Pervasive computing : next generation platforms for intelligent data collection /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Dobre, Ciprian (Editor ), Xhafa, Fatos (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, UK : Academic Press is an imprint of Elsevier, 2016.
Colección:Intelligent data centric systems.
Temas:
Ubiquitous computing.
Informatique omnipr�esente.
Ubiquitous computing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Pervasive Computing: Next Generation Platforms for Intelligent Data Collection
  • Copyright
  • Dedication
  • Contents
  • Contributors
  • About the Editors
  • Foreword
  • Preface
  • Organization of the Book
  • Acknowledgments
  • Part I: Automated capture of experiences with easy access
  • Chapter 1: On preserving privacy in cloud computing using ToR
  • 1 Introduction
  • 2 Overview of cloud computing
  • 2.1 Cloud Computing Reference Model
  • 2.2 Privacy in the Cloud
  • 2.3 Anonymity in the Cloud
  • 3 An overview of ToR
  • 3.1 The ToR Network
  • 3.2 Connection/Circuit Setup in ToR
  • 3.3 Attacks Against ToR
  • 3.3.1 Denial of service attack
  • 3.3.2 The timing attack
  • 3.3.3 Website fingerprinting attack
  • 3.4 ToR in Existing Implementations
  • 4 ToR in cloud computing
  • 4.1 Our Experiments
  • 4.1.1 ToR with DropBox
  • 4.1.2 ToR with FaceBook
  • 4.1.3 ToR with Youtube
  • 5 Conclusion
  • Acronyms
  • Glossary
  • References
  • Chapter 2: Self-adaptive overlay networks
  • 1 Introduction
  • 2 Background/literature review/context
  • 2.1 Unstructured Peer-to-Peer
  • 2.1.1 Freenet 2001
  • Joining, leaving, and lookup in Freenet overlay
  • 2.1.2 Gnutella 2002
  • Joining, leaving, and lookup in Gnutella overlay
  • 2.1.3 FastTrack 2006
  • Joining, leaving, and lookup in FastTrack overlay
  • 2.1.4 Other unstructured peer-to-peer overlays
  • BitTorrent 2003
  • Gia 2004
  • UMM 2010
  • 2.2 Structured Peer-to-Peer
  • 2.2.1 CAN 2001
  • Joining in CAN overlays
  • Leaving in CAN overlays
  • Lookup in CAN overlays
  • Applications of CAN overlays
  • 2.2.2 Kademlia 2002
  • Joining and leaving in Kademlia overlays
  • Lookup in Kademlia overlays
  • Applications of Kademlia overlays
  • 2.2.3 Chord 2003
  • Joining and leaving Chord P2P overlays
  • Lookup in Chord overlays
  • Applications of Chord overlays
  • 2.2.4 Other structured P2P overlays.
  • Viceroy 2002
  • SkipNet 2003
  • Coral 2004
  • Tapestry 2004
  • Cycloid 2006
  • HyPeer 2011
  • 3 Self-adaptive overlays
  • 3.1 Bio-Inspired P2P Overlays
  • 3.1.1 Self-Chord 2010
  • 3.1.2 P2PBA 2011
  • 3.1.3 Self-CAN 2012
  • 3.1.4 Honeycomb 2014
  • 3.1.5 SPIDER 2015
  • 3.2 Multi-Layer Peer-to-Peer
  • 4 Hybrid peer-to-peer systems
  • 4.1 JXTA
  • 4.2 MOPAR
  • 5 Discussion and conclusions
  • Acronyms
  • Acknowledgments
  • References
  • Chapter 3: Users in the urban sensing process: Challenges and research opportunities
  • 1 Introduction
  • 2 Participatory sensor networks
  • 2.1 What is a Participatory Sensor Network?
