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Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications


Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications


1. Aufl.

von: Deepak Gupta, Aditya Khamparia

103,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 03.12.2020
ISBN/EAN: 9781119670094
Sprache: englisch
Anzahl Seiten: 464

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Beschreibungen

<p><b>A practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications</b></p> <p>With the rapid development in artificially intelligent and hybrid technologies, IoT, edge, fog-driven, and pervasive computing techniques are becoming important parts of our daily lives. This book focuses on recent advances, roles, and benefits of these technologies, describing the latest intelligent systems from a practical point of view. <i>Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications</i> is also valuable for engineers and professionals trying to solve practical, economic, or technical problems. With a uniquely practical approach spanning multiple fields of interest, contributors cover theory, applications, and design methodologies for intelligent systems. These technologies are rapidly transforming engineering, industry, and agriculture by enabling real-time processing of data via computational, resource-oriented metaheuristics and machine learning algorithms. As edge/fog computing and associated technologies are implemented far and wide, we are now able to solve previously intractable problems. With chapters contributed by experts in the field, this book:</p> <ul> <li>Describes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing</li> <li>Considers probabilistic storage systems and proven optimization techniques for intelligent IoT</li> <li>Covers 5G edge network slicing and virtual network systems that utilize new networking capacity</li> <li>Explores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications</li> <li>Presents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more</li> </ul> <p>Researchers, graduate students, and practitioners working in the intelligent systems domain will appreciate this book’s practical orientation and comprehensive coverage. Intelligent IoT is revolutionizing every industry and field today, and <i>Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications </i>provides the background, orientation, and inspiration needed to begin.</p>
<p>About the Editors xvii</p> <p>List of Contributors xix</p> <p>Preface xxv</p> <p>Acknowledgments xxxiii</p> <p><b>1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use </b><b>1<br /></b><i>Afroj Alam, Sahar Qazi, Naiyar Iqbal, and Khalid Raza</i></p> <p>1.1 Introduction 1</p> <p>1.2 Why Fog, Edge, and Pervasive Computing? 3</p> <p>1.3 Technologies Related to Fog and Edge Computing 6</p> <p>1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era 9</p> <p>1.5 The Hierarchical Architecture of Fog/Edge Computing 12</p> <p>1.6 Applications of Fog, Edge and Pervasive Computing in IoT-based Healthcare 15</p> <p>1.7 Issues, Challenges, and Opportunity 17</p> <p>1.7.1 Security and Privacy Issues 18</p> <p>1.7.2 Resource Management 19</p> <p>1.7.3 Programming Platform 19</p> <p>1.8 Conclusion 20</p> <p>Bibliography 20</p> <p><b>2 Future Opportunistic Fog/Edge Computational Models and their Limitations </b><b>27<br /></b><i>Sonia Singla, Naveen Kumar Bhati, and S. Aswath</i></p> <p>2.1 Introduction 28</p> <p>2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? 