Shikha Mehta is Associate Professor in the Department of CSE & IT, Jaypee Institute of Information Technology, India. Her research interests include machine/deep learning algorithms, nature-inspired computing and social networks analytics.Sanju Tiwari is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.Patrick Siarry is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.

• Preface xiShikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBARChapter 1 The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT 1Sven GROPPE, Jinghua GROPPE and Tobias GROTH1.1 Introduction 11.2 Databases for multi-model data 51.3 Platforms 71.4 Variations of SHM3P DBMS 131.5 What are the benefits of SHM3P databases for IoT? 141.5.1 Data storage and placement 141.5.2 Data processing 151.5.3 IoT applications 151.6 Summary and conclusions 161.7 References 16Chapter 2 A Systematic Review of Ontologies for the Water Domain 21Sanju TIWARI and Raúl GARCÍA-CASTRO2.1 Introduction 212.2 Literature review 232.2.1 Features in the water domain 232.2.2 Semantic models in the water domain 242.2.3 A comprehensive review of ontologies in the water domain 242.3 Applications of ontologies in the water domain 322.4 Discussion and conclusion 352.5 References 36Chapter 3 Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation 41Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR3.1 Introduction 423.2 Background 433.2.1 Semantic Web 433.2.2 SSN (Semantic Sensor Network) ontology 443.3 IoT Smart Health applications and semantics 453.4 Proposed approach and implementation 463.4.1 Knowledge representation 473.4.2 Ontology evaluation 513.4.3 Reasoning and querying 513.4.4 Linked Data 553.5 Conclusion 563.6 References 57Chapter 4 Role of Clustering in Discovery Services for the Semantic Internet of Things 61Shachi SHARMA4.1 Introduction 614.2 Discovery services in IoT 644.2.1 Directory-based architectures 644.2.2 Directory-less architectures 664.3 Semantic-based architectures 674.3.1 Search engine-based 674.3.2 ONS DNS-based 684.4 Discovery services and clustering 684.5 Clustering methods in IoT 694.6 Conclusion 714.7 References 71Chapter 5 Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective 75Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG5.1 Introduction 755.2 Related studies 775.3 Background of dynamic security testing techniques 795.3.1 Black Box testing techniques 805.4 DAST using static analysis 825.4.1 Current implementation 825.5 DAST using user session 845.5.1 Current implementation 845.6 DAST using Extended Tainted Mode Model 865.6.1 Current implementation 875.7 Current issues and research directions 885.8 Conclusion 895.9 References 89Chapter 6 SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 93Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI6.1 Introduction 946.2 Literature survey 976.2.1 Formulation and modeling of ontologies for varied domains of importance 976.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 976.2.3 Ontology-driven systems and applications 986.2.4 Automatic Knowledge Acquisition systems 996.2.5 Science fiction as an independent domain of existence 996.3 Modeling and evaluation of the ontology 1006.3.1 Ontology modeling 1006.3.2 Ontology visualization 1046.3.3 Ontology evaluation 1076.4 Automatic Knowledge Acquisition model 1116.4.1 System architecture 1116.4.2 Acquisition algorithm 1136.5 Conclusion 1196.6 References 119Chapter 7 Semantic Web-Enabled IoT Integration for a Smart City 123Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ7.1 Introduction: Semantic Web and sensors 1237.2 Motivation and challenge 1247.3 Literature review 1247.4 Implementation of forest planting using SPARQL queries 1257.4.1 Architecture sketch with conceptual diagram 1257.4.2 Implementation ontology from the dataset 1267.4.3 Technologies and tools 1297.5 Conclusion 1367.6 References 136Chapter 8 Heart Rate Monitoring Using IoT and AI 139Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY8.1 Introduction 1408.2 Literature survey 1428.3 Heart rate monitoring system 1458.4 Results and discussion 1498.5 Conclusion and future