - Light weight cryptography
  • 10.3.1.1 - Symmetric-Key LWC Algorithms
  • 10.3.2 - Asymmetric LWC algorithms
  • 10.3.3 - Key agreement, distribution, and bootstrapping
  • 10.3.3.1 - Security Bootstrapping
  • 10.4 - Privacy in IoT networks
  • 10.4.1 - Secure data aggregation
  • 10.4.2 - Enigma
  • 10.4.3 - Zero knowledge protocols
  • 10.4.4 - Privacy in beacons
  • 10.5 - Summary and conclusions
  • References
  • Chapter 11 - Internet of Things-robustness and reliability
  • 11.1 - Introduction
  • 11.2 - IoT characteristics and reliability issues
  • 11.2.1 - IoT architecture in brief
  • 11.2.1.1 - Different Categories of Applications
  • 11.2.1.1.1 - Zero tolerance
  • 11.2.1.1.2 - Restartable
  • 11.2.1.1.3 - Error Tolerant
  • 11.2.2 - Failure scenarios
  • 11.2.2.1 - Infrastructure Fault
  • 11.2.2.2 - Interaction Fault
  • 11.2.2.3 - Fault in Service Platform
  • 11.2.3 - Reliability challenges
  • 11.2.3.1 - Making Service Available to User
  • 11.2.3.2 - Serviceability of IoT System
  • 11.2.3.3 - Reliability at Network Level
  • 11.2.3.4 - Device Level Reliability
  • 11.2.4 - Privacy and reliability
  • 11.2.5 - Interoperability of devices
  • 11.2.6 - Reliability issues due to energy constraint
  • 11.3 - Addressing reliability
  • 11.3.1 - Nullifying impact of fault
  • 11.3.1.1 - Redundancy in Service Platform Design
  • 11.3.1.2 - Redundancy in M2M Topology
  • 11.3.1.3 - Graceful Degradation
  • 11.3.1.3.1 - Software Design
  • 11.3.1.3.2 - Performability Model
  • 11.3.2 - Error detection
  • 11.3.2.1 - Watchdog
  • 11.3.2.2 - Heartbeat
  • 11.3.2.3 - Exception Handling
  • 11.3.2.4 - Recovery Through Restart
  • 11.3.3 - Fault prevention
  • 11.3.3.1 - Failure Prediction
  • 11.3.3.2 - Improving Communication Reliability
  • 11.3.3.2.1 - Service Degradation Support
  • 11.3.3.3 - Failure Prevention by Service Platform
  • 11.3.3.4 - Improving Energy Efficiency
  • 11.3.3.4.1 - Device Power Management
  • 11.3.3.4.2 - Communication Power Management
  • 11.3.3.4.3 - Service Platform
  • 11.3.3.4.3.1 - Data Quality vs Energy Usage
  • References
  • Chapter 12 - Governing Internet of Things: issues, approaches, and new paradigms
  • 12.1 - Introduction
  • 12.2 - Background and related work
  • 12.2.1 - Overview
  • 12.2.2 - Background
  • 12.2.2.1 - Governance
  • 12.2.2.2 - Internet Governance
  • 12.2.2.3 - Enterprise Network Management
  • 12.2.2.4 - Management Versus Governance
  • 12.2.2.5 - Surveillance and Internet of Things
  • 12.2.3 - Related work
  • 12.3 - IoT governance
  • 12.3.1 - Overview
  • 12.3.2 - An integrated governance idea
  • 12.3.3 - Governance models
  • 12.3.4 - Important governance issues
  • 12.3.5 - Existing approaches
  • 12.3.6 - New paradigms
  • 12.4 - Future research directions
  • 12.5 - Conclusions
  • References
  • Chapter 13 - TinyTO: two-way authentication for constrained devices in the Internet of Things
  • 13.1 - Introduction
  • 13.2 - Security aspects and solutions
  • 13.3 - Design decisions
  • 13.4 - TinyTO protocol
  • 13.4.1 - Possible handshake protocol candidates
  • 13.4.2 - BCK with preshared keys for TinyTO
  • 13.4.3 - Handshake implementation
  • 13.5 - Evaluation
  • 13.5.1 - Memory consumption
  • 13.5.2 - Runtime performance