32</p> <p>2.3 Disadvantages 34</p> <p>2.4 Challenges 34</p> <p>2.5 Role in Health Care 35</p> <p>2.6 Blockchain and Fog, Edge Computing 38</p> <p>2.7 How Blockchain will Illuminate Human Services Issues 40</p> <p>2.8 Uses of Blockchain in the Future 41</p> <p>2.9 Uses of Blockchain in Health Care 42</p> <p>2.10 Edge Computing Segmental Analysis 42</p> <p>2.11 Uses of Fog Computing 43</p> <p>2.12 Analytics in Fog Computing 44</p> <p>2.13 Conclusion 44</p> <p>Bibliography 44</p> <p><b>3 Automating Elicitation Technique Selection using Machine Learning </b><b>47<br /></b><i>Hatim M. Elhassan Ibrahim Dafallaa, Nazir Ahmad, Mohammed Burhanur Rehman, Iqrar Ahmad, and Rizwan khan</i></p> <p>3.1 Introduction 47</p> <p>3.2 Related Work 48</p> <p>3.3 Model: Requirement Elicitation Technique Selection Model 52</p> <p>3.3.1 Determining Key Attributes 54</p> <p>3.3.2 Selection Attributes 54</p> <p>3.3.2.1 Analyst Experience 55</p> <p>3.3.2.2 Number of Stakeholders 55</p> <p>3.3.2.3 Technique Time 56</p> <p>3.3.2.4 Level of Information 56</p> <p>3.3.3 Selection Attributes Dataset 56</p> <p>3.3.3.1 Mapping the Selection Attributes 57</p> <p>3.3.4 <i>k</i>-nearest Neighbor Algorithm Application 57</p> <p>3.4 Analysis and Results 60</p> <p>3.5 The Error Rate 61</p> <p>3.6 Validation 61</p> <p>3.6.1 Discussion of the Results of the Experiment 62</p> <p>3.7 Conclusion 62</p> <p>Bibliography 65</p> <p><b>4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing </b><b>67<br /></b><i>Murali Mallikarjuna Rao Perumalla, Sanjay Kumar Singh, Aditya Khamparia, Anjali Goyal, and Ashish Mishra</i></p> <p>4.1 Introduction 68</p> <p>4.1.1 Fog Computing and Edge Computing 68</p> <p>4.1.2 Pervasive Computing 68</p> <p>4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing 69</p> <p>4.2.1 TensorFlow 69</p> <p>4.2.2 Keras 70</p> <p>4.2.3 PyTorch 70</p> <p>4.2.4 TensorFlow Lite 70</p> <p>4.2.4.1 Use Pre-train Models 70</p> <p>4.2.4.2 Convert the Model 70</p> <p>4.2.4.3 On-device Inference 71</p> <p>4.2.4.4 Model Optimization 71</p> <p>4.2.5 Machine Learning and Deep Learning Techniques 71</p> <p>4.2.5.1 Supervised, Unsupervised and Reinforcement Learning 71</p> <p>4.2.5.2 Machine Learning, Deep Learning Techniques 72</p> <p>4.2.5.3 Deep Learning Techniques 75</p> <p>4.2.5.4 Efficient Deep Learning Algorithms for Inference 77</p> <p>4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing 78</p> <p>4.2.6.1 Advantages using ML Algorithms 78</p> <p>4.2.6.2 Disadvantages of using ML Algorithms 79</p> <p>4.2.7 Hybrid ML Model for Smart IoT Applications 79</p> <p>4.2.7.1 Multi-Task Learning 79</p> <p>4.2.7.2 Ensemble Learning 80</p> <p>4.2.8 Possible Applications in Fog Era using Machine Learning 81</p> <p>4.2.8.1 Computer Vision 81</p> <p>4.2.8.2 ML- Assisted Healthcare Monitoring System 81</p> <p>4.2.8.3 Smart Homes 81</p> <p>4.2.8.4 Behavior Analyses 82</p> <p>4.2.8.5 Monitoring in Remote Areas and Industries 82</p> <p>4.2.8.6 Self-Driving Cars 82</p> <p>Bibliography 82</p> <p><b>5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications </b><b>85<br /></b><i>Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal</i></p> <p>5.1 Introduction 86</p> <p>5.1.1 Execution Plan for the Mobile Application 