works 1528.6 References 152Chapter 9 IoT Security Issues and Its Defensive Methods 155Keshavi NALLA and Seshu VARDHAN POTHABATHULA9.1 Introduction 1559.2 IoT security architecture 1589.2.1 Typical IoT architecture 1589.2.2 Centralized and distributed approaches over the IoT security architecture 1619.2.3 IoT security architecture based on blockchain 1639.2.4 Internet of Things security architecture: trust zones and boundaries 1649.2.5 Threat modeling in IoT security architecture 1689.3 Specific security challenges and approaches 1709.3.1 Identity and authentication 1709.3.2 Access control 1719.3.3 Protocol and network security 1729.3.4 Privacy 1729.3.5 Trust and governance 1739.3.6 Fault tolerance 1739.4 Methodologies used for securing the systems 1749.4.1 PKI and digital certificates 1749.4.2 Network security 1749.4.3 API security 1749.4.4 Network access control 1759.4.5 Segmentation 1759.4.6 Security gateways 1759.4.7 Patch management and software updates 1759.5 Conclusion 1769.6 References 176Chapter 10 Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success 179Deepika CHAUDHARY and Jaiteg SINGH10.1 Introduction 17910.2 Correlation of the Semantic Web of Things with IR4.0 18010.2.1 Smart machines 18110.2.2 Smart products 18210.2.3 Augmented operators 18210.2.4 The Web of Things 18310.2.5 Semantic Web of Things 18410.3 Smart manufacturing system and ontologies 18510.3.1 Vertical level integration 18510.3.2 Horizontal level of integration 18510.3.3 End-to-end integration 18510.4 Literature survey 18810.5 Conclusion and future work 19010.6 References 190Chapter 11 Semantic Web and Internet of Things in e-Health for Covid-19 195ANURAG and Naren JEEVA11.1 Introduction 19611.2 Dataset 19711.3 Application of IoT for Covid-19 19811.3.1 Continuous real-time remote monitoring 19811.3.2 Remote monitoring using W-kit 19811.3.3 Early identification and monitoring 19811.3.4 Continuous and reliable health monitoring 19811.3.5 ANN-assisted patient monitoring 19911.3.6 City lockdown monitoring 19911.3.7 Technologies for tracking and tracing 19911.3.8 Tracking and tracing suspected cases 19911.3.9 Anonymity preserving contact tracing model 20011.3.10 Cognitive radio-based IoT architecture 20011.3.11 Analyzing reasons for the outbreak 20011.3.12 Analyzing Covid-19 cases using disruptive technology 20011.3.13 Post-Covid applications 20111.4 Semantic Web applications for Covid-19 20111.4.1 Ontological approach for drug development 20211.4.2 Early detection and diagnosis 20211.4.3 Knowledge-based pre-diagnosis system 20211.4.4 Semantic-based searching for online learning resources 20311.4.5 Ontology-based physiological monitoring of students 20311.4.6 Analysis of clinical trials 20311.4.7 Data annotation of EHRs 20411.4.8 Disease pattern study 20411.4.9 Surveillance in primary care 20411.4.10 Performance assessment of healthcare services 20511.4.11 Vaccination drives and rollout strategies 20511.5 Limitations and challenges of IoT and SW models 20511.6 Discussion 20611.7 Conclusion 20611.8 References 207Chapter 12 Development of a Semantic Web Enabled Job_Search Ontology System 211Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL12.1 Introduction 21112.1.1 Ontology 21212.1.2 Importance of ontology 21312.1.3 Semantic Web and its solutions 21412.1.4 Online recruitment scenarios 21412.2 Review of the related work done for online recruitment 21512.3 Design of “SearchAJob” ontology for the IT domain 21712.3.1 Ontology structure 21812.4 Implementing the proposed ontology 22212.4.1 Architecture of semantics-based job ontology 22312.5 Benefits of Semantic Web enabled SearchAJob system 23112.6 Conclusion and future scope 23212.7 References 233List of Authors 237Index 241

Shikha Mehta is Associate Professor in the Department of CSE & IT, Jaypee Institute of Information Technology, India. Her research interests include machine/deep learning algorithms, nature-inspired computing and social networks analytics.Sanju Tiwari is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.Patrick Siarry is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.

