Semantic IoT: Theory and Applications: Interoperability, Provenance and Beyond (Studies in Computational Intelligence, 941)

✍ Scribed by Rajiv Pandey (editor), Marcin Paprzycki (editor), Nidhi Srivastava (editor), Subhash Bhalla (editor), Katarzyna Wasielewska-Michniewska (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 424
• Category: Library

✦ Synopsis

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments.

Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality?

Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

✦ Table of Contents

Preface
Contents
Fundamentals
Semantic Web and IoT
1 Introduction
2 Applications, Related Work and Research Challenges in Semantic Web and IoT
2.1 Applications on Different Domains
2.2 Main Research Challenges
3 IoT Knowledge Representation with Semantic Web Technologies
3.1 Modelling Sensors
3.2 Modelling Multi-modal Events and Observations
3.3 Ontology-Based Reasoning
4 The Semantic Web of Things and How It Augments the IoT
5 Conclusion
References
Semantic Web Technologies
1 Introduction
2 Semantic Web-Future of Internet
2.1 Linked Open Data (LOD)
2.2 Semantic Metadata
3 Semantic Web Technologies
3.1 Explicit Metadata
3.2 Ontologies
3.3 RDF
3.4 RDF Schema
3.5 OWL
3.6 Logic
3.7 Agents
4 The Semantic Web Stack
5 Provenance in Semantic Web
6 Semantic Web Implementations and Applications
6.1 Software Agents
6.2 Semantic Desktop
6.3 Geospatial Semantic Web
6.4 Semantic Web in Agriculture
6.5 Semantic Web in Healthcare
6.6 Semantic Web and IoT
7 Conclusion
References
A Look at Semantic Web Technology and the Potential Semantic Web Search in the Modern Era
1 Introduction
2 Semantic Web
3 Semantic Web Technologies
3.1 HTML
3.2 XML
3.3 RDF
3.4 Linked Data and Open Data
4 Ontology
4.1 OWL
4.2 FOAF and SPARQL
5 Architecture Semantic Web
6 Discussion
7 Trends
8 Conclusions
References
Semantic IoT: The Key to Realizing IoT Value
1 Introduction
2 Semantic Internet of Things (SIoT)
3 Semantic Interoperability (SI)
3.1 Issues of Semantic Interoperability (SI)
4 Semantic IoT Versus Machine Learning
5 Semantic Ontology
6 Network Tools for Efficient SIoT
7 Security Policy in SIoT
8 Conclusions
References
Provenance Data Models and Assertions: A Demonstrative Approach
1 Introduction
2 Trust in Semantic Web
3 Provenance: Types and Models
3.1 Data and Workflow Provenance
4 Provenance Models
4.1 OPM Model Leading to PROV-DM
4.2 PROV-DM Data Model
5 The Provenance Architecture
5.1 PROV-DM
5.2 PROV-N, PROV-O, PROV-XML
5.3 PROV-CONSTRAINTS and PROV-SEM
5.4 PROV-DICTIONARY and PROV-LINKS
5.5 PROV-AQ
5.6 ROV-DC
5.7 ProvONE
6 Tools for Capturing Provenance
6.1 Karma
6.2 Taverna
6.3 Protégé
6.4 CamFlow
6.5 Kepler
6.6 Linux Provenance Modules
6.7 Open Provenance Model
6.8 ProvStore
7 Provenance Implementation in University Ontology Using Basic Constructs of PROV-DM
8 Embedding Assertions: A Scenario-Based Approach
8.1 Provenance Assertions for Entity
8.2 Provenance Assertions for Agent
8.3 Provenance Assertions for Activity
9 Scenario Assertions for Provenance Representation
9.1 Scenario: ActedOnBehalfOf Provenance Assertion of an Agent
9.2 Scenario: WasAttributedTo, Provenance Assertion of an Activity
10 Conclusions
References
IoT Data and Interoperability
Need and Relevance of Common Vocabularies and Ontologies in IoT Domain
1 Introduction
2 Vocabulary
2.1 Vocabulary Tools
3 Ontology
3.1 Components of Ontology
3.2 Types of Ontology
3.3 IoT Ontologies
3.4 Advantages of Ontology
3.5 Restrictions of Ontology
4 Need of Vocabulary and Ontology