86</p> <p>5.1.2 Main Contribution 86</p> <p>5.2 Understanding Library Management 87</p> <p>5.3 Integration of Mobile Platform with the Physical Library- Brief Concept 88</p> <p>5.4 Database (Cloud Based) - A Must have Component for Library Automation 88</p> <p>5.5 IoT Driven Mobile Based Library Management - General Concept 89</p> <p>5.6 IoT Involved Real Time GUI (Cross Platform) Available to User 93</p> <p>5.7 IoT Challenges 98</p> <p>5.7.1 Infrastructure Challenges 99</p> <p>5.7.2 Security Challenges 99</p> <p>5.7.3 Societal Challenges 100</p> <p>5.7.4 Commercial Challenges 101</p> <p>5.8 Conclusion 102</p> <p>Bibliography 104</p> <p><b>6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer </b><b>105<br /></b><i>Nikita, Harsh Sadawarti, Balwinder Kaur, and Jimmy Singla</i></p> <p>6.1 Introduction 106</p> <p>6.2 Related Works 107</p> <p>6.3 Conclusion 119</p> <p>Bibliography 119</p> <p><b>7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation </b><b>123<br /></b><i>Saravjeet Singh and Jaiteg Singh</i></p> <p>7.1 Introduction to Fog and Edge Computing 124</p> <p>7.1.1 Need for Fog and Edge Computing 124</p> <p>7.1.2 Fog Computing 125</p> <p>7.1.2.1 Application Areas of Fog Computing 125</p> <p>7.1.3 Edge Computing 126</p> <p>7.1.3.1 Advantages of Edge Computing 127</p> <p>7.1.3.2 Application Areas of Fog Computing 129</p> <p>7.2 Introduction to Transportation System 129</p> <p>7.3 Route Finding Process 131</p> <p>7.3.1 Challenges Associated with Land Navigation and Routing Process 132</p> <p>7.4 Edge Architecture for Route Finding 133</p> <p>7.5 Technique Used 135</p> <p>7.6 Algorithms Used for the Location Identification and Route Finding Process 137</p> <p>7.6.1 Location Identification 137</p> <p>7.6.2 Path Generation Technique 138</p> <p>7.7 Results and Discussions 140</p> <p>7.7.1 Output 140</p> <p>7.7.2 Benefits of Edge-based Routing 143</p> <p>7.8 Conclusion 145</p> <p>Bibliography 146</p> <p><b>8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator </b><b>149<br /></b><i>Natasha Tiwari, Anil Kumar, Pallavi Asthana, Sumita Mishra, and Bramah Hazela</i></p> <p>8.1 Introduction 149</p> <p>8.2 Related Work 151</p> <p>8.3 Vehicle Condition Monitoring through Acoustic Emission 151</p> <p>8.4 Piezo-resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE 152</p> <p>8.5 Designing of MEM Sensor 153</p> <p>8.6 Experimental Setup 153</p> <p>8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB 155</p> <p>8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 155</p> <p>8.6.3 Electrostatic Study Steps for the Optimized Tri-plate Comb Structure 156</p> <p>8.7 Result and Discussions 157</p> <p>8.8 Conclusion 158</p> <p>Bibliography 158</p> <p><b>9 IoT Driven Healthcare Monitoring System </b><b>161<br /></b><i>Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal</i></p> <p>9.1 Introduction 161</p> <p>9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications 162</p> <p>9.1.2 Main Contribution 164</p> <p>9.2 General Concept for IoT Based Healthcare System 164</p> <p>9.3 View of the Overall IoT Healthcare System- Tiers Explained 165</p> <p>9.4 A Brief Design of the IoT Healthcare Architecture-individual Block Explanation 166</p> <p>9.5 Models/Frameworks for IoT use in Healthcare 168</p> <p>9.6 IoT e-Health System Model 171</p> <p>9.7 Process Flow for the Overall Model 172</p> <p>9.8 Conclusion 173</p> <p>Bibliography 