{"product_id":"fog-edge-and-pervasive-computing-in-intelligent-iot-driven-applications-isbn-9781119670070","title":"Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications","description":"\u003cp\u003e\u003cb\u003eA practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith 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. \u003ci\u003eFog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications\u003c\/i\u003e 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDescribes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing\u003c\/li\u003e \u003cli\u003eConsiders probabilistic storage systems and proven optimization techniques for intelligent IoT\u003c\/li\u003e \u003cli\u003eCovers 5G edge network slicing and virtual network systems that utilize new networking capacity\u003c\/li\u003e \u003cli\u003eExplores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications\u003c\/li\u003e \u003cli\u003ePresents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearchers, 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 \u003ci\u003eFog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications \u003c\/i\u003eprovides the background, orientation, and inspiration needed to begin.\u003c\/p\u003e \u003cp\u003eAbout the Editors xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xix\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eAcknowledgments xxxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use \u003c\/b\u003e\u003cb\u003e1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAfroj Alam, Sahar Qazi, Naiyar Iqbal, and Khalid Raza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Why Fog, Edge, and Pervasive Computing? 3\u003c\/p\u003e \u003cp\u003e1.3 Technologies Related to Fog and Edge Computing 6\u003c\/p\u003e \u003cp\u003e1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era 9\u003c\/p\u003e \u003cp\u003e1.5 The Hierarchical Architecture of Fog\/Edge Computing 12\u003c\/p\u003e \u003cp\u003e1.6 Applications of Fog, Edge and Pervasive Computing in IoT-based Healthcare 15\u003c\/p\u003e \u003cp\u003e1.7 Issues, Challenges, and Opportunity 17\u003c\/p\u003e \u003cp\u003e1.7.1 Security and Privacy Issues 18\u003c\/p\u003e \u003cp\u003e1.7.2 Resource Management 19\u003c\/p\u003e \u003cp\u003e1.7.3 Programming Platform 19\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 20\u003c\/p\u003e \u003cp\u003eBibliography 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Future Opportunistic Fog\/Edge Computational Models and their Limitations \u003c\/b\u003e\u003cb\u003e27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSonia Singla, Naveen Kumar Bhati, and S. Aswath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 28\u003c\/p\u003e \u003cp\u003e2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? 32\u003c\/p\u003e \u003cp\u003e2.3 Disadvantages 34\u003c\/p\u003e \u003cp\u003e2.4 Challenges 34\u003c\/p\u003e \u003cp\u003e2.5 Role in Health Care 35\u003c\/p\u003e \u003cp\u003e2.6 Blockchain and Fog, Edge Computing 38\u003c\/p\u003e \u003cp\u003e2.7 How Blockchain will Illuminate Human Services Issues 40\u003c\/p\u003e \u003cp\u003e2.8 Uses of Blockchain in the Future 41\u003c\/p\u003e \u003cp\u003e2.9 Uses of Blockchain in Health Care 42\u003c\/p\u003e \u003cp\u003e2.10 Edge Computing Segmental Analysis 42\u003c\/p\u003e \u003cp\u003e2.11 Uses of Fog Computing 