Pervasive Computing

Next Generation Platforms for Intelligent Data Collection
 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 6. Mai 2016
  • |
  • 548 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-803702-7 (ISBN)
 

Pervasive Computing: Next Generation Platforms for Intelligent Data Collection presents current advances and state-of-the-art work on methods, techniques, and algorithms designed to support pervasive collection of data under ubiquitous networks of devices able to intelligently collaborate towards common goals.

Using numerous illustrative examples and following both theoretical and practical results the authors discuss: a coherent and realistic image of today's architectures, techniques, protocols, components, orchestration, choreography, and developments related to pervasive computing components for intelligently collecting data, resource, and data management issues; the importance of data security and privacy in the era of big data; the benefits of pervasive computing and the development process for scientific and commercial applications and platforms to support them in this field.

Pervasive computing has developed technology that allows sensing, computing, and wireless communication to be embedded in everyday objects, from cell phones to running shoes, enabling a range of context-aware applications. Pervasive computing is supported by technology able to acquire and make use of the ubiquitous data sensed or produced by many sensors blended into our environment, designed to make available a wide range of new context-aware applications and systems. While such applications and systems are useful, the time has come to develop the next generation of pervasive computing systems. Future systems will be data oriented and need to support quality data, in terms of accuracy, latency and availability.

Pervasive Computing is intended as a platform for the dissemination of research efforts and presentation of advances in the pervasive computing area, and constitutes a flagship driver towards presenting and supporting advanced research in this area.


  • Offers a coherent and realistic image of today's architectures, techniques, protocols, components, orchestration, choreography, and development related to pervasive computing
  • Explains the state-of-the-art technological solutions necessary for the development of next-generation pervasive data systems, including: components for intelligently collecting data, resource and data management issues, fault tolerance, data security, monitoring and controlling big data, and applications for pervasive context-aware processing
  • Presents the benefits of pervasive computing, and the development process of scientific and commercial applications and platforms to support them in this field
  • Provides numerous illustrative examples and follows both theoretical and practical results to serve as a platform for the dissemination of research advances in the pervasive computing area
  • Englisch
  • San Francisco
  • |
  • USA
Elsevier Science
  • 31,82 MB
978-0-12-803702-7 (9780128037027)
0128037024 (0128037024)
weitere Ausgaben werden ermittelt
  • 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 Tréhel 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
  • 7.3 Data for Medical Research
  • 7.3.1 Data characteristics
  • 7.3.2 Data management
  • 8 Stakeholders and their interests
  • 9 Privacy versus quality of research
  • 10 Data integration platforms
  • 10.1 Existing Solutions
  • 10.2 Proposed Architecture
  • 10.2.1 General structure
  • 10.2.2 Technical support
  • Configurators
  • Operators (data brokers)
  • Call centers
  • 10.2.3 Research institute
  • 11 Technical challenges
  • 11.1 Stream Processing
  • 11.2 Stream Distribution
  • 12 Challenges for data analytics
  • 12.1 Clinical Trials for Medical Devices
  • 12.2 Problem Complexity-Example
  • 12.3 Quality of a Medical Device-Multiple Aspects
  • 12.4 Multiple Variants-Settings of a Device
  • 12.5 Variability of the Device-Software Updates
  • 12.6 Various Patient Groups, Unbalanced Cohort
  • 13 Responsibility for published results
  • 14 Background/Literature Review/Context
  • 15 Conclusion
  • Acronyms
  • Glossary
  • References
  • Chapter 10: Measuring energy efficiency in data centers
  • 1 Introduction
  • 2 Problem statement
  • 3 Criteria and methodology for the metrics selection
  • Power/energy metrics
  • Thermal metrics
  • Productivity metrics
  • 4 Power/energy metrics
  • 5 Thermal metrics
  • 6 Productivity metrics
  • 7 Challenges in data center energy efficiency metrics
  • Acknowledgments
  • Acronyms
  • Symbols
  • Technical Words
  • References
  • Chapter 11: Enhancing energy efficiency in buildings through innovative data analytics technologies
  • 1 Introduction
  • 2 Building energy modeling approaches
  • 3 Energy data management and mining systems
  • 4 Collecting and storing energy data
  • 5 Knowledge discovery process on building energy data
  • 5.1 Data Pre-Processing
  • 5.2 Data Segmentation
  • 5.3 Knowledge Discovery
  • 5.3.1 Clustering algorithms
  • 5.3.2 Classification algorithms
  • 5.3.3 Association rules
  • 5.4 Knowledge Exploitation
  • 6 Enhancing Energy Efficiency Through Data Analytics: Applications in Energy and Buildings
  • 6.1 The Prediction of Building Energy Consumption
  • 6.2 Energy Profiling in Buildings
  • 6.3 Fault Detection and Diagnosis
  • 6.4 Benchmarking
  • 6.5 Occupant Behavior
  • 7 Lessons Learned
  • 8 Conclusions and emerging trends in data analytics technologies
  • Acronyms
  • Glossary
  • Technical Words
  • References
  • Part IV: Pervasive computing and applications
  • Chapter 12: A failure detector based on processes' relevance and the confidence degree in the system for self-healing in u ...
  • 1 Introduction
  • 2 Background
  • 2.1 Self-Healing Property
  • 2.2 Unreliable Failure Detectors
  • 2.2.1 Implementation of failure detectors
  • 2.2.2 Estimation of heartbeat arrivals
  • 3 Motivation scenarios
  • 4 Self-healing module
  • 4.1 Failure Detector
  • 4.2 Adaptation Manager
  • 5 Impact failure detector
  • 6 Performance evaluation
  • 6.1 Environment
  • 6.2 QoS Metrics
  • 6.3 Set Configuration
  • 6.4 Experiments
  • 6.4.1 Experiment 1-query accuracy probability
  • 6.4.2 Experiment 2-detection time
  • 6.4.3 Experiment 3-average mistake rate
  • 7 Related work
  • 8 Conclusion and Future Work
  • Acronyms
  • Glossary
  • References
  • Chapter 13: Video streaming: Overview and challenges in the internet of things
  • 1 Introduction
  • 2 Architectures
  • 3 Streaming mechanisms with HTTP
  • 4 Content delivery networks
  • 5 Mobile P2P streaming
  • 6 Encoding
  • 7 Cloud based encoding and transcoding
  • 8 Internet of things
  • 8.1 Internet of Things Applications
  • 9 Low power personal area networks
  • 9.1 Evaluation of H.264 for LowPANS
  • 10 Conclusions
  • Acronyms
  • Glossary
  • References
  • Chapter 14: Extending cloud-based applications with mobile opportunistic networks: Security issues and privacy challenges
  • 1 Introduction
  • 2 Networking and functional challenges
  • 2.1 High Mobility of the Nodes
  • 2.2 Heterogeneity of Networks
  • 2.3 Device Constraints
  • 2.4 Unpredictable User Behavior
  • 2.5 Summary
  • 3 Data storage and resource management challenges
  • 3.1 Users' Authorization and Authentication
  • 3.2 User Identity
  • 3.3 Application-Level Security
  • 3.4 Data Storage Protection
  • 3.5 Risks in Multitenancy and Resource-Related Challenges
  • 3.6 Data Confidentiality
  • 3.7 Location-Related Privacy Issues
  • 3.8 Summary
  • 4 Future research questions
  • 5 Conclusion
  • Acronyms
  • Glossary
  • Acknowledgments
  • References
  • Glossary
  • Acronyms
  • Index
  • Back Cover

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