  • 13.5.3 - Energy consumption
  • 13.6 - Summary
  • Acknowledgments
  • References
  • Chapter 14 - Obfuscation and diversification for securing the internet of things (IoT)
  • 14.1 - Introduction
  • 14.2 - Distinguishing characteristics of IoT
  • 14.2.1 - Operating systems and software in IoT
  • 14.2.2 - IoT network stack and access protocols
  • 14.2.3 - Security and privacy in IoT
  • 14.3 - Obfuscation and diversification techniques
  • 14.4 - Enhancing the security in IoT using obfuscation and diversification techniques
  • 14.4.1 - Motivations and limitations of the proposed ideas
  • 14.5 - Different use-case scenarios on software diversification and obfuscation
  • 14.6 - Conclusions and future work
  • References
  • Part V - IoT Applications
  • Chapter 15 - Applied Internet of Things
  • 15.1 - Introduction
  • 15.2 - Scenario
  • 15.3 - Architecture overview
  • 15.3.1 - Sensor to gateway communication
  • 15.3.1.1 - Wired Gateway Interfaces
  • 15.3.1.2 - Wireless Gateway Interfaces
  • 15.4 - Sensors
  • 15.5 - The gateway
  • 15.5.1 - Gateway hardware
  • 15.5.2 - Gateway software
  • 15.5.3 - Summary
  • 15.6 - Data transmission
  • 15.6.1 - Advanced Message Queuing Protocol
  • 15.6.2 - Backend processing
  • 15.6.2.1 - Overview
  • 15.6.2.2 - Data Processing Framework
  • 15.6.3 - To Cloud or not to Cloud
  • 15.7 - Conclusions
  • Acknowledgments
  • References
  • Chapter 16 - Internet of Vehicles and applications
  • 16.1 - Basics of IoV
  • 16.1.1 - Background and concept
  • 16.1.2 - Network architecture
  • 16.1.2.1 - Vehicles in IoV
  • 16.1.2.2 - Connections in IoV
  • 16.1.2.3 - Servers/Clouds in IoV
  • 16.2 - Characteristics and challenges
  • 16.2.1 - Characteristics of IoV
  • 16.2.2 - Challenges in IoV
  • 16.3 - Enabling technologies
  • 16.3.1 - MAC protocols and standards
  • 16.3.1.1 - IEEE 802.11
  • 16.3.1.2 - IEEE 802.11p/WAVE
  • 16.3.2 - Routing protocols
  • 16.3.2.1 - AODV
  • 16.3.2.2 - OLSR
  • 16.3.2.3 - Multihop-MAC Protocol (IEEE 802.11s)
  • 16.3.3 - Broadcasting and information dissemination
  • 16.3.3.1 - V2V Based
  • 16.3.3.2 - V2R Based
  • 16.3.3.3 - DTN Based
  • 16.4 - Applications
  • 16.4.1 - Driving safety related
  • 16.4.2 - Transportation efficiency related
  • 16.4.2.1 - Intersection Control
  • 16.4.2.2 - Route Navigation
  • 16.4.2.3 - Parking Navigation
  • 16.4.2.4 - Cooperative Driving
  • 16.4.3 - Infotainment services
  • 16.5 - Summary and future directions
  • References
  • Chapter 17 - Cloud-Based Smart-Facilities Management
  • 17.1 - Introduction
  • 17.2 - Background and related work
  • 17.3 - A cloud-based architecture for smart-facility management
  • 17.4 - Middleware services
  • 17.5 - Resource management techniques for wireless sensor networks
  • 17.5.1 - Sensor allocation
  • 17.5.2 Request scheduling
  • 17.6 - Resource management techniques for supporting data analytics
  • 17.6.1 - Streaming data analytics
  • 17.7 - Case study: management of sensor-based bridges
  • 17.8 - Case study: research collaboration platform for management of smart machinery
  • 17.9 - Conclusions
  • 17.9.1 - Future research directions
  • Acknowledgments
  • References
  • Index
  • Back cover

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