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

A PRACTICAL GUIDE TO THE DESIGN, IMPLEMENTATION, EVALUATION, AND DEPLOYMENT OF EMERGING TECHNOLOGIES FOR INTELLIGENT IOT APPLICATIONS

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

* Describes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing
* Considers probabilistic storage systems and proven optimization techniques for intelligent IoT
* Covers 5G edge network slicing and virtual network systems that utilize new networking capacity
* Explores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications
* Presents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more

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 Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications provides the background, orientation, and inspiration needed to begin.

DEEPAK GUPTA, PHD, 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. ADITYA KHAMPARIA, PHD, 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. About the Editors xvii

List of Contributors xix

Preface xxv

Acknowledgments xxxiii

1 FOG, EDGE AND PERVASIVE COMPUTING IN INTELLIGENT INTERNET OF THINGS DRIVEN APPLICATIONS IN HEALTHCARE: CHALLENGES, LIMITATIONS AND FUTURE USE 1

Afroj Alam, Sahar Qazi, Naiyar Iqbal, and Khalid Raza

1.1 Introduction 1

1.2 Why Fog, Edge, and Pervasive Computing? 3

1.3 Technologies Related to Fog and Edge Computing 6

1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era 9

1.5 The Hierarchical Architecture of Fog/Edge Computing 12

1.6 Applications of Fog, Edge and Pervasive Computing in IoT-based Healthcare 15

1.7 Issues, Challenges, and Opportunity 17

1.7.1 Security and Privacy Issues 18

1.7.2 Resource Management 19

1.7.3 Programming Platform 19

1.8 Conclusion 20

Bibliography 20

2 FUTURE OPPORTUNISTIC FOG/EDGE COMPUTATIONAL MODELS AND THEIR LIMITATIONS 27

Sonia Singla, Naveen Kumar Bhati, and S. Aswath

2.1 Introduction 28

2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? 32

2.3 Disadvantages 34

2.4 Challenges 34

2.5 Role in Health Care 35

2.6 Blockchain and Fog, Edge Computing 38

2.7 How Blockchain will Illuminate Human Services Issues 40

2.8 Uses of Blockchain in the Future 41

2.9 Uses of Blockchain in Health Care 42

2.10 Edge Computing Segmental Analysis 42

2.11 Uses of Fog Computing 43

2.12 Analytics in Fog Computing 44

2.13 Conclusion 44

Bibliography 44

3 AUTOMATING ELICITATION TECHNIQUE SELECTION USING MACHINE LEARNING 47

Hatim M. Elhassan Ibrahim Dafallaa, Nazir Ahmad, Mohammed Burhanur Rehman, Iqrar Ahmad, and Rizwan khan

3.1 Introduction 47

3.2 Related Work 48

3.3 Model: Requirement Elicitation Technique Selection Model 52

3.3.1 Determining Key Attributes 54

3.3.2 Selection Attributes 54

3.3.2.1 Analyst Experience 55

3.3.2.2 Number of Stakeholders 55

3.3.2.3 Technique Time 56

3.3.2.4 Level of Information 56

3.3.3 Selection Attributes Dataset 56

3.3.3.1 Mapping the Selection Attributes 57

3.3.4 k-nearest Neighbor Algorithm Application 57

3.4 Analysis and Results 60

3.5 The Error Rate 61

3.6 Validation 61

3.6.1 Discussion of the Results of the Experiment 62

3.7 Conclusion 62

Bibliography 65

4 MACHINE LEARNING FRAMEWORKS AND ALGORITHMS FOR FOG AND EDGE COMPUTING 67

Murali Mallikarjuna Rao Perumalla, Sanjay Kumar Singh, Aditya Khamparia, Anjali Goyal, and Ashish Mishra