5 Ontology Quality Methodology
6 Application
7 Background
8 IoT-Related Ontologies
9 Semantic Web
9.1 Difficulties
9.2 Principles
9.3 Parts
9.4 The Semantic Web Stack
9.5 Applications
9.6 The Semantic Web Stack for the IoT
10 Ontologies for Smart Cities
10.1 Measures to Compare Smart Cities and IoT Ontologies
11 Future Trends and Conclusion
References
PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies
1 Introduction
2 Improving Ontology Quality, Accessibility and Knowledge Classification
2.1 Encouraging the Reuse of Knowledge Through Ontology Best Practices
2.2 Disseminating Tools for Ontology Quality
2.3 Development Time Optimization and Ontology Improvement
2.4 Semantic Ontology Interoperability Methodology
3 Related Work
3.1 Existing Surveys
3.2 Ontology Methodologies and Ontology Design Patterns (ODPs)
3.3 Ontology Evaluation
3.4 Ontology Metrics
3.5 Ontology Quality
3.6 Limitations of the Related Work
4 PerfectO: Architecture and Implementation
4.1 PerfectO Architecture
4.2 Ontology Improvement Methodology
4.3 Use Cases
4.4 Limitations
5 Evaluation, Lessons Learned and Discussions
5.1 Ontology Quality Evaluation
5.2 Semantic Web Community Evaluation Criteria
5.3 Discussions and Lessons Learned
6 Conclusion and Future Work
7 Appendix
7.1 Catalogs of Tools
7.2 Dr. PerfectO Availability of Tools (DPAT)
7.3 PerfectO Guidance: The Most Accessible Tools for Ontology Engineering
References
Discovering Critical Factors Affecting RDF Stores Success
1 Introduction
2 Related Works
3 The Methodological Approach
3.1 Analysis of the Case Studies
3.2 Selection of the Critical Success Factors
3.3 Analysis of the Critical Success Factors
4 Conclusions
References
Creation of Ontological Knowledge Bases in the Semantic Web by Analyzing Table Structures
1 Goal and Objectives of the Research
2 Comparative Analysis of Modern Approaches to the Formation of Ontological Knowledge Bases in Semantic Web Systems
3 The Methodological Basis for the Presentation of Table Structures as Sources of Knowledge in Semantic Web Systems
4 Method for the Formation of Ontological Knowledge Bases in Semantic Web Systems Based on Targeted Enumeration
5 Formal Model of Table Structure Knowledge Sources
6 The Method of Analysis of the Sources of Knowledge of Table Structures Based on Targeted Enumeration
7 Experimental Studies of the Effectiveness of the Method of Targeted Enumeration in the Formation of OKB
8 Conclusion
References
Semantic Techniques to Support IoT Interoperability
1 Introduction
2 State of the Art
2.1 Semantic Technologies
3 The Methodology
4 The API Analysis
5 The API Graph
6 The WSDL and OWL-S Representations
7 Conclusions and Future Work
References
Semantic IoT Interoperability and Data Analytics Using Machine Learning in Healthcare Sector
1 Introduction
2 Literature Review
3 Layered Framework of Interoperability in IoT
3.1 IoT Interoperability Challenges
3.2 Web Ontology Framework for Semantic Interoperability in IoT
3.3 Conceptual Graphs
4 Methodology for Classifying Semantic Data Using Machine Learning
4.1 Vocabulary Associated with Healthcare Perspective
4.2 Data Collection and Structure
4.3 Classification
4.4 Semantic Analysis
4.5 Results and Discussions
4.6 Analyze and Visualize Text Using N-Gram Frequency Counts
5 Conclusion
References
Domain-Specific Applications
SAGRO-Lite: A Light Weight Agent Based Semantic Model for the Internet of Things for Smart Agriculture in Developing Countries
1 Introduction
1.1 Popularity of IoT
