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<p>Preface xi<br /><i>Shikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBAR</i></p> <p><b>Chapter 1 The Role of Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases for IoT </b><b>1<br /></b><i>Sven GROPPE, Jinghua GROPPE and Tobias GROTH</i></p> <p>1.1 Introduction 1</p> <p>1.2 Databases for multi-model data 5</p> <p>1.3 Platforms 7</p> <p>1.4 Variations of SHM3P DBMS 13</p> <p>1.5 What are the benefits of SHM3P databases for IoT? 14</p> <p>1.5.1 Data storage and placement 14</p> <p>1.5.2 Data processing 15</p> <p>1.5.3 IoT applications 15</p> <p>1.6 Summary and conclusions 16</p> <p>1.7 References 16</p> <p><b>Chapter 2 A Systematic Review of Ontologies for the Water Domain </b><b>21<br /></b><i>Sanju TIWARI and Raúl GARCÍA-CASTRO</i></p> <p>2.1 Introduction 21</p> <p>2.2 Literature review 23</p> <p>2.2.1 Features in the water domain 23</p> <p>2.2.2 Semantic models in the water domain 24</p> <p>2.2.3 A comprehensive review of ontologies in the water domain 24</p> <p>2.3 Applications of ontologies in the water domain 32</p> <p>2.4 Discussion and conclusion 35</p> <p>2.5 References 36</p> <p><b>Chapter 3 Semantic Web Approach for Smart Health to Enhance Patient Monitoring in Resuscitation </b><b>41<br /></b><i>Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR</i></p> <p>3.1 Introduction 42</p> <p>3.2 Background 43</p> <p>3.2.1 Semantic Web 43</p> <p>3.2.2 SSN (Semantic Sensor Network) ontology 44</p> <p>3.3 IoT Smart Health applications and semantics 45</p> <p>3.4 Proposed approach and implementation 46</p> <p>3.4.1 Knowledge representation 47</p> <p>3.4.2 Ontology evaluation 51</p> <p>3.4.3 Reasoning and querying 51</p> <p>3.4.4 Linked Data 55</p> <p>3.5 Conclusion 56</p> <p>3.6 References 57</p> <p><b>Chapter 4 Role of Clustering in Discovery Services for the Semantic Internet of Things </b><b>61<br /></b><i>Shachi SHARMA</i></p> <p>4.1 Introduction 61</p> <p>4.2 Discovery services in IoT 64</p> <p>4.2.1 Directory-based architectures 64</p> <p>4.2.2 Directory-less architectures 66</p> <p>4.3 Semantic-based architectures 67</p> <p>4.3.1 Search engine-based 67</p> <p>4.3.2 ONS DNS-based 68</p> <p>4.4 Discovery services and clustering 68</p> <p>4.5 Clustering methods in IoT 69</p> <p>4.6 Conclusion 71</p> <p>4.7 References 71</p> <p><b>Chapter 5 Dynamic Security Testing Techniques for the Semantic Web of Things: Market and Industry Perspective </b><b>75<br /></b><i>Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG</i></p> <p>5.1 Introduction 75</p> <p>5.2 Related studies 77</p> <p>5.3 Background of dynamic security testing techniques 79</p> <p>5.3.1 Black Box testing techniques 80</p> <p>5.4 DAST using static analysis 82</p> <p>5.4.1 Current implementation 82</p> <p>5.5 DAST using user session 84</p> <p>5.5.1 Current implementation 84</p> <p>5.6 DAST using Extended Tainted Mode Model 86</p> <p>5.6.1 Current implementation 87</p> <p>5.7 Current issues and research directions 88</p> <p>5.8 Conclusion 89</p> <p>5.9 References 89</p> <p><b>Chapter 6 SciFiOnto: Modeling, Visualization and Evaluation of Science Fiction Ontologies Based on Indian Contextualization with Automatic Knowledge Acquisition 93<br /></b><i>Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI</i></p> <p>6.1 Introduction 94</p> <p>6.2 Literature survey 97</p> <p>6.2.1 Formulation and modeling of ontologies for varied domains of importance 97</p> <p>6.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 97</p> <p>6.2.3 Ontology-driven systems and applications 98</p> <p>6.2.4 Automatic Knowledge Acquisition systems 99</p> <p>6.2.5 Science fiction as an independent domain of existence 99</p> <p>6.3 Modeling and evaluation of the ontology 100</p> <p>6.3.1 Ontology modeling 100</p> <p>6.3.2 Ontology visualization 104</p> <p>6.3.3 Ontology evaluation 107</p> <p>6.4 Automatic Knowledge Acquisition model 111</p> <p>6.4.1 System architecture 111</p> <p>6.4.2 Acquisition algorithm 113</p> <p>6.5 Conclusion 119</p> <p>6.6 References 119</p> <p><b>Chapter 7 Semantic Web-Enabled IoT Integration for a Smart City </b><b>123<br /></b><i>Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ</i></p> <p>7.1 Introduction: Semantic Web and sensors 123</p> <p>7.2 Motivation and challenge 124</p> <p>7.3 Literature review 124</p> <p>7.4 Implementation of forest planting using SPARQL queries 125</p> <p>7.4.1 Architecture sketch with conceptual diagram 125</p> <p>7.4.2 Implementation ontology from the dataset 126</p> <p>7.4.3 Technologies and tools 129</p> <p>7.5 Conclusion 136</p> <p>7.6 References 136</p> <p><b>Chapter 8 Heart Rate Monitoring Using IoT and AI </b><b>139<br /></b><i>Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY</i></p> <p>8.1 Introduction 140</p> <p>8.2 Literature survey 142</p> <p>8.3 Heart rate monitoring system 145</p> <p>8.4 Results and discussion 149</p> <p>8.5 