Tools, Languages, Methodologies for Representing Semantics on the Web of Things

Description

This book is a guide to the combination of the Internet of Things (IoT) and the Semantic Web, covering a variety of tools, technologies and applications that serve the myriad needs of the researchers in this field. It provides a multi dimensional view of the concepts, tools, techniques and issues that are involved in the development of semantics for the Web of Things. The various aspects studied in this book include Multi-Model Multi-Platform (SHM3P) databases for the IoT, clustering techniques for discovery services for the semantic IoT, dynamic security testing methods for the Semantic Web of Things, Semantic Web-enabled IoT integration for a smart city, IoT security issues, the role of the Semantic Web of Things in Industry 4.0, the integration of the Semantic Web and the IoT for e-health, smart healthcare systems to monitor patients, Semantic Web-based ontologies for the water domain, science fiction and searching for a job. Cover Title Page Copyright Page Contents Preface Chapter 1. The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT 1.1. Introduction 1.2. Databases for multi-model data 1.3. Platforms 1.4. Variations of SHM3P DBMS 1.5. What are the benefits of SHM3P databases for IoT? 1.5.1. Data storage and placement 1.5.2. Data processing 1.5.3. IoT applications 1.6. Summary and conclusions 1.7. References Chapter 2. A Systematic Review of Ontologies for the Water Domain 2.1. Introduction 2.2. Literature review 2.2.1. Features in the water domain 2.2.2. Semantic models in the water domain 2.2.3. A comprehensive review of ontologies in the water domain 2.3. Applications of ontologies in the water domain 2.4. Discussion and conclusion 2.5. References Chapter 3. Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation 3.1. Introduction 3.2. Background 3.2.1. Semantic Web 3.2.2. SSN (Semantic Sensor Network) ontology 3.3. IoT Smart Health applications and semantics 3.4. Proposed approach and implementation 3.4.1. Knowledge representation 3.4.2. Ontology evaluation 3.4.3. Reasoning and querying 3.4.4. Linked Data 3.5. Conclusion 3.6. References Chapter 4. Role of Clustering in Discovery Services for the Semantic Internet of Things 4.1. Introduction 4.2. Discovery services in IoT 4.2.1. Directory-based architectures 4.2.2. Directory-less architectures 4.3. Semantic-based architectures 4.3.1. Search engine-based 4.3.2. ONS DNS-based 4.4. Discovery services and clustering 4.5. Clustering methods in IoT 4.6. Conclusion 4.7. References Chapter 5. Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective 5.1. Introduction 5.2. Related studies 5.3. Background of dynamic security testing techniques 5.3.1. Black Box testing techniques 5.4. DAST using static analysis 5.4.1. Current implementation 5.5. DAST using user session 5.5.1. Current implementation 5.6. DAST using Extended Tainted Mode Model 5.6.1. Current implementation 5.7. Current issues and research directions 5.8. Conclusion 5.9. References Chapter 6. SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 6.1. Introduction 6.2. Literature survey 6.2.1. Formulation and modeling of ontologies for varied domains of importance 6.2.2. Auxiliary automatic and semi-automatic models in ontology synthesis 6.2.3. Ontology-driven systems and applications 6.2.4. Automatic Knowledge Acquisition systems 6.2.5. Science fiction as an independent domain of existence 6.3. Modeling and evaluation of the ontology 6.3.1. Ontology modeling 6.3.2. Ontology visualization 6.3.3. Ontology evaluation 6.4. Automatic Knowledge Acquisition model 6.4.1. System architecture 6.4.2. Acquisition algorithm 6.5. Conclusion 6.6. References Chapter 7. Semantic Web-Enabled IoT Integration for a Smart City 7.1. Introduction: Semantic Web and sensors 