43\u003c\/p\u003e \u003cp\u003e2.12 Analytics in Fog Computing 44\u003c\/p\u003e \u003cp\u003e2.13 Conclusion 44\u003c\/p\u003e \u003cp\u003eBibliography 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Automating Elicitation Technique Selection using Machine Learning \u003c\/b\u003e\u003cb\u003e47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHatim M. Elhassan Ibrahim Dafallaa, Nazir Ahmad, Mohammed Burhanur Rehman, Iqrar Ahmad, and Rizwan khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Related Work 48\u003c\/p\u003e \u003cp\u003e3.3 Model: Requirement Elicitation Technique Selection Model 52\u003c\/p\u003e \u003cp\u003e3.3.1 Determining Key Attributes 54\u003c\/p\u003e \u003cp\u003e3.3.2 Selection Attributes 54\u003c\/p\u003e \u003cp\u003e3.3.2.1 Analyst Experience 55\u003c\/p\u003e \u003cp\u003e3.3.2.2 Number of Stakeholders 55\u003c\/p\u003e \u003cp\u003e3.3.2.3 Technique Time 56\u003c\/p\u003e \u003cp\u003e3.3.2.4 Level of Information 56\u003c\/p\u003e \u003cp\u003e3.3.3 Selection Attributes Dataset 56\u003c\/p\u003e \u003cp\u003e3.3.3.1 Mapping the Selection Attributes 57\u003c\/p\u003e \u003cp\u003e3.3.4 \u003ci\u003ek\u003c\/i\u003e-nearest Neighbor Algorithm Application 57\u003c\/p\u003e \u003cp\u003e3.4 Analysis and Results 60\u003c\/p\u003e \u003cp\u003e3.5 The Error Rate 61\u003c\/p\u003e \u003cp\u003e3.6 Validation 61\u003c\/p\u003e \u003cp\u003e3.6.1 Discussion of the Results of the Experiment 62\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 62\u003c\/p\u003e \u003cp\u003eBibliography 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing \u003c\/b\u003e\u003cb\u003e67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMurali Mallikarjuna Rao Perumalla, Sanjay Kumar Singh, Aditya Khamparia, Anjali Goyal, and Ashish Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 68\u003c\/p\u003e \u003cp\u003e4.1.1 Fog Computing and Edge Computing 68\u003c\/p\u003e \u003cp\u003e4.1.2 Pervasive Computing 68\u003c\/p\u003e \u003cp\u003e4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing 69\u003c\/p\u003e \u003cp\u003e4.2.1 TensorFlow 69\u003c\/p\u003e \u003cp\u003e4.2.2 Keras 70\u003c\/p\u003e \u003cp\u003e4.2.3 PyTorch 70\u003c\/p\u003e \u003cp\u003e4.2.4 TensorFlow Lite 70\u003c\/p\u003e \u003cp\u003e4.2.4.1 Use Pre-train Models 70\u003c\/p\u003e \u003cp\u003e4.2.4.2 Convert the Model 70\u003c\/p\u003e \u003cp\u003e4.2.4.3 On-device Inference 71\u003c\/p\u003e \u003cp\u003e4.2.4.4 Model Optimization 71\u003c\/p\u003e \u003cp\u003e4.2.5 Machine Learning and Deep Learning Techniques 71\u003c\/p\u003e \u003cp\u003e4.2.5.1 Supervised, Unsupervised and Reinforcement Learning 71\u003c\/p\u003e \u003cp\u003e4.2.5.2 Machine Learning, Deep Learning Techniques 72\u003c\/p\u003e \u003cp\u003e4.2.5.3 Deep Learning Techniques 75\u003c\/p\u003e \u003cp\u003e4.2.5.4 Efficient Deep Learning Algorithms for Inference 77\u003c\/p\u003e \u003cp\u003e4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing 78\u003c\/p\u003e \u003cp\u003e4.2.6.1 Advantages using ML Algorithms 78\u003c\/p\u003e \u003cp\u003e4.2.6.2 Disadvantages of using ML Algorithms 79\u003c\/p\u003e \u003cp\u003e4.2.7 Hybrid ML Model for Smart IoT Applications 79\u003c\/p\u003e \u003cp\u003e4.2.7.1 Multi-Task Learning 79\u003c\/p\u003e \u003cp\u003e4.2.7.2 Ensemble Learning 80\u003c\/p\u003e \u003cp\u003e4.2.8 Possible Applications