175</p> <p><b>10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks </b><b>177<br /></b><i>Harjit Singh, Dr. Vijay Laxmi, Dr. Arun Malik, and Dr. Isha</i></p> <p>10.1 Introduction 178</p> <p>10.2 Future VANET: Primary Issues and Specifications 180</p> <p>10.3 Fog Computing 181</p> <p>10.3.1 Fog Computing Concept 183</p> <p>10.3.2 Fog Technology Characterization 183</p> <p>10.4 Related Works in Cloud and Fog Computing 185</p> <p>10.5 Fog and Cloud Computing-based Technology Applications in VANET 186</p> <p>10.6 Challenges of Fog Computing in VANET 188</p> <p>10.7 Issues of Fog Computing in VANET 189</p> <p>10.8 Conclusion 190</p> <p>Bibliography 191</p> <p><b>11 An Overview to Design an Efficient and Secure Fog-assisted Data Collection Method in the Internet of Things </b><b>193<br /></b><i>Sofia, Arun Malik, Isha, and Aditya Khamparia</i></p> <p>11.1 Introduction 193</p> <p>11.2 Related Works 194</p> <p>11.3 Overview of the Chapter 196</p> <p>11.4 Data Collection in the IoT 197</p> <p>11.5 Fog Computing 197</p> <p>11.5.1 Why fog Computing for Data Collection in IoT? 197</p> <p>11.5.2 Architecture of Fog Computing 200</p> <p>11.5.3 Features of Fog Computing 200</p> <p>11.5.4 Threats of Fog Computing 202</p> <p>11.5.5 Applications of Fog Computing with the IoT 203</p> <p>11.6 Requirements for Designing a Data Collection Method 204</p> <p>11.7 Conclusion 206</p> <p>Bibliography 206</p> <p><b>12 Role of Fog Computing Platform in Analytics of Internet of Things- Issues, Challenges and Opportunities </b><b>209<br /></b><i>Mamoon Rashid and Umer Iqbal Wani</i></p> <p>12.1 Introduction to Fog Computing 209</p> <p>12.1.1 Hierarchical Fog Computing Architecture 210</p> <p>12.1.2 Layered Fog Computing Architecture 212</p> <p>12.1.3 Comparison of Fog and Cloud Computing 213</p> <p>12.2 Introduction to Internet of Things 214</p> <p>12.2.1 Overview of Internet of Things 214</p> <p>12.3 Conceptual Architecture of Internet of Things 216</p> <p>12.4 Relationship between Internet of Things and Fog Computing 217</p> <p>12.5 Use of Fog Analytics in Internet of Things 218</p> <p>12.6 Conclusion 218</p> <p>Bibliography 218</p> <p><b>13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets </b><b>221<br /></b><i>Prabjot Kaur and Maria Jamal</i></p> <p>13.1 Introduction 221</p> <p>13.2 Preliminaries 223</p> <p>13.2.1 Introduction 223</p> <p>13.2.2 Fuzzy Sets 223</p> <p>13.2.3 Intuitionistic Fuzzy Sets 224</p> <p>13.2.4 Intuitionistic Fuzzy Relation 224</p> <p>13.2.5 Max-Min-Max Composition 224</p> <p>13.2.6 Linguistic Variable 224</p> <p>13.2.7 Distance Measure In Intuitionistic Fuzzy Sets 224</p> <p>13.2.7.1 The Hamming Distance 224</p> <p>13.2.7.2 Normalized Hamming Distance 224</p> <p>13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix 225</p> <p>13.2.7.4 Revised Max-Min Average Composition of A and B (A Φ B) 225</p> <p>13.3 Max-Min-Max Algorithm for Disease Diagnosis 225</p> <p>13.4 Case Study 226</p> <p>13.5 Intuitionistic Fuzzy Max-Min Average Algorithm for Disease Diagnosis 227</p> <p>13.6 Result 228</p> <p>13.7 Code for Calculation 229</p> <p>13.8 Conclusion 233</p> <p>13.9 Acknowledgement 234</p> <p>Bibliography 234</p> <p><b>14 Security Attacks in Internet of Things </b><b>237<br /></b><i>Rajit Nair, Preeti Sharma, and Dileep Kumar Singh</i></p> <p>14.1 Introduction 238</p> <p>14.2 Reference Model of Internet