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 1Afroj 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 27Sonia 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 47Hatim 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 67Murali 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 85Md 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 105Nikita, 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 123Saravjeet 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 149Natasha 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 161Md 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 177Harjit 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 193Sofia, 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 209Mamoon 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 221Prabjot 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 237Rajit 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 263Inderpreet 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 287Satinder 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 311Priyanka 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 333Muzafer 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 351Priyanka 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 369Shweta 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 389Deepak 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

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 computingConsiders probabilistic storage systems and proven optimization techniques for intelligent IoTCovers 5G edge network slicing and virtual network systems that utilize new networking capacityExplores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applicationsPresents 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.