• Preface xiShikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBARChapter 1 The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT 1Sven GROPPE, Jinghua GROPPE and Tobias GROTH1.1 Introduction 11.2 Databases for multi-model data 51.3 Platforms 71.4 Variations of SHM3P DBMS 131.5 What are the benefits of SHM3P databases for IoT? 141.5.1 Data storage and placement 141.5.2 Data processing 151.5.3 IoT applications 151.6 Summary and conclusions 161.7 References 16Chapter 2 A Systematic Review of Ontologies for the Water Domain 21Sanju TIWARI and Raúl GARCÍA-CASTRO2.1 Introduction 212.2 Literature review 232.2.1 Features in the water domain 232.2.2 Semantic models in the water domain 242.2.3 A comprehensive review of ontologies in the water domain 242.3 Applications of ontologies in the water domain 322.4 Discussion and conclusion 352.5 References 36Chapter 3 Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation 41Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR3.1 Introduction 423.2 Background 433.2.1 Semantic Web 433.2.2 SSN (Semantic Sensor Network) ontology 443.3 IoT Smart Health applications and semantics 453.4 Proposed approach and implementation 463.4.1 Knowledge representation 473.4.2 Ontology evaluation 513.4.3 Reasoning and querying 513.4.4 Linked Data 553.5 Conclusion 563.6 References 57Chapter 4 Role of Clustering in Discovery Services for the Semantic Internet of Things 61Shachi SHARMA4.1 Introduction 614.2 Discovery services in IoT 644.2.1 Directory-based architectures 644.2.2 Directory-less architectures 664.3 Semantic-based architectures 674.3.1 Search engine-based 674.3.2 ONS DNS-based 684.4 Discovery services and clustering 684.5 Clustering methods in IoT 694.6 Conclusion 714.7 References 71Chapter 5 Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective 75Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG5.1 Introduction 755.2 Related studies 775.3 Background of dynamic security testing techniques 795.3.1 Black Box testing techniques 805.4 DAST using static analysis 825.4.1 Current implementation 825.5 DAST using user session 845.5.1 Current implementation 845.6 DAST using Extended Tainted Mode Model 865.6.1 Current implementation 875.7 Current issues and research directions 885.8 Conclusion 895.9 References 89Chapter 6 SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 93Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI6.1 Introduction 946.2 Literature survey 976.2.1 Formulation and modeling of ontologies for varied domains of importance 976.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 976.2.3 Ontology-driven systems and applications 986.2.4 Automatic Knowledge Acquisition systems 996.2.5 Science fiction as an independent domain of existence 996.3 Modeling and evaluation of the ontology 1006.3.1 Ontology modeling 1006.3.2 Ontology visualization 1046.3.3 Ontology evaluation 1076.4 Automatic Knowledge Acquisition model 1116.4.1 System architecture 1116.4.2 Acquisition algorithm 1136.5 Conclusion 1196.6 References 119Chapter 7 Semantic Web-Enabled IoT Integration for a Smart City 123Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ7.1 Introduction: Semantic Web and sensors 1237.2 Motivation and challenge 1247.3 Literature review 1247.4 Implementation of forest planting using SPARQL queries 1257.4.1 Architecture sketch with conceptual diagram 1257.4.2 Implementation ontology from the dataset 1267.4.3 Technologies and tools 1297.5 Conclusion 1367.6 References 136Chapter 8 Heart Rate Monitoring Using IoT and AI 139Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY8.1 Introduction 1408.2 Literature survey 1428.3 Heart rate monitoring system 1458.4 Results and discussion 1498.5 Conclusion and future works 1528.6 References 152Chapter 9 IoT Security Issues and Its Defensive Methods 155Keshavi NALLA and Seshu VARDHAN POTHABATHULA9.1 Introduction 1559.2 IoT security architecture 1589.2.1 