4.1 Introduction 68

4.1.1 Fog Computing and Edge Computing 68

4.1.2 Pervasive Computing 68

4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing 69

4.2.1 TensorFlow 69

4.2.2 Keras 70

4.2.3 PyTorch 70

4.2.4 TensorFlow Lite 70

4.2.4.1 Use Pre-train Models 70

4.2.4.2 Convert the Model 70

4.2.4.3 On-device Inference 71

4.2.4.4 Model Optimization 71

4.2.5 Machine Learning and Deep Learning Techniques 71

4.2.5.1 Supervised, Unsupervised and Reinforcement Learning 71

4.2.5.2 Machine Learning, Deep Learning Techniques 72

4.2.5.3 Deep Learning Techniques 75

4.2.5.4 Efficient Deep Learning Algorithms for Inference 77

4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing 78

4.2.6.1 Advantages using ML Algorithms 78

4.2.6.2 Disadvantages of using ML Algorithms 79

4.2.7 Hybrid ML Model for Smart IoT Applications 79

4.2.7.1 Multi-Task Learning 79

4.2.7.2 Ensemble Learning 80

4.2.8 Possible Applications in Fog Era using Machine Learning 81

4.2.8.1 Computer Vision 81

4.2.8.2 ML- Assisted Healthcare Monitoring System 81

4.2.8.3 Smart Homes 81

4.2.8.4 Behavior Analyses 82

4.2.8.5 Monitoring in Remote Areas and Industries 82

4.2.8.6 Self-Driving Cars 82

Bibliography 82

5 INTEGRATED CLOUD BASED LIBRARY MANAGEMENT IN INTELLIGENT IOT DRIVEN APPLICATIONS 85

Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal

5.1 Introduction 86

5.1.1 Execution Plan for the Mobile Application 86

5.1.2 Main Contribution 86

5.2 Understanding Library Management 87

5.3 Integration of Mobile Platform with the Physical Library- Brief Concept 88

5.4 Database (Cloud Based) - A Must have Component for Library Automation 88

5.5 IoT Driven Mobile Based Library Management - General Concept 89

5.6 IoT Involved Real Time GUI (Cross Platform) Available to User 93

5.7 IoT Challenges 98

5.7.1 Infrastructure Challenges 99

5.7.2 Security Challenges 99

5.7.3 Societal Challenges 100

5.7.4 Commercial Challenges 101

5.8 Conclusion 102

Bibliography 104

6 A SYSTEMATIC AND STRUCTURED REVIEW OF INTELLIGENT SYSTEMS FOR DIAGNOSIS OF RENAL CANCER 105

Nikita, Harsh Sadawarti, Balwinder Kaur, and Jimmy Singla

6.1 Introduction 106

6.2 Related Works 107

6.3 Conclusion 119

Bibliography 119

7 LOCATION DRIVEN EDGE ASSISTED DEVICE AND SOLUTIONS FOR INTELLIGENT TRANSPORTATION 123

Saravjeet Singh and Jaiteg Singh

7.1 Introduction to Fog and Edge Computing 124

7.1.1 Need for Fog and Edge Computing 124

7.1.2 Fog Computing 125

7.1.2.1 Application Areas of Fog Computing 125

7.1.3 Edge Computing 126

7.1.3.1 Advantages of Edge Computing 127

7.1.3.2 Application Areas of Fog Computing 129

7.2 Introduction to Transportation System 129

7.3 Route Finding Process 131

7.3.1 Challenges Associated with Land Navigation and Routing Process 132

7.4 Edge Architecture for Route Finding 133

7.5 Technique Used 135

7.6 Algorithms Used for the Location Identification and Route Finding Process 137

7.6.1 Location Identification 137

7.6.2 Path Generation Technique 138

7.7 Results and Discussions 140

7.7.1 Output 140

7.7.2 Benefits of Edge-based Routing 143

7.8 Conclusion 145

Bibliography 146

8 DESIGN AND SIMULATION OF MEMS FOR AUTOMOBILE CONDITION MONITORING USING COMSOL MULTIPHYSICS SIMULATOR 149

Natasha Tiwari, Anil Kumar, Pallavi Asthana, Sumita Mishra, and Bramah Hazela

8.1 Introduction 149

8.2 Related Work 151

8.3 Vehicle Condition Monitoring through Acoustic Emission 151