  • 2.2 The Functioning of PSN
  • 2.3 Examples of PSNs
  • 3 Properties of PSN
  • 3.1 Data Description
  • 3.2 Network Coverage
  • 3.3 Sensing Interval
  • 3.4 Routines and Data Sharing
  • 3.5 Node Behavior
  • 3.6 Discussion
  • 4 Working with PSN data
  • 4.1 Data Collection
  • 4.1.1 APIs
  • 4.1.2 Web crawler
  • 4.1.3 Applications
  • 4.2 Understanding City Dynamics
  • 4.3 Social, Economic, and Cultural Patterns
  • 4.4 Final Considerations
  • 5 Challenges and opportunities
  • 5.1 Sensing Layers
  • 5.1.1 Preliminaries
  • 5.1.2 Framework for the integration of multiple layers
  • 5.1.3 Challenges and opportunities
  • 5.2 Temporal Dynamics of PSNs
  • 5.2.1 Preliminaries
  • 5.2.2 Challenges and opportunities
  • 5.3 Incentive Mechanism for PSN
  • 5.3.1 Preliminaries
  • 5.3.2 User cooperation
  • 5.3.3 Reward-based incentive mechanisms
  • 5.3.4 Gamification-based incentive mechanism
  • 5.3.5 Challenges and opportunities
  • 5.4 Quality of Data From PSN
  • 5.4.1 Preliminaries
  • 5.4.2 Challenges
  • 5.4.3 Opportunities
  • 5.5 PSNs and Vehicular Networks
  • 5.5.1 Preliminaries
  • 5.5.2 Monitoring events
  • 5.5.3 Routines and behaviors
  • 5.5.4 Traffic management
  • 5.5.5 Challenges and opportunities
  • 5.6 Other Challenges and Opportunities Related to PSNs.
  • 5.6.1 Data sampling
  • 5.6.2 Large volume of data
  • 5.6.3 Privacy
  • 6 Conclusion
  • Acronyms
  • Glossary
  • References
  • Chapter 4: Integration in the Internet of Things: A semantic middleware approach to seamless integration of heterogeneous t ...
  • 1 Introduction
  • 2 Motivating scenario
  • 3 Current approaches to integration in IoT
  • 3.1 Understanding the Integration Issues
  • 3.1.1 To connect: The connection problem
  • 3.1.2 To communicate: The understanding problem
  • 3.1.3 To range: The scalability problem
  • 3.1.4 To configure: The adaptation problem
  • 3.2 Approaches for Integration: The History
  • 3.3 Frameworks Based on Publish/Subscribe
  • 3.3.1 Challenges and open issues
  • 4 Design of an integration layer for P/S frameworks
  • 4.1 Integration Layer
  • 4.2 Adapters Design
  • 4.2.1 Structure dimension
  • 4.2.2 Interaction dimension
  • 4.2.3 Behavior dimension
  • 4.3 Adapters Implementation and Deployment
  • 4.3.1 Boundary layer
  • 4.3.2 Control layer
  • 4.3.3 Entity layer
  • 5 Example of adapters implementation and system deployment
  • 5.1 Publisher Adapter
  • 5.2 Subscriber Adapter
  • 5.3 Run-Time Modifications
  • 6 Conclusions and Future Work
  • Acronyms
  • Glossary
  • Acknowledgments
  • References
  • Part II: Context-aware/sensitive interactions and applications
  • Chapter 5: A context-aware system for efficient peer-to-peer content provision
  • 1 Introduction
  • 2 Related work and research motivation
  • 2.1 Effective Content Distribution Mechanisms
  • 2.2 Service Publication/Management
  • 2.3 Peer-to-Peer Network Instantiation Inside the Home Box
  • 2.4 Delivery Manager
  • 2.5 Publishing Manager
  • 2.6 Discovery Manager
  • 2.7 Streaming Gateway
  • 2.8 HTTP Gateway
  • 2.9 Conclusion
  • Acronyms
  • Glossary
  • Acknowledgments
  • References
  • Chapter 6: Transparent distributed data management in large scale distributed systems.