Typical IoT architecture 1589.2.2 Centralized and distributed approaches over the IoT security architecture 1619.2.3 IoT security architecture based on blockchain 1639.2.4 Internet of Things security architecture: trust zones and boundaries 1649.2.5 Threat modeling in IoT security architecture 1689.3 Specific security challenges and approaches 1709.3.1 Identity and authentication 1709.3.2 Access control 1719.3.3 Protocol and network security 1729.3.4 Privacy 1729.3.5 Trust and governance 1739.3.6 Fault tolerance 1739.4 Methodologies used for securing the systems 1749.4.1 PKI and digital certificates 1749.4.2 Network security 1749.4.3 API security 1749.4.4 Network access control 1759.4.5 Segmentation 1759.4.6 Security gateways 1759.4.7 Patch management and software updates 1759.5 Conclusion 1769.6 References 176Chapter 10 Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success 179Deepika CHAUDHARY and Jaiteg SINGH10.1 Introduction 17910.2 Correlation of the Semantic Web of Things with IR4.0 18010.2.1 Smart machines 18110.2.2 Smart products 18210.2.3 Augmented operators 18210.2.4 The Web of Things 18310.2.5 Semantic Web of Things 18410.3 Smart manufacturing system and ontologies 18510.3.1 Vertical level integration 18510.3.2 Horizontal level of integration 18510.3.3 End-to-end integration 18510.4 Literature survey 18810.5 Conclusion and future work 19010.6 References 190Chapter 11 Semantic Web and Internet of Things in e-Health for Covid-19 195ANURAG and Naren JEEVA11.1 Introduction 19611.2 Dataset 19711.3 Application of IoT for Covid-19 19811.3.1 Continuous real-time remote monitoring 19811.3.2 Remote monitoring using W-kit 19811.3.3 Early identification and monitoring 19811.3.4 Continuous and reliable health monitoring 19811.3.5 ANN-assisted patient monitoring 19911.3.6 City lockdown monitoring 19911.3.7 Technologies for tracking and tracing 19911.3.8 Tracking and tracing suspected cases 19911.3.9 Anonymity preserving contact tracing model 20011.3.10 Cognitive radio-based IoT architecture 20011.3.11 Analyzing reasons for the outbreak 20011.3.12 Analyzing Covid-19 cases using disruptive technology 20011.3.13 Post-Covid applications 20111.4 Semantic Web applications for Covid-19 20111.4.1 Ontological approach for drug development 20211.4.2 Early detection and diagnosis 20211.4.3 Knowledge-based pre-diagnosis system 20211.4.4 Semantic-based searching for online learning resources 20311.4.5 Ontology-based physiological monitoring of students 20311.4.6 Analysis of clinical trials 20311.4.7 Data annotation of EHRs 20411.4.8 Disease pattern study 20411.4.9 Surveillance in primary care 20411.4.10 Performance assessment of healthcare services 20511.4.11 Vaccination drives and rollout strategies 20511.5 Limitations and challenges of IoT and SW models 20511.6 Discussion 20611.7 Conclusion 20611.8 References 207Chapter 12 Development of a Semantic Web Enabled Job_Search Ontology System 211Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL12.1 Introduction 21112.1.1 Ontology 21212.1.2 Importance of ontology 21312.1.3 Semantic Web and its solutions 21412.1.4 Online recruitment scenarios 21412.2 Review of the related work done for online recruitment 21512.3 Design of “SearchAJob” ontology for the IT domain 21712.3.1 Ontology structure 21812.4 Implementing the proposed ontology 22212.4.1 Architecture of semantics-based job ontology 22312.5 Benefits of Semantic Web enabled SearchAJob system 23112.6 Conclusion and future scope 23212.7 References 233List of Authors 237Index 241

Shikha Mehta is Associate Professor in the Department of CSE & IT, Jaypee Institute of Information Technology, India. Her research interests include machine/deep learning algorithms, nature-inspired computing and social networks analytics.Sanju Tiwari is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.Patrick Siarry is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.

