Adaptive middleware for the Internet of things : the GAMBAS approach

Adaptive middleware for the Internet of things : the GAMBAS approach /

Over the past years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of basic technology as well as innovative applications for the Internet of Things. Adaptive Middleware for the Internet of Things introduces a scalable, interoperable and pri...

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Bibliographic Details
Main Authors: Handte, Marcus (Author), Marrón, Pedro J. (Author), Schiele, Gregor (Author), Matoses, Manuel Serrano (Author)
Format: Electronic eBook
Language:English
Published: Gistrup, Denmark : River Publishers, [2019]
Series:River Publishers series in communications.
Subjects:
Internet of things.
Middleware.
Online Access: Full text (Emmanuel users only)
Table of Contents:
  • Front Cover; Half Title Page; RIVER PUBLISHERS SERIES IN COMMUNICATIONS; Title Page
  • Adaptive Middleware for the Internet of Things
  • The GAMBAS Approach; Copyright Page; Contents; Preface; List of Figures; List of Abbreviations; Chapter 1
  • Introduction; 1.1 Motivation; 1.2 GAMBAS Objectives; 1.3 Application Scenarios; 1.3.1 Mobility Scenario; 1.3.2 Environmental Scenario; 1.4 Overarching Vision; 1.4.1 Smart Cities; 1.4.2 Characteristics; 1.5 State of the Art; 1.5.1 Hardware Technologies; 1.5.1.1 Devices; 1.5.1.2 Communication; 1.5.1.3 Sensing; 1.5.1.4 Classification
  • 1.5.2 Communication Middleware1.5.3 Context Management; 1.5.4 Sensing Applications; 1.6 Innovations; Chapter 2
  • Architecture; 2.1 Static Perspective; 2.1.1 Operational View; 2.1.2 Component View; 2.1.3 Data View; 2.1.3.1 Data Access; 2.1.3.2 Data Representation; 2.1.3.3 Data Dynamics; 2.2 Dynamic Perspective; 2.2.1 Acquisition View; 2.2.1.1 Personal Data Acquisition; 2.2.1.2 Collaborative Data Acquisition; 2.2.2 Processing View; 2.2.2.1 Processing of Public Data; 2.2.2.2 Processing of Shared Data; 2.2.3 Inference View; 2.2.3.1 Local Inferences; 2.2.3.2 Distributed Inferences
  • 2.3 Interface Perspective2.3.1 Storage Interfaces; 2.3.2 Query Interfaces; 2.3.3 Privacy Interfaces; 2.3.4 Control Interfaces; Chapter 3
  • Data Acquisition; 3.1 Focus and Contribution; 3.1.1 Data Acquisition Frameworks; 3.1.2 Rapid Prototyping Tools; 3.1.3 Application-Specific Acquisition; 3.1.4 Contribution; 3.2 Data Acquisition Framework; 3.2.1 Component System; 3.2.1.1 Component Model; 3.2.1.1.1 Components; 3.2.1.1.2 Parameters; 3.2.1.1.3 Ports; 3.2.1.1.4 Connectors; 3.2.1.1.5 Configurations; 3.2.1.2 Runtime System; 3.2.1.2.1 System Structure; 3.2.1.2.2 Configuration Execution
  • 3.2.1.2.3 Platform Support3.2.1.3 Tool Support; 3.2.2 Activation System; 3.2.2.1 Activation Model; 3.2.2.1.1 States; 3.2.2.1.2 Transitions; 3.2.2.2 Runtime System; 3.2.2.2.1 System Structure; 3.2.2.2.2 Configuration Mapping; 3.2.2.2.3 Platform Support; 3.2.2.2.4 Tool Support; 3.3 Data Acquisition Components; 3.3.1 Context Recognition; 3.3.1.1 Location Recognition; 3.3.1.2 Trip Recognition; 3.3.1.3 Sound Recognition; 3.3.1.3.1 Noise Recognition; 3.3.1.3.2 Voice Tagging; 3.3.1.3.3 Voice Control; 3.3.2 Intent Recognition; 3.3.2.1 Duration Prediction; 3.3.2.2 Destination Prediction
  • 3.3.2.3 Prediction AlgorithmChapter 4
  • Data Processing; 4.1 Focus and Contribution; 4.1.1 Data Representation; 4.1.2 Query Processing; 4.1.3 Contribution; 4.2 Data Model; 4.2.1 Data Definition; 4.2.1.1 User Class; 4.2.1.2 Place Class; 4.2.1.3 Activity Class; 4.2.1.4 Journey Class; 4.2.1.5 TravelMode Class; 4.2.1.6 Bus Class; 4.2.1.7 Jogging Class; 4.2.1.8 Shopping Class; 4.2.2 Query Specification; 4.2.2.1 Queries on Users; 4.2.2.2 Queries on Buses; 4.3 Data Discovery; 4.3.1 Architecture; 4.3.2 Metadata Management; 4.3.2.1 Publishing Metadata; 4.3.3 Querying Data Sources

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