  • 1 Introduction and overview
  • 1.1 Main Objectives
  • 1.2 Contributions
  • 2 Mutual Exclusion
  • 2.1 Distributed Mutual Exclusion Algorithms
  • 2.2 The Naimi and T�rhel Algorithm
  • 2.3 Simultaneous Requests
  • 3 Algorithm for Exclusive Locks With Mobile Processes (ELMP)
  • 3.1 The Data Structure
  • 3.2 Atomic Operations
  • 3.3 Connecting to the System
  • 3.4 Balancing Strategies
  • 3.5 Balancing Following New Insertions
  • 3.6 Balancing Following a Token Request
  • 3.7 Balancing Following Departure
  • 3.8 The Proof of the ELMP Algorithm
  • 4 Read Write Locks for Mobile Processes (RW-LMP) Algorithm
  • 4.1 Handling Requests in the Linked-List
  • 4.2 Entering the Critical Section
  • 4.3 Leaving the Critical Section
  • 4.4 Leaving the System
  • 4.5 The Proof of the RW-LMP Algorithm
  • 5 Multi-Level Architecture and Data Abstraction
  • 5.1 The Basic Model of the DHO API
  • 5.1.1 DHO cooperation model
  • 5.1.2 The path of a DHO request
  • 5.2 Modeling of DHO Life Cycle
  • 5.3 Observed Delays
  • 5.4 Connection and Disconnection at Application Level
  • 5.5 Deviation From the Normal DHO Cycle
  • 6 Experimental Results
  • 6.1 DHO Cycle Evaluation With Asynchronous Locks
  • 6.2 Shared and Exclusive Requests
  • 6.3 DHO Cycle Evaluation With Mobility of Peers
  • 7 Discussion
  • 8 Conclusion
  • Acronyms
  • Glossary
  • References
  • Chapter 7: Converged information-centric spaces based on wireless data hubs
  • 1 Introduction
  • 2 Connectivity and congestion in dense wireless spaces
  • 2.1 Common Connectivity Problem
  • 2.2 Example Devices and Operation Modes
  • 2.3 Literature on Wireless Interference and Congestion
  • 2.4 Time Slot Division Method
  • 3 Group connect and wireless traffic offload
  • 3.1 Two Wireless Stacks
  • 3.2 Conventional Versus P2P WiFi Designs
  • 3.3 Basic Idea of Group Connect
  • 3.4 Traffic Offload Function of Group Connect.
  • 4 Wireless data hub
  • 4.1 Parameters for a Converged Information-Centric Space
  • 4.2 Wireless Data Hub
  • 4.3 WDH in the Center of a Social Wireless Network
  • 4.4 Maintenance in the No-Wires WDH Space
  • 5 Modeling and performance analysis
  • 5.1 Mobility Traces and Throughput Datasets
  • 5.2 Group Connect and WDH Deployment Models
  • 5.3 Group Connect Versus 4G Infrastructure
  • 5.4 Wireless Data Hub Versus 3G/LTE Spaces
  • 6 Summary
  • Acronyms
  • Glossary
  • References
  • Chapter 8: Data fusion for orientation sensing in wireless body area sensor networks using smart phones
  • 1 Introduction
  • 2 WBASN and E-Health Systems
  • 2.1 Data Aggregation and Data Fusion
  • 2.1.1 Data fusion algorithms
  • 2.2 Smart Phones for e-Health Monitoring
  • 3 Orientation sensing
  • 3.1 Sensor Data Fusion: A Layered Approach
  • 4 Orientation approximation
  • 4.1 Gyroscope Accelerometer Integration
  • 4.2 Complementary Filtering
  • 4.3 Kalman Filter
  • 5 Experimental setup
  • 6 Kalman and complementary filtering
  • 6.1 On Test Basis
  • 6.2 On Real-Time Data
  • 6.3 Comparison
  • 7 Discussion
  • 8 Conclusion and future works
  • Acronyms
  • Glossary
  • References
  • Part III: Ubiquitous services independent of devices/platforms
  • Chapter 9: Reuse of data from smart medical devices for quality control and evidence-based medicine
  • 1 Introduction
  • 2 State of the art of medical technology
  • 3 Obstacles to the adoption of smart medical devices in the practice
  • 4 Smart medical devices as complex cyber-physical systems
  • 5 Interdisciplinary approach versus mentality mismatch
  • 6 Necessity of postmarket surveillance
  • 7 Primary and secondary use of smart medical devices data
  • 7.1 Data for Health Support
  • 7.1.1 Data characteristics
  • 7.1.2 Data management
  • 7.2 Data for Quality Assurance
  • 7.2.1 Data characteristics
  • 7.2.2 Data management.

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