8.4 Piezo-resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE 152

8.5 Designing of MEM Sensor 153

8.6 Experimental Setup 153

8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB 155

8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 155

8.6.3 Electrostatic Study Steps for the Optimized Tri-plate Comb Structure 156

8.7 Result and Discussions 157

8.8 Conclusion 158

Bibliography 158

9 IOT DRIVEN HEALTHCARE MONITORING SYSTEM 161

Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal

9.1 Introduction 161

9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications 162

9.1.2 Main Contribution 164

9.2 General Concept for IoT Based Healthcare System 164

9.3 View of the Overall IoT Healthcare System- Tiers Explained 165

9.4 A Brief Design of the IoT Healthcare Architecture-individual Block Explanation 166

9.5 Models/Frameworks for IoT use in Healthcare 168

9.6 IoT e-Health System Model 171

9.7 Process Flow for the Overall Model 172

9.8 Conclusion 173

Bibliography 175

10 FOG COMPUTING AS FUTURE PERSPECTIVE IN VEHICULAR AD HOC NETWORKS 177

Harjit Singh, Dr. Vijay Laxmi, Dr. Arun Malik, and Dr. Isha

10.1 Introduction 178

10.2 Future VANET: Primary Issues and Specifications 180

10.3 Fog Computing 181

10.3.1 Fog Computing Concept 183

10.3.2 Fog Technology Characterization 183

10.4 Related Works in Cloud and Fog Computing 185

10.5 Fog and Cloud Computing-based Technology Applications in VANET 186

10.6 Challenges of Fog Computing in VANET 188

10.7 Issues of Fog Computing in VANET 189

10.8 Conclusion 190

Bibliography 191

11 AN OVERVIEW TO DESIGN AN EFFICIENT AND SECURE FOG-ASSISTED DATA COLLECTION METHOD IN THE INTERNET OF THINGS 193

Sofia, Arun Malik, Isha, and Aditya Khamparia

11.1 Introduction 193

11.2 Related Works 194

11.3 Overview of the Chapter 196

11.4 Data Collection in the IoT 197

11.5 Fog Computing 197

11.5.1 Why fog Computing for Data Collection in IoT? 197

11.5.2 Architecture of Fog Computing 200

11.5.3 Features of Fog Computing 200

11.5.4 Threats of Fog Computing 202

11.5.5 Applications of Fog Computing with the IoT 203

11.6 Requirements for Designing a Data Collection Method 204

11.7 Conclusion 206

Bibliography 206

12 ROLE OF FOG COMPUTING PLATFORM IN ANALYTICS OF INTERNET OF THINGS- ISSUES, CHALLENGES AND OPPORTUNITIES 209

Mamoon Rashid and Umer Iqbal Wani

12.1 Introduction to Fog Computing 209

12.1.1 Hierarchical Fog Computing Architecture 210

12.1.2 Layered Fog Computing Architecture 212

12.1.3 Comparison of Fog and Cloud Computing 213

12.2 Introduction to Internet of Things 214

12.2.1 Overview of Internet of Things 214

12.3 Conceptual Architecture of Internet of Things 216

12.4 Relationship between Internet of Things and Fog Computing 217

12.5 Use of Fog Analytics in Internet of Things 218

12.6 Conclusion 218

Bibliography 218

13 A MEDICAL DIAGNOSIS OF URETHRAL STRICTURE USING INTUITIONISTIC FUZZY SETS 221

Prabjot Kaur and Maria Jamal

13.1 Introduction 221

13.2 Preliminaries 223

13.2.1 Introduction 223

13.2.2 Fuzzy Sets 223

13.2.3 Intuitionistic Fuzzy Sets 224

13.2.4 Intuitionistic Fuzzy Relation 224

13.2.5 Max-Min-Max Composition 224

13.2.6 Linguistic Variable 224

13.2.7 Distance Measure In Intuitionistic Fuzzy Sets 224

13.2.7.1 The Hamming Distance 224

13.2.7.2 Normalized Hamming Distance 224

13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix 225

13.2.7.4 Revised Max-Min Average Composition of A and B (A Φ B) 225