of Things (IoT) 238</p> <p>14.3 IoT Communication Protocol 246</p> <p>14.4 IoT Security 247</p> <p>14.4.1 Physical Attack 248</p> <p>14.4.2 Network Attack 252</p> <p>14.4.3 Software Attack 254</p> <p>14.4.4 Encryption Attack 255</p> <p>14.5 Security Challenges in IoT 256</p> <p>14.5.1 Cryptographic Strategies 256</p> <p>14.5.2 Key Administration 256</p> <p>14.5.3 Denial of Service 256</p> <p>14.5.4 Authentication and Access Control 257</p> <p>14.6 Conclusion 257</p> <p>Bibliography 257</p> <p><b>15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery </b><b>263<br /></b><i>Inderpreet Kaur, Kamaljit Singh Saini, and Jaiteg Singh Khaira</i></p> <p>15.1 Introduction 264</p> <p>15.2 Associated Work and Dimensions 266</p> <p>15.3 Need of Security in Telemedicine Domain and Internet of Things (IoT) 267</p> <p>15.3.1 Analytics Reports 268</p> <p>15.4 Fog Integrated Architecture for Telehealth Delivery 268</p> <p>15.5 Research Dimensions 269</p> <p>15.5.1 Benchmark Datasets 269</p> <p>15.6 Research Methodology and Implementation on Software Defined Networking 270</p> <p>15.6.1 Key Tools and Frameworks for IoT, Fog Computing and Edge Computing 274</p> <p>15.6.2 Simulation Analysis 276</p> <p>15.7 Conclusion 282</p> <p>Bibliography 282</p> <p><b>16 Fruit Fly Optimization Algorithm for Intelligent IoT Applications </b><b>287<br /></b><i>Satinder Singh Mohar, Sonia Goyal, and Ranjit Kaur</i></p> <p>16.1 An Introduction to the Internet of Things 287</p> <p>16.2 Background of the IoT 288</p> <p>16.2.1 Evolution of the IoT 288</p> <p>16.2.2 Elements Involved in IoT Communication 288</p> <p>16.3 Applications of the IoT 289</p> <p>16.3.1 Industrial 290</p> <p>16.3.2 Smart Parking 290</p> <p>16.3.3 Health Care 290</p> <p>16.3.4 Smart Offices and Homes 290</p> <p>16.3.5 Augment Maps 291</p> <p>16.3.6 Environment Monitoring 291</p> <p>16.3.7 Agriculture 291</p> <p>16.4 Challenges in the IoT 291</p> <p>16.4.1 Addressing Schemes 291</p> <p>16.4.2 Energy Consumption 292</p> <p>16.4.3 Transmission Media 292</p> <p>16.4.4 Security 292</p> <p>16.4.5 Quality of Service (QoS) 292</p> <p>16.5 Introduction to Optimization 293</p> <p>16.6 Classification of Optimization Algorithms 293</p> <p>16.6.1 Particle Swarm Optimization (PSO) Algorithm 293</p> <p>16.6.2 Genetic Algorithms 294</p> <p>16.6.3 Heuristic Algorithms 294</p> <p>16.6.4 Bio-inspired Algorithms 294</p> <p>16.6.5 Evolutionary Algorithms (EA) 294</p> <p>16.7 Network Optimization and IoT 295</p> <p>16.8 Network Parameters optimized by Different Optimization Algorithms 295</p> <p>16.8.1 Load Balancing 295</p> <p>16.8.2 Maximizing Network Lifetime 295</p> <p>16.8.3 Link Failure Management 296</p> <p>16.8.4 Quality of the Link 296</p> <p>16.8.5 Energy Efficiency 296</p> <p>16.8.6 Node Deployment 296</p> <p>16.9 Fruit Fly Optimization Algorithm 297</p> <p>16.9.1 Steps Involved in FOA 297</p> <p>16.9.2 Flow Chart of Fruit Fly Optimization Algorithm 298</p> <p>16.10 Applicability of FOA in IoT Applications 300</p> <p>16.10.1 Cloud Service Distribution in Fog Computing 300</p> <p>16.10.2 Cluster Head Selection in IoT 300</p> <p>16.10.3 Load Balancing in IoT 300</p> <p>16.10.4 Quality of Service in Web Services 300</p> <p>16.10.5 Electronics Health Records in Cloud Computing 301</p> <p>16.10.6 Intrusion Detection System in Network 301</p> <p>16.10.7 Node Capture Attack in WSN 301</p> <p>16.10.8 