1.2 Indian Agriculture and Farmers—Problems and Reforms
1.3 Importance of Agriculture in Indian Economy
1.4 Characteristics and Problems of Indian Agriculture
1.5 Solution to Problems of Farmers
2 IoT and Its Potential for Agriculture
2.1 IoT Functional Blocks
2.2 IoT Agriculture Framework
2.3 IoT Based Agriculture Applications
3 IoT Equipments
3.1 Existing IoT Products for Smart Farming
3.2 Multi-agent System Architecture
4 Proposed Model
4.1 Agent Based Semantic Model for Smart Agriculture (ABSMSA)
5 Design and Development of ABSMSA
5.1 Goal Diagram
5.2 Role Diagram
5.3 Agent Diagram
5.4 Protocol Diagram
6 Ontologies Used in ABSMSA
6.1 IOT-Lite
6.2 Complex Event Service Ontology
6.3 SAGRO-Lite
7 Scenario
8 Future Trends and Conclusion
References
How to Understand Better ``Smart Vehicle''? Knowledge Extraction for the Automotive Sector Using Web of Things
1 Highlights
2 Introduction
3 Background and Related Work
4 Knowledge Extraction for the Automotive Sector (KEAS) Methodology
4.1 Survey Methodology to Collect Ontologies for Smart Vehicles
4.2 Building the Corpus of Knowledge for the Transportation Domain
5 Evaluation
6 Conclusion and Future Work
7 Appendix
7.1 Clustering Results
References
IoT Semantic Interoperability for Active and Healthy Ageing
1 Introduction
2 Semantic Interoperability
2.1 Methods for the Achievement of Semantic Interoperability
2.2 Semantic Interoperability in IoT
2.3 Semantic Interoperability in AHA
3 USE CASE: The ACTIVAGE European Smart Homes
3.1 Overview
3.2 Interoperability Goals in ACTIVAGE
3.3 AHA Scenarios
3.4 ACTIVAGE Technical Approach for Semantic Interoperability
3.5 Application and Impact of the Semantic Framework
4 Conclusions
References
IoT in Provenance Management of Medical Data
1 Introduction
2 Review of Related Work
3 Medical Data Provenance
3.1 Dataflow of Medical Data
3.2 Medical Data Formats
3.3 Analysis of the Proposed Solution for Medical Data Provenance
4 Provenance of Data and Reliability of Calibrators of Melatonin-Sulfate: Case Study
5 Conclusions
References
Problem-Specific Applications
Semantic Localization for IoT
1 Location as IoT Context
1.1 Designing a Robo-Cafe
1.2 Spatial Ontologies
1.3 Semantic Technologies
1.4 Standards for Spatial Representation
2 Location Modeling
2.1 Model Theory
2.2 Semantic Localization
2.3 Physical and Relational Ontologies
2.4 Formalizing Open Ontologies
3 Logical Inference on Ontologies
4 Related Formal Structures from AI and Robotics
5 Conclusion
References
IFTTT Rely Based a Semantic Web Approach to Simplifying Trigger-Action Programming for End-User Application with IoT Applications
1 Introduction
2 Background
3 IoT Gateways Architecture Based Semantic Web-Based Approach
4 Communication Technologies and Protocol
4.1 Exclusive Advances
4.2 Short-Extend Advancements
4.3 Long-Extend Innovations
5 Future of IFTTT (if This then that)
6 Dimensions for Interoperability
7 EUPont Based Semantic Model for END User Application
8 Conclusion and Future Scope
References
Semantic Internet of Things (IoT) Interoperability Using Software Defined Network (SDN) and Network Function Virtualization (NFV)
1 Introduction
2 Internet of Things (IoT)
3 Semantic IoT Interoperability Terminologies
4 SDN (Software Defined Network)
5 NFV (Network Function Virtualization)
6 Cloud Technologies
7 Solutions for Semantic Interoperability for IoT
7.1 Cloud Based Solution
7.2 SDN Based Solution
7.3 NFV Based Solution
7.4 Combined Solution of SDN and NFV
7.5 Graph Structure
8 Conclusion
9 Future Work
References