7.2. Motivation and challenge 7.3. Literature review 7.4. Implementation of forest planting using SPARQL queries 7.4.1. Architecture sketch with conceptual diagram 7.4.2. Implementation ontology from the dataset 7.4.3. Technologies and tools 7.5. Conclusion 7.6. References Chapter 8. Heart Rate Monitoring Using IoT and AI 8.1. Introduction 8.2. Literature survey 8.3. Heart rate monitoring system 8.4. Results and discussion 8.5. Conclusion and future works 8.6. References Chapter 9. IoT Security Issues and Its Defensive Methods 9.1. Introduction 9.2. IoT security architecture 9.2.1. Typical IoT architecture 9.2.2. Centralized and distributed approaches over the IoT security architecture 9.2.3. IoT security architecture based on blockchain 9.2.4. Internet of Things security architecture: trust zones and boundaries 9.2.5. Threat modeling in IoT security architecture 9.3. Specific security challenges and approaches 9.3.1. Identity and authentication 9.3.2. Access control 9.3.3. Protocol and network security 9.3.4. Privacy 9.3.5. Trust and governance 9.3.6. Fault tolerance 9.4. Methodologies used for securing the systems 9.4.1. PKI and digital certificates 9.4.2. Network security 9.4.3. API security 9.4.4. Network access control 9.4.5. Segmentation 9.4.6. Security gateways 9.4.7. Patch management and software updates 9.5. Conclusion 9.6. References Chapter 10. Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success 10.1. Introduction 10.2. Correlation of the Semantic Web of Things with IR4.0 10.2.1. Smart machines 10.2.2. Smart products 10.2.3. Augmented operators 10.2.4. The Web of Things 10.2.5. Semantic Web of Things 10.3. Smart manufacturing system and ontologies 10.3.1. Vertical level integration 10.3.2. Horizontal level of integration 10.3.3. End-to-end integration 10.4. Literature survey 10.5. Conclusion and future work 10.6. References Chapter 11. Semantic Web and Internet of Things in e-Health for Covid-19 11.1. Introduction 11.2. Dataset 11.3. Application of IoT for Covid-19 11.3.1. Continuous real-time remote monitoring 11.3.2. Remote monitoring using W-kit 11.3.3. Early identification and monitoring 11.3.4. Continuous and reliable health monitoring 11.3.5. ANN-assisted patient monitoring 11.3.6. City lockdown monitoring 11.3.7. Technologies for tracking and tracing 11.3.8. Tracking and tracing suspected cases 11.3.9. Anonymity preserving contact tracing model 11.3.10. Cognitive radio-based IoT architecture 11.3.11. Analyzing reasons for the outbreak 11.3.12. Analyzing Covid-19 cases using disruptive technology 11.3.13. Post-Covid applications 11.4. Semantic Web applications for Covid-19 11.4.1. Ontological approach for drug development 11.4.2. Early detection and diagnosis 11.4.3. Knowledge-based pre-diagnosis system 11.4.4. Semantic-based searching for online learning resources 11.4.5. Ontology-based physiological monitoring of students 11.4.6. Analysis of clinical trials 11.4.7. Data annotation of EHRs 11.4.8. Disease pattern study 11.4.9. Surveillance in primary care 11.4.10. Performance assessment of healthcare services 11.4.11. Vaccination drives and rollout strategies 11.5. Limitations and challenges of IoT and SW models 11.6. Discussion 11.7. Conclusion 11.8. References Chapter 12. Development of a Semantic Web Enabled Job_Search Ontology System 12.1. Introduction 12.1.1. Ontology 12.1.2. Importance of ontology 12.1.3. Semantic Web and its solutions 12.1.4. Online recruitment scenarios 12.2. Review of the related work done for online recruitment 12.3. Design of “SearchAJob” ontology for the IT domain 12.3.1. Ontology structure 12.4. Implementing the proposed ontology 12.4.1. Architecture of semantics-based job ontology 12.5. Benefits of Semantic Web enabled SearchAJob system 12.6. Conclusion and future scope 12.7. References List of Authors Index EULA