Node Deployment in WSN 302</p> <p>16.11 Node Deployment Using Fruit Fly Optimization Algorithm 302</p> <p>16.12 Conclusion 304</p> <p>Bibliography 304</p> <p><b>17 Optimization Techniques for Intelligent IoT Applications </b><b>311<br /></b><i>Priyanka Pattnaik, Subhashree Mishra, and Bhabani Shankar Prasad Mishra</i></p> <p>17.1 Cuckoo Search 312</p> <p>17.1.1 Introduction to Cuckoo 312</p> <p>17.1.2 Natural Cuckoo 312</p> <p>17.1.3 Artificial Cuckoo Search 313</p> <p>17.1.4 Cuckoo Search Algorithm 313</p> <p>17.1.5 Cuckoo Search Variants 314</p> <p>17.1.6 Discrete Cuckoo Search 314</p> <p>17.1.7 Binary Cuckoo Search 314</p> <p>17.1.8 Chaotic Cuckoo Search 316</p> <p>17.1.9 Parallel Cuckoo Search 317</p> <p>17.1.10 Application of Cuckoo Search 317</p> <p>17.2 Glow Worm Algorithm 317</p> <p>17.2.1 Introduction to Glow Worm 317</p> <p>17.2.2 Glow Worm Swarm Optimization Algorithm (GSO) 317</p> <p>17.3 Wasp Swarm Optimization 321</p> <p>17.3.1 Introduction to Wasp Swarm and Wasp Swarm Algorithm (WSO) 321</p> <p>17.3.2 Fish Swarm Optimization (FSO) 322</p> <p>17.3.3 Fruit Fly Optimization (FLO) 322</p> <p>17.3.4 Cockroach Swarm Optimization 324</p> <p>17.3.5 Bumblebee Algorithm 324</p> <p>17.3.6 Dolphin Echolocation 325</p> <p>17.3.7 Shuffled Frog-leaping Algorithm 326</p> <p>17.3.8 Paddy Field Algorithm 327</p> <p>17.4 Real World Applications Area 328</p> <p>Summary 329</p> <p>Bibliography 329</p> <p><b>18 Optimization Techniques for Intelligent IoT Applications in Transport Processes </b><b>333<br /></b><i>Muzafer Sara</i><i>čević, Zoran Lon</i><i>čarević, and Adnan Hasanović</i></p> <p>18.1 Introduction 333</p> <p>18.2 Related Works 335</p> <p>18.3 TSP Optimization Techniques 336</p> <p>18.4 Implementation and Testing of Proposed Solution 338</p> <p>18.5 Experimental Results 342</p> <p>18.5.1 Example Test with 50 Cities 343</p> <p>18.5.2 Example Test with 100 Cities 344</p> <p>18.6 Conclusion and Further Works 346</p> <p>Bibliography 347</p> <p><b>19 Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities </b><b>351<br /></b><i>Priyanka Rajan Kumar and Sonia Goel</i></p> <p>19.1 Fog Computing 352</p> <p>19.1.1 Need of Fog computing 352</p> <p>19.1.2 Architecture of Fog Computing 353</p> <p>19.1.3 Fog Computing Reference Architecture 354</p> <p>19.1.4 Processing on Fog 355</p> <p>19.2 Concept of Intelligent IoT Applications in Smart Computing Era 355</p> <p>19.3 Components of Edge and Fog Driven Algorithm 356</p> <p>19.4 Working of Edge and Fog Driven Algorithms 357</p> <p>19.5 Future Opportunistic Fog/Edge Computational Models 360</p> <p>19.5.1 Future Opportunistic Techniques 361</p> <p>19.6 Challenges of Fog Computing for Intelligent IoT Applications 361</p> <p>19.7 Applications of Cloud Based Computing for Smart Devices 363</p> <p>Bibliography 364</p> <p><b>20 Security and Privacy Issues in Fog/Edge/Pervasive Computing </b><b>369<br /></b><i>Shweta Kaushik and Charu Gandhi</i></p> <p>20.1 Introduction to Data Security and Privacy in Fog Computing 370</p> <p>20.2 Data Protection/ Security 375</p> <p>20.3 Great Security Practices In Fog Processing Condition 377</p> <p>20.4 Developing Patterns in Security and Privacy 381</p> <p>20.5 Conclusion 385</p> <p>Bibliography 385</p> <p><b>21 Fog and Edge Driven Security & Privacy Issues in IoT Devices </b><b>389<br /></b><i>Deepak Kumar Sharma, Aarti Goel, and Pragun