• Preface xiShikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBARChapter 1 The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT 1Sven GROPPE, Jinghua GROPPE and Tobias GROTH1.1 Introduction 11.2 Databases for multi-model data 51.3 Platforms 71.4 Variations of SHM3P DBMS 131.5 What are the benefits of SHM3P databases for IoT? 141.5.1 Data storage and placement 141.5.2 Data processing 151.5.3 IoT applications 151.6 Summary and conclusions 161.7 References 16Chapter 2 A Systematic Review of Ontologies for the Water Domain 21Sanju TIWARI and Raúl GARCÍA-CASTRO2.1 Introduction 212.2 Literature review 232.2.1 Features in the water domain 232.2.2 Semantic models in the water domain 242.2.3 A comprehensive review of ontologies in the water domain 242.3 Applications of ontologies in the water domain 322.4 Discussion and conclusion 352.5 References 36Chapter 3 Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation 41Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR3.1 Introduction 423.2 Background 433.2.1 Semantic Web 433.2.2 SSN (Semantic Sensor Network) ontology 443.3 IoT Smart Health applications and semantics 453.4 Proposed approach and implementation 463.4.1 Knowledge representation 473.4.2 Ontology evaluation 513.4.3 Reasoning and querying 513.4.4 Linked Data 553.5 Conclusion 563.6 References 57Chapter 4 Role of Clustering in Discovery Services for the Semantic Internet of Things 61Shachi SHARMA4.1 Introduction 614.2 Discovery services in IoT 644.2.1 Directory-based architectures 644.2.2 Directory-less architectures 664.3 Semantic-based architectures 674.3.1 Search engine-based 674.3.2 ONS DNS-based 684.4 Discovery services and clustering 684.5 Clustering methods in IoT 694.6 Conclusion 714.7 References 71Chapter 5 Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective 75Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG5.1 Introduction 755.2 Related studies 775.3 Background of dynamic security testing techniques 795.3.1 Black Box testing techniques 805.4 DAST using static analysis 825.4.1 Current implementation 825.5 DAST using user session 845.5.1 Current implementation 845.6 DAST using Extended Tainted Mode Model 865.6.1 Current implementation 875.7 Current issues and research directions 885.8 Conclusion 895.9 References 89Chapter 6 SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 93Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI6.1 Introduction 946.2 Literature survey 976.2.1 Formulation and modeling of ontologies for varied domains of importance 976.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 976.2.3 Ontology-driven systems and applications 986.2.4 Automatic Knowledge Acquisition systems 996.2.5 Science fiction as an independent domain of existence 996.3 Modeling and evaluation of the ontology 1006.3.1 Ontology modeling 1006.3.2 Ontology visualization 1046.3.3 Ontology evaluation 1076.4 Automatic Knowledge Acquisition model 1116.4.1 System architecture 1116.4.2 Acquisition algorithm 1136.5 Conclusion 1196.6 References 119Chapter 7 Semantic Web-Enabled IoT Integration for a Smart City 123Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ7.1 Introduction: Semantic Web and sensors 1237.2 Motivation and challenge 1247.3 Literature review 1247.4 Implementation of forest planting using SPARQL queries 1257.4.1 Architecture sketch with conceptual diagram 1257.4.2 Implementation ontology from the dataset 1267.4.3 Technologies and tools 1297.5 Conclusion 1367.6 References 136Chapter 8 Heart Rate Monitoring Using IoT and AI 139Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY8.1 Introduction 1408.2 Literature survey 1428.3 Heart rate monitoring system 1458.4 Results and discussion 1498.5 Conclusion and