13.3 Max-Min-Max Algorithm for Disease Diagnosis 225

13.4 Case Study 226

13.5 Intuitionistic Fuzzy Max-Min Average Algorithm for Disease Diagnosis 227

13.6 Result 228

13.7 Code for Calculation 229

13.8 Conclusion 233

13.9 Acknowledgement 234

Bibliography 234

14 SECURITY ATTACKS IN INTERNET OF THINGS 237

Rajit Nair, Preeti Sharma, and Dileep Kumar Singh

14.1 Introduction 238

14.2 Reference Model of Internet of Things (IoT) 238

14.3 IoT Communication Protocol 246

14.4 IoT Security 247

14.4.1 Physical Attack 248

14.4.2 Network Attack 252

14.4.3 Software Attack 254

14.4.4 Encryption Attack 255

14.5 Security Challenges in IoT 256

14.5.1 Cryptographic Strategies 256

14.5.2 Key Administration 256

14.5.3 Denial of Service 256

14.5.4 Authentication and Access Control 257

14.6 Conclusion 257

Bibliography 257

15 FOG INTEGRATED NOVEL ARCHITECTURE FOR TELEHEALTH SERVICES WITH SWIFT MEDICAL DELIVERY 263

Inderpreet Kaur, Kamaljit Singh Saini, and Jaiteg Singh Khaira

15.1 Introduction 264

15.2 Associated Work and Dimensions 266

15.3 Need of Security in Telemedicine Domain and Internet of Things (IoT) 267

15.3.1 Analytics Reports 268

15.4 Fog Integrated Architecture for Telehealth Delivery 268

15.5 Research Dimensions 269

15.5.1 Benchmark Datasets 269

15.6 Research Methodology and Implementation on Software Defined Networking 270

15.6.1 Key Tools and Frameworks for IoT, Fog Computing and Edge Computing 274

15.6.2 Simulation Analysis 276

15.7 Conclusion 282

Bibliography 282

16 FRUIT FLY OPTIMIZATION ALGORITHM FOR INTELLIGENT IOT APPLICATIONS 287

Satinder Singh Mohar, Sonia Goyal, and Ranjit Kaur

16.1 An Introduction to the Internet of Things 287

16.2 Background of the IoT 288

16.2.1 Evolution of the IoT 288

16.2.2 Elements Involved in IoT Communication 288

16.3 Applications of the IoT 289

16.3.1 Industrial 290

16.3.2 Smart Parking 290

16.3.3 Health Care 290

16.3.4 Smart Offices and Homes 290

16.3.5 Augment Maps 291

16.3.6 Environment Monitoring 291

16.3.7 Agriculture 291

16.4 Challenges in the IoT 291

16.4.1 Addressing Schemes 291

16.4.2 Energy Consumption 292

16.4.3 Transmission Media 292

16.4.4 Security 292

16.4.5 Quality of Service (QoS) 292

16.5 Introduction to Optimization 293

16.6 Classification of Optimization Algorithms 293

16.6.1 Particle Swarm Optimization (PSO) Algorithm 293

16.6.2 Genetic Algorithms 294

16.6.3 Heuristic Algorithms 294

16.6.4 Bio-inspired Algorithms 294

16.6.5 Evolutionary Algorithms (EA) 294

16.7 Network Optimization and IoT 295

16.8 Network Parameters optimized by Different Optimization Algorithms 295

16.8.1 Load Balancing 295

16.8.2 Maximizing Network Lifetime 295

16.8.3 Link Failure Management 296

16.8.4 Quality of the Link 296

16.8.5 Energy Efficiency 296

16.8.6 Node Deployment 296

16.9 Fruit Fly Optimization Algorithm 297

16.9.1 Steps Involved in FOA 297

16.9.2 Flow Chart of Fruit Fly Optimization Algorithm 298

16.10 Applicability of FOA in IoT Applications 300

16.10.1 Cloud Service Distribution in Fog Computing 300

16.10.2 Cluster Head Selection in IoT 300

16.10.3 Load Balancing in IoT 300

16.10.4 Quality of Service in Web Services 300

16.10.5 Electronics Health Records in Cloud Computing 301

16.10.6 Intrusion Detection System in Network 301

16.10.7 Node Capture Attack in WSN 301

16.10.8 Node Deployment in WSN 302