Conclusion and future works 152</p> <p>8.6 References 152</p> <p><b>Chapter 9 IoT Security Issues and Its Defensive Methods </b><b>155<br /></b><i>Keshavi NALLA and Seshu VARDHAN POTHABATHULA</i></p> <p>9.1 Introduction 155</p> <p>9.2 IoT security architecture 158</p> <p>9.2.1 Typical IoT architecture 158</p> <p>9.2.2 Centralized and distributed approaches over the IoT security architecture 161</p> <p>9.2.3 IoT security architecture based on blockchain 163</p> <p>9.2.4 Internet of Things security architecture: trust zones and boundaries 164</p> <p>9.2.5 Threat modeling in IoT security architecture 168</p> <p>9.3 Specific security challenges and approaches 170</p> <p>9.3.1 Identity and authentication 170</p> <p>9.3.2 Access control 171</p> <p>9.3.3 Protocol and network security 172</p> <p>9.3.4 Privacy 172</p> <p>9.3.5 Trust and governance 173</p> <p>9.3.6 Fault tolerance 173</p> <p>9.4 Methodologies used for securing the systems 174</p> <p>9.4.1 PKI and digital certificates 174</p> <p>9.4.2 Network security 174</p> <p>9.4.3 API security 174</p> <p>9.4.4 Network access control 175</p> <p>9.4.5 Segmentation 175</p> <p>9.4.6 Security gateways 175</p> <p>9.4.7 Patch management and software updates 175</p> <p>9.5 Conclusion 176</p> <p>9.6 References 176</p> <p><b>Chapter 10 Elucidating the Semantic Web of Things for Making the Industry 4.0 Revolution a Success </b><b>179<br /></b><i>Deepika CHAUDHARY and Jaiteg SINGH</i></p> <p>10.1 Introduction 179</p> <p>10.2 Correlation of the Semantic Web of Things with IR4.0 180</p> <p>10.2.1 Smart machines 181</p> <p>10.2.2 Smart products 182</p> <p>10.2.3 Augmented operators 182</p> <p>10.2.4 The Web of Things 183</p> <p>10.2.5 Semantic Web of Things 184</p> <p>10.3 Smart manufacturing system and ontologies 185</p> <p>10.3.1 Vertical level integration 185</p> <p>10.3.2 Horizontal level of integration 185</p> <p>10.3.3 End-to-end integration 185</p> <p>10.4 Literature survey 188</p> <p>10.5 Conclusion and future work 190</p> <p>10.6 References 190</p> <p><b>Chapter 11 Semantic Web and Internet of Things in e-Health for Covid-19 </b><b>195<br /></b><i>ANURAG and Naren JEEVA</i></p> <p>11.1 Introduction 196</p> <p>11.2 Dataset 197</p> <p>11.3 Application of IoT for Covid-19 198</p> <p>11.3.1 Continuous real-time remote monitoring 198</p> <p>11.3.2 Remote monitoring using W-kit 198</p> <p>11.3.3 Early identification and monitoring 198</p> <p>11.3.4 Continuous and reliable health monitoring 198</p> <p>11.3.5 ANN-assisted patient monitoring 199</p> <p>11.3.6 City lockdown monitoring 199</p> <p>11.3.7 Technologies for tracking and tracing 199</p> <p>11.3.8 Tracking and tracing suspected cases 199</p> <p>11.3.9 Anonymity preserving contact tracing model 200</p> <p>11.3.10 Cognitive radio-based IoT architecture 200</p> <p>11.3.11 Analyzing reasons for the outbreak 200</p> <p>11.3.12 Analyzing Covid-19 cases using disruptive technology 200</p> <p>11.3.13 Post-Covid applications 201</p> <p>11.4 Semantic Web applications for Covid-19 201</p> <p>11.4.1 Ontological approach for drug development 202</p> <p>11.4.2 Early detection and diagnosis 202</p> <p>11.4.3 Knowledge-based pre-diagnosis system 202</p> <p>11.4.4 Semantic-based searching for online learning resources 203</p> <p>11.4.5 Ontology-based physiological monitoring of students 203</p> <p>11.4.6 Analysis of clinical trials 203</p> <p>11.4.7 Data annotation of EHRs 204</p> <p>11.4.8 Disease pattern study 204</p> <p>11.4.9 Surveillance in primary care 204</p> <p>11.4.10 Performance assessment of healthcare services 205</p> <p>11.4.11 Vaccination drives and rollout strategies 205</p> <p>11.5 Limitations and challenges of IoT and SW models 205</p> <p>11.6 Discussion 206</p> <p>11.7 Conclusion 206</p> <p>11.8 References 207</p> <p><b>Chapter 12 Development of a Semantic Web Enabled Job_Search Ontology System </b><b>211<br /></b><i>Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL</i></p> <p>12.1 Introduction 211</p> <p>12.1.1 Ontology 212</p> <p>12.1.2 Importance of ontology 213</p> <p>12.1.3 Semantic Web and its solutions 214</p> <p>12.1.4 Online recruitment scenarios 214</p> <p>12.2 Review of the related work done for online recruitment 215</p> <p>12.3 Design of “SearchAJob” ontology for the IT domain 217</p> <p>12.3.1 Ontology structure 218</p> <p>12.4 Implementing the proposed ontology 222</p> <p>12.4.1 Architecture of semantics-based job ontology 223</p> <p>12.5 Benefits of Semantic Web enabled SearchAJob system 231</p> <p>12.6 Conclusion and future scope 232</p> <p>12.7 References 233</p> <p>List of Authors 237</p> <p>Index 241</p>

<b>Shikha Mehta</b> 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.<br /><br /><b>Sanju Tiwari</b> 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.<br /><br /><b>Patrick Siarry</b> 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.

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