future works 1528.6 References 152Chapter 9 IoT Security Issues and Its Defensive Methods 155Keshavi NALLA and Seshu VARDHAN POTHABATHULA9.1 Introduction 1559.2 IoT security architecture 1589.2.1 Typical IoT architecture 1589.2.2 Centralized and distributed approaches over the IoT security architecture 1619.2.3 IoT security architecture based on blockchain 1639.2.4 Internet of Things security architecture: trust zones and boundaries 1649.2.5 Threat modeling in IoT security architecture 1689.3 Specific security challenges and approaches 1709.3.1 Identity and authentication 1709.3.2 Access control 1719.3.3 Protocol and network security 1729.3.4 Privacy 1729.3.5 Trust and governance 1739.3.6 Fault tolerance 1739.4 Methodologies used for securing the systems 1749.4.1 PKI and digital certificates 1749.4.2 Network security 1749.4.3 API security 1749.4.4 Network access control 1759.4.5 Segmentation 1759.4.6 Security gateways 1759.4.7 Patch management and software updates 1759.5 Conclusion 1769.6 References 176Chapter 10 Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success 179Deepika CHAUDHARY and Jaiteg SINGH10.1 Introduction 17910.2 Correlation of the Semantic Web of Things with IR4.0 18010.2.1 Smart machines 18110.2.2 Smart products 18210.2.3 Augmented operators 18210.2.4 The Web of Things 18310.2.5 Semantic Web of Things 18410.3 Smart manufacturing system and ontologies 18510.3.1 Vertical level integration 18510.3.2 Horizontal level of integration 18510.3.3 End-to-end integration 18510.4 Literature survey 18810.5 Conclusion and future work 19010.6 References 190Chapter 11 Semantic Web and Internet of Things in e-Health for Covid-19 195ANURAG and Naren JEEVA11.1 Introduction 19611.2 Dataset 19711.3 Application of IoT for Covid-19 19811.3.1 Continuous real-time remote monitoring 19811.3.2 Remote monitoring using W-kit 19811.3.3 Early identification and monitoring 19811.3.4 Continuous and reliable health monitoring 19811.3.5 ANN-assisted patient monitoring 19911.3.6 City lockdown monitoring 19911.3.7 Technologies for tracking and tracing 19911.3.8 Tracking and tracing suspected cases 19911.3.9 Anonymity preserving contact tracing model 20011.3.10 Cognitive radio-based IoT architecture 20011.3.11 Analyzing reasons for the outbreak 20011.3.12 Analyzing Covid-19 cases using disruptive technology 20011.3.13 Post-Covid applications 20111.4 Semantic Web applications for Covid-19 20111.4.1 Ontological approach for drug development 20211.4.2 Early detection and diagnosis 20211.4.3 Knowledge-based pre-diagnosis system 20211.4.4 Semantic-based searching for online learning resources 20311.4.5 Ontology-based physiological monitoring of students 20311.4.6 Analysis of clinical trials 20311.4.7 Data annotation of EHRs 20411.4.8 Disease pattern study 20411.4.9 Surveillance in primary care 20411.4.10 Performance assessment of healthcare services 20511.4.11 Vaccination drives and rollout strategies 20511.5 Limitations and challenges of IoT and SW models 20511.6 Discussion 20611.7 Conclusion 20611.8 References 207Chapter 12 Development of a Semantic Web Enabled Job_Search Ontology System 211Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL12.1 Introduction 21112.1.1 Ontology 21212.1.2 Importance of ontology 21312.1.3 Semantic Web and its solutions 21412.1.4 Online recruitment scenarios 21412.2 Review of the related work done for online recruitment 21512.3 Design of “SearchAJob” ontology for the IT domain 21712.3.1 Ontology structure 21812.4 Implementing the proposed ontology 22212.4.1 Architecture of semantics-based job ontology 22312.5 Benefits of Semantic Web enabled SearchAJob system 23112.6 Conclusion and future scope 23212.7 References 233List of Authors 237Index 241