16.11 Node Deployment Using Fruit Fly Optimization Algorithm 302

16.12 Conclusion 304

Bibliography 304

17 OPTIMIZATION TECHNIQUES FOR INTELLIGENT IOT APPLICATIONS 311

Priyanka Pattnaik, Subhashree Mishra, and Bhabani Shankar Prasad Mishra

17.1 Cuckoo Search 312

17.1.1 Introduction to Cuckoo 312

17.1.2 Natural Cuckoo 312

17.1.3 Artificial Cuckoo Search 313

17.1.4 Cuckoo Search Algorithm 313

17.1.5 Cuckoo Search Variants 314

17.1.6 Discrete Cuckoo Search 314

17.1.7 Binary Cuckoo Search 314

17.1.8 Chaotic Cuckoo Search 316

17.1.9 Parallel Cuckoo Search 317

17.1.10 Application of Cuckoo Search 317

17.2 Glow Worm Algorithm 317

17.2.1 Introduction to Glow Worm 317

17.2.2 Glow Worm Swarm Optimization Algorithm (GSO) 317

17.3 Wasp Swarm Optimization 321

17.3.1 Introduction to Wasp Swarm and Wasp Swarm Algorithm (WSO) 321

17.3.2 Fish Swarm Optimization (FSO) 322

17.3.3 Fruit Fly Optimization (FLO) 322

17.3.4 Cockroach Swarm Optimization 324

17.3.5 Bumblebee Algorithm 324

17.3.6 Dolphin Echolocation 325

17.3.7 Shuffled Frog-leaping Algorithm 326

17.3.8 Paddy Field Algorithm 327

17.4 Real World Applications Area 328

Summary 329

Bibliography 329

18 OPTIMIZATION TECHNIQUES FOR INTELLIGENT IOT APPLICATIONS IN TRANSPORT PROCESSES 333

Muzafer Saračević, Zoran Lončarević, and Adnan Hasanović

18.1 Introduction 333

18.2 Related Works 335

18.3 TSP Optimization Techniques 336

18.4 Implementation and Testing of Proposed Solution 338

18.5 Experimental Results 342

18.5.1 Example Test with 50 Cities 343

18.5.2 Example Test with 100 Cities 344

18.6 Conclusion and Further Works 346

Bibliography 347

19 ROLE OF INTELLIGENT IOT APPLICATIONS IN FOG PARADIGM: ISSUES, CHALLENGES AND FUTURE OPPORTUNITIES 351

Priyanka Rajan Kumar and Sonia Goel

19.1 Fog Computing 352

19.1.1 Need of Fog computing 352

19.1.2 Architecture of Fog Computing 353

19.1.3 Fog Computing Reference Architecture 354

19.1.4 Processing on Fog 355

19.2 Concept of Intelligent IoT Applications in Smart Computing Era 355

19.3 Components of Edge and Fog Driven Algorithm 356

19.4 Working of Edge and Fog Driven Algorithms 357

19.5 Future Opportunistic Fog/Edge Computational Models 360

19.5.1 Future Opportunistic Techniques 361

19.6 Challenges of Fog Computing for Intelligent IoT Applications 361

19.7 Applications of Cloud Based Computing for Smart Devices 363

Bibliography 364

20 SECURITY AND PRIVACY ISSUES IN FOG/EDGE/PERVASIVE COMPUTING 369

Shweta Kaushik and Charu Gandhi

20.1 Introduction to Data Security and Privacy in Fog Computing 370

20.2 Data Protection/ Security 375

20.3 Great Security Practices In Fog Processing Condition 377

20.4 Developing Patterns in Security and Privacy 381

20.5 Conclusion 385

Bibliography 385

21 FOG AND EDGE DRIVEN SECURITY & PRIVACY ISSUES IN IOT DEVICES 389

Deepak Kumar Sharma, Aarti Goel, and Pragun Mangla

21.1 Introduction to Fog Computing 390

21.1.1 Architecture of Fog 390

21.1.2 Benefits of Fog Computing 392

21.1.3 Applications of Fog with IoT 393

21.1.4 Major Challenges for Fog with IoT 394

21.1.5 Security and Privacy Issues in Fog Computing 395

21.2 Introduction to Edge Computing 399

21.2.1 Architecture and Working 400

21.2.2 Applications and use Cases 400

21.2.3 Characteristics of Edge Computing 403

21.2.4 Challenges of Edge Computing 404

21.2.5 How to Protect Devices “On the Edge”? 405

21.2.6 Comparison with Fog Computing 405

Bibliography 406

Index 409