Mangla</i></p> <p>21.1 Introduction to Fog Computing 390</p> <p>21.1.1 Architecture of Fog 390</p> <p>21.1.2 Benefits of Fog Computing 392</p> <p>21.1.3 Applications of Fog with IoT 393</p> <p>21.1.4 Major Challenges for Fog with IoT 394</p> <p>21.1.5 Security and Privacy Issues in Fog Computing 395</p> <p>21.2 Introduction to Edge Computing 399</p> <p>21.2.1 Architecture and Working 400</p> <p>21.2.2 Applications and use Cases 400</p> <p>21.2.3 Characteristics of Edge Computing 403</p> <p>21.2.4 Challenges of Edge Computing 404</p> <p>21.2.5 How to Protect Devices “On the Edge”? 405</p> <p>21.2.6 Comparison with Fog Computing 405</p> <p>Bibliography 406</p> <p>Index 409</p>
<p><b>Deepak Gupta, PhD,</b> is an Assistant Professor in the Department of Computer Science and Engineering at the Maharaja Agrasen Institute of Technology, Delhi, India. He has published 158 papers and 3 patents. He is associated with numerous professional bodies, including IEEE, ISTE, IAENG, and IACSIT. He is the convener and organizer of the ICICC, ICDAM Springer Conference Series. <p><b>Aditya Khamparia, PhD,</b> is Associate Professor of Computer Science at Lovely Professional University, Punjab, India. He has published more than 45 scientific research publications and is a member of CSI, IET, ISTE, IAENG, ACM and IACSIT.
<p><b>A practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications</b> <p>With the rapid development in artificially intelligent and hybrid technologies, IoT, edge, fog-driven, and pervasive computing techniques are becoming important parts of our daily lives. This book focuses on recent advances, roles, and benefits of these technologies, describing the latest intelligent systems from a practical point of view. <i>Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications</i> is also valuable for engineers and professionals trying to solve practical, economic, or technical problems. With a uniquely practical approach spanning multiple fields of interest, contributors cover theory, applications, and design methodologies for intelligent systems. These technologies are rapidly transforming engineering, industry, and agriculture by enabling real-time processing of data via computational, resource-oriented metaheuristics and machine learning algorithms. As edge/fog computing and associated technologies are implemented far and wide, we are now able to solve previously intractable problems. With chapters contributed by experts in the field, this book: <ul> <li>Describes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing</li> <li>Considers probabilistic storage systems and proven optimization techniques for intelligent IoT</li> <li>Covers 5G edge network slicing and virtual network systems that utilize new networking capacity</li> <li>Explores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications</li> <li>Presents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more</li> </ul> <p>Researchers, graduate students, and practitioners working in the intelligent systems domain will appreciate this book's practical orientation and comprehensive coverage. Intelligent IoT is revolutionizing every industry and field today, and <i>Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications</i> provides the background, orientation, and inspiration needed to begin.

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