in Fog Era using Machine Learning 81\u003c\/p\u003e \u003cp\u003e4.2.8.1 Computer Vision 81\u003c\/p\u003e \u003cp\u003e4.2.8.2 ML- Assisted Healthcare Monitoring System 81\u003c\/p\u003e \u003cp\u003e4.2.8.3 Smart Homes 81\u003c\/p\u003e \u003cp\u003e4.2.8.4 Behavior Analyses 82\u003c\/p\u003e \u003cp\u003e4.2.8.5 Monitoring in Remote Areas and Industries 82\u003c\/p\u003e \u003cp\u003e4.2.8.6 Self-Driving Cars 82\u003c\/p\u003e \u003cp\u003eBibliography 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications \u003c\/b\u003e\u003cb\u003e85\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMd Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 86\u003c\/p\u003e \u003cp\u003e5.1.1 Execution Plan for the Mobile Application 86\u003c\/p\u003e \u003cp\u003e5.1.2 Main Contribution 86\u003c\/p\u003e \u003cp\u003e5.2 Understanding Library Management 87\u003c\/p\u003e \u003cp\u003e5.3 Integration of Mobile Platform with the Physical Library- Brief Concept 88\u003c\/p\u003e \u003cp\u003e5.4 Database (Cloud Based) - A Must have Component for Library Automation 88\u003c\/p\u003e \u003cp\u003e5.5 IoT Driven Mobile Based Library Management - General Concept 89\u003c\/p\u003e \u003cp\u003e5.6 IoT Involved Real Time GUI (Cross Platform) Available to User 93\u003c\/p\u003e \u003cp\u003e5.7 IoT Challenges 98\u003c\/p\u003e \u003cp\u003e5.7.1 Infrastructure Challenges 99\u003c\/p\u003e \u003cp\u003e5.7.2 Security Challenges 99\u003c\/p\u003e \u003cp\u003e5.7.3 Societal Challenges 100\u003c\/p\u003e \u003cp\u003e5.7.4 Commercial Challenges 101\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 102\u003c\/p\u003e \u003cp\u003eBibliography 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer \u003c\/b\u003e\u003cb\u003e105\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNikita, Harsh Sadawarti, Balwinder Kaur, and Jimmy Singla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 106\u003c\/p\u003e \u003cp\u003e6.2 Related Works 107\u003c\/p\u003e \u003cp\u003e6.3 Conclusion 119\u003c\/p\u003e \u003cp\u003eBibliography 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation \u003c\/b\u003e\u003cb\u003e123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSaravjeet Singh and Jaiteg Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Fog and Edge Computing 124\u003c\/p\u003e \u003cp\u003e7.1.1 Need for Fog and Edge Computing 124\u003c\/p\u003e \u003cp\u003e7.1.2 Fog Computing 125\u003c\/p\u003e \u003cp\u003e7.1.2.1 Application Areas of Fog Computing 125\u003c\/p\u003e \u003cp\u003e7.1.3 Edge Computing 126\u003c\/p\u003e \u003cp\u003e7.1.3.1 Advantages of Edge Computing 127\u003c\/p\u003e \u003cp\u003e7.1.3.2 Application Areas of Fog Computing 129\u003c\/p\u003e \u003cp\u003e7.2 Introduction to Transportation System 129\u003c\/p\u003e \u003cp\u003e7.3 Route Finding Process 131\u003c\/p\u003e \u003cp\u003e7.3.1 Challenges Associated with Land Navigation and Routing Process 132\u003c\/p\u003e \u003cp\u003e7.4 Edge Architecture for Route Finding 133\u003c\/p\u003e \u003cp\u003e7.5 Technique Used 135\u003c\/p\u003e \u003cp\u003e7.6 Algorithms Used for the Location Identification and Route Finding Process 137\u003c\/p\u003e \u003cp\u003e7.6.1 Location Identification 137\u003c\/p\u003e \u003cp\u003e7.6.2 Path Generation Technique 138\u003c\/p\u003e \u003cp\u003e7.7 Results and Discussions 140\u003c\/p\u003e \u003cp\u003e7.7.1 Output 140\u003c\/p\u003e \u003cp\u003e7.7.2 Benefits of Edge-based Routing 143\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 145\u003c\/p\u003e \u003cp\u003eBibliography 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator \u003c\/b\u003e\u003cb\u003e149\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNatasha Tiwari, Anil Kumar, Pallavi Asthana, Sumita Mishra, and Bramah Hazela\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 149\u003c\/p\u003e \u003cp\u003e8.2 Related Work 151\u003c\/p\u003e \u003cp\u003e8.3 Vehicle Condition Monitoring through Acoustic Emission 151\u003c\/p\u003e \u003cp\u003e8.4 Piezo-resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE 152\u003c\/p\u003e \u003cp\u003e8.5 Designing of MEM Sensor 153\u003c\/p\u003e \u003cp\u003e8.6 Experimental Setup 153\u003c\/p\u003e \u003cp\u003e8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB 155\u003c\/p\u003e \u003cp\u003e8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 155\u003c\/p\u003e \u003cp\u003e8.6.3 Electrostatic Study Steps for the Optimized Tri-plate Comb Structure 156\u003c\/p\u003e \u003cp\u003e8.7 Result and Discussions 157\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 158\u003c\/p\u003e \u003cp\u003eBibliography 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 IoT Driven Healthcare Monitoring System \u003c\/b\u003e\u003cb\u003e161\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMd Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 161\u003c\/p\u003e \u003cp\u003e9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications 162\u003c\/p\u003e \u003cp\u003e9.1.2 Main Contribution 164\u003c\/p\u003e \u003cp\u003e9.2 General Concept for IoT Based Healthcare System 164\u003c\/p\u003e \u003cp\u003e9.3 View of the Overall IoT Healthcare System- Tiers Explained 165\u003c\/p\u003e \u003cp\u003e9.4 A Brief Design of the IoT Healthcare Architecture-individual Block Explanation 166\u003c\/p\u003e \u003cp\u003e9.5 Models\/Frameworks for IoT use in Healthcare 168\u003c\/p\u003e \u003cp\u003e9.6 IoT e-Health System Model 171\u003c\/p\u003e \u003cp\u003e9.7 Process Flow for the Overall Model 172\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 173\u003c\/p\u003e \u003cp\u003eBibliography 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks \u003c\/b\u003e\u003cb\u003e177\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHarjit Singh, Dr. Vijay Laxmi, Dr. Arun Malik, and Dr. Isha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 178\u003c\/p\u003e \u003cp\u003e10.2 Future VANET: Primary Issues and Specifications 180\u003c\/p\u003e \u003cp\u003e10.3 Fog Computing 181\u003c\/p\u003e \u003cp\u003e10.3.1 Fog Computing Concept 183\u003c\/p\u003e \u003cp\u003e10.3.2 Fog Technology Characterization 183\u003c\/p\u003e \u003cp\u003e10.4 Related Works in Cloud and Fog Computing 185\u003c\/p\u003e \u003cp\u003e10.5 Fog and Cloud Computing-based Technology Applications in VANET 186\u003c\/p\u003e \u003cp\u003e10.6 Challenges of Fog Computing in VANET 188\u003c\/p\u003e \u003cp\u003e10.7 Issues of Fog Computing in VANET 189\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 190\u003c\/p\u003e \u003cp\u003eBibliography 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 An Overview to Design an Efficient and Secure Fog-assisted Data Collection Method in the Internet of Things \u003c\/b\u003e\u003cb\u003e193\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSofia, Arun Malik, Isha, and Aditya Khamparia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 193\u003c\/p\u003e \u003cp\u003e11.2 Related Works 194\u003c\/p\u003e \u003cp\u003e11.3 Overview of the Chapter 196\u003c\/p\u003e \u003cp\u003e11.4 Data Collection in the IoT 197\u003c\/p\u003e \u003cp\u003e11.5 Fog Computing 197\u003c\/p\u003e \u003cp\u003e11.5.1 Why fog Computing for Data Collection in IoT? 197\u003c\/p\u003e \u003cp\u003e11.5.2 Architecture of Fog Computing 200\u003c\/p\u003e \u003cp\u003e11.5.3 Features of Fog Computing 200\u003c\/p\u003e \u003cp\u003e11.5.4 Threats of Fog Computing 202\u003c\/p\u003e \u003cp\u003e11.5.5 Applications of Fog Computing with the IoT 203\u003c\/p\u003e \u003cp\u003e11.6 Requirements for Designing a Data Collection Method 204\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 206\u003c\/p\u003e \u003cp\u003eBibliography 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Role of Fog Computing Platform in Analytics of Internet of Things- Issues, Challenges and Opportunities \u003c\/b\u003e\u003cb\u003e209\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMamoon Rashid and Umer Iqbal Wani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction to Fog Computing 209\u003c\/p\u003e \u003cp\u003e12.1.1 Hierarchical Fog Computing Architecture 210\u003c\/p\u003e \u003cp\u003e12.1.2 Layered Fog Computing Architecture 212\u003c\/p\u003e \u003cp\u003e12.1.3 Comparison of Fog and Cloud Computing 213\u003c\/p\u003e \u003cp\u003e12.2 Introduction to Internet of Things 214\u003c\/p\u003e \u003cp\u003e12.2.1 Overview of Internet of Things 214\u003c\/p\u003e \u003cp\u003e12.3 Conceptual Architecture of Internet of Things 216\u003c\/p\u003e \u003cp\u003e12.4 Relationship between Internet of Things and Fog Computing 217\u003c\/p\u003e \u003cp\u003e12.5 Use of Fog Analytics in Internet of Things 218\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 218\u003c\/p\u003e \u003cp\u003eBibliography 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets \u003c\/b\u003e\u003cb\u003e221\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrabjot Kaur and Maria Jamal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 221\u003c\/p\u003e \u003cp\u003e13.2 Preliminaries 223\u003c\/p\u003e \u003cp\u003e13.2.1 Introduction 223\u003c\/p\u003e \u003cp\u003e13.2.2 Fuzzy Sets 223\u003c\/p\u003e \u003cp\u003e13.2.3 Intuitionistic Fuzzy Sets 224\u003c\/p\u003e \u003cp\u003e13.2.4 Intuitionistic Fuzzy Relation 224\u003c\/p\u003e \u003cp\u003e13.2.5 Max-Min-Max Composition 224\u003c\/p\u003e \u003cp\u003e13.2.6 Linguistic Variable 224\u003c\/p\u003e \u003cp\u003e13.2.7 Distance Measure In Intuitionistic Fuzzy Sets 224\u003c\/p\u003e \u003cp\u003e13.2.7.1 The Hamming Distance 224\u003c\/p\u003e \u003cp\u003e13.2.7.2 Normalized Hamming Distance 224\u003c\/p\u003e \u003cp\u003e13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix 225\u003c\/p\u003e \u003cp\u003e13.2.7.4 Revised Max-Min Average Composition of A and B (A Φ B) 225\u003c\/p\u003e \u003cp\u003e13.3 Max-Min-Max Algorithm for Disease Diagnosis 225\u003c\/p\u003e \u003cp\u003e13.4 Case Study 226\u003c\/p\u003e \u003cp\u003e13.5 Intuitionistic Fuzzy Max-Min Average Algorithm for Disease Diagnosis 227\u003c\/p\u003e \u003cp\u003e13.6 Result 228\u003c\/p\u003e \u003cp\u003e13.7 Code for Calculation 229\u003c\/p\u003e \u003cp\u003e13.8 Conclusion 233\u003c\/p\u003e \u003cp\u003e13.9 Acknowledgement 234\u003c\/p\u003e \u003cp\u003eBibliography 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Security Attacks in Internet of Things \u003c\/b\u003e\u003cb\u003e237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRajit Nair, Preeti Sharma, and Dileep Kumar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 238\u003c\/p\u003e \u003cp\u003e14.2 Reference Model of Internet of Things (IoT) 238\u003c\/p\u003e \u003cp\u003e14.3 IoT Communication Protocol 246\u003c\/p\u003e \u003cp\u003e14.4 IoT Security 247\u003c\/p\u003e \u003cp\u003e14.4.1 Physical Attack 248\u003c\/p\u003e \u003cp\u003e14.4.2 Network Attack 252\u003c\/p\u003e \u003cp\u003e14.4.3 Software Attack 254\u003c\/p\u003e \u003cp\u003e14.4.4 Encryption Attack 255\u003c\/p\u003e \u003cp\u003e14.5 Security Challenges in IoT 256\u003c\/p\u003e \u003cp\u003e14.5.1 Cryptographic Strategies 256\u003c\/p\u003e \u003cp\u003e14.5.2 Key Administration 256\u003c\/p\u003e \u003cp\u003e14.5.3 Denial of Service 256\u003c\/p\u003e \u003cp\u003e14.5.4 Authentication and Access Control 257\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 257\u003c\/p\u003e \u003cp\u003eBibliography 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery \u003c\/b\u003e\u003cb\u003e263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eInderpreet Kaur, Kamaljit Singh Saini, and Jaiteg Singh Khaira\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 264\u003c\/p\u003e \u003cp\u003e15.2 Associated Work and Dimensions 266\u003c\/p\u003e \u003cp\u003e15.3 Need of Security in Telemedicine Domain and Internet of Things (IoT) 267\u003c\/p\u003e \u003cp\u003e15.3.1 Analytics Reports 268\u003c\/p\u003e \u003cp\u003e15.4 Fog Integrated Architecture for Telehealth Delivery 268\u003c\/p\u003e \u003cp\u003e15.5 Research Dimensions 269\u003c\/p\u003e \u003cp\u003e15.5.1 Benchmark Datasets 269\u003c\/p\u003e \u003cp\u003e15.6 Research Methodology and Implementation on Software Defined Networking 270\u003c\/p\u003e \u003cp\u003e15.6.1 Key Tools and Frameworks for IoT, Fog Computing and Edge Computing 274\u003c\/p\u003e \u003cp\u003e15.6.2 Simulation Analysis 276\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 282\u003c\/p\u003e \u003cp\u003eBibliography 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Fruit Fly Optimization Algorithm for Intelligent IoT Applications \u003c\/b\u003e\u003cb\u003e287\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSatinder Singh Mohar, Sonia Goyal, and Ranjit Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 An Introduction to the Internet of Things 287\u003c\/p\u003e \u003cp\u003e16.2 Background of the IoT 288\u003c\/p\u003e \u003cp\u003e16.2.1 Evolution of the IoT 288\u003c\/p\u003e \u003cp\u003e16.2.2 Elements Involved in IoT Communication 288\u003c\/p\u003e \u003cp\u003e16.3 Applications of the IoT 289\u003c\/p\u003e \u003cp\u003e16.3.1 Industrial 290\u003c\/p\u003e \u003cp\u003e16.3.2 Smart Parking 290\u003c\/p\u003e \u003cp\u003e16.3.3 Health Care 290\u003c\/p\u003e \u003cp\u003e16.3.4 Smart Offices and Homes 290\u003c\/p\u003e \u003cp\u003e16.3.5 Augment Maps 291\u003c\/p\u003e \u003cp\u003e16.3.6 Environment Monitoring 291\u003c\/p\u003e \u003cp\u003e16.3.7 Agriculture 291\u003c\/p\u003e \u003cp\u003e16.4 Challenges in the IoT 291\u003c\/p\u003e \u003cp\u003e16.4.1 Addressing Schemes 291\u003c\/p\u003e \u003cp\u003e16.4.2 Energy Consumption 292\u003c\/p\u003e \u003cp\u003e16.4.3 Transmission Media 292\u003c\/p\u003e \u003cp\u003e16.4.4 Security 292\u003c\/p\u003e \u003cp\u003e16.4.5 Quality of Service (QoS) 292\u003c\/p\u003e \u003cp\u003e16.5 Introduction to Optimization 293\u003c\/p\u003e \u003cp\u003e16.6 Classification of Optimization Algorithms 293\u003c\/p\u003e \u003cp\u003e16.6.1 Particle Swarm Optimization (PSO) Algorithm 293\u003c\/p\u003e \u003cp\u003e16.6.2 Genetic Algorithms 294\u003c\/p\u003e \u003cp\u003e16.6.3 Heuristic Algorithms 294\u003c\/p\u003e \u003cp\u003e16.6.4 Bio-inspired Algorithms 294\u003c\/p\u003e \u003cp\u003e16.6.5 Evolutionary Algorithms (EA) 294\u003c\/p\u003e \u003cp\u003e16.7 Network Optimization and IoT 295\u003c\/p\u003e \u003cp\u003e16.8 Network Parameters optimized by Different Optimization Algorithms 295\u003c\/p\u003e \u003cp\u003e16.8.1 Load Balancing 295\u003c\/p\u003e \u003cp\u003e16.8.2 Maximizing Network Lifetime 295\u003c\/p\u003e \u003cp\u003e16.8.3 Link Failure Management 296\u003c\/p\u003e \u003cp\u003e16.8.4 Quality of the Link 296\u003c\/p\u003e \u003cp\u003e16.8.5 Energy Efficiency 296\u003c\/p\u003e \u003cp\u003e16.8.6 Node Deployment 296\u003c\/p\u003e \u003cp\u003e16.9 Fruit Fly Optimization Algorithm 297\u003c\/p\u003e \u003cp\u003e16.9.1 Steps Involved in FOA 297\u003c\/p\u003e \u003cp\u003e16.9.2 Flow Chart of Fruit Fly Optimization Algorithm 298\u003c\/p\u003e \u003cp\u003e16.10 Applicability of FOA in IoT Applications 300\u003c\/p\u003e \u003cp\u003e16.10.1 Cloud Service Distribution in Fog Computing 300\u003c\/p\u003e \u003cp\u003e16.10.2 Cluster Head Selection in IoT 300\u003c\/p\u003e \u003cp\u003e16.10.3 Load Balancing in IoT 300\u003c\/p\u003e \u003cp\u003e16.10.4 Quality of Service in Web Services 300\u003c\/p\u003e \u003cp\u003e16.10.5 Electronics Health Records in Cloud Computing 301\u003c\/p\u003e \u003cp\u003e16.10.6 Intrusion Detection System in Network 301\u003c\/p\u003e \u003cp\u003e16.10.7 Node Capture Attack in WSN 301\u003c\/p\u003e \u003cp\u003e16.10.8 Node Deployment in WSN 302\u003c\/p\u003e \u003cp\u003e16.11 Node Deployment Using Fruit Fly Optimization Algorithm 302\u003c\/p\u003e \u003cp\u003e16.12 Conclusion 304\u003c\/p\u003e \u003cp\u003eBibliography 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Optimization Techniques for Intelligent IoT Applications \u003c\/b\u003e\u003cb\u003e311\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyanka Pattnaik, Subhashree Mishra, and Bhabani Shankar Prasad Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Cuckoo Search 312\u003c\/p\u003e \u003cp\u003e17.1.1 Introduction to Cuckoo 312\u003c\/p\u003e \u003cp\u003e17.1.2 Natural Cuckoo 312\u003c\/p\u003e \u003cp\u003e17.1.3 Artificial Cuckoo Search 313\u003c\/p\u003e \u003cp\u003e17.1.4 Cuckoo Search Algorithm 313\u003c\/p\u003e \u003cp\u003e17.1.5 Cuckoo Search Variants 314\u003c\/p\u003e \u003cp\u003e17.1.6 Discrete Cuckoo Search 314\u003c\/p\u003e \u003cp\u003e17.1.7 Binary Cuckoo Search 314\u003c\/p\u003e \u003cp\u003e17.1.8 Chaotic Cuckoo Search 316\u003c\/p\u003e \u003cp\u003e17.1.9 Parallel Cuckoo Search 317\u003c\/p\u003e \u003cp\u003e17.1.10 Application of Cuckoo Search 317\u003c\/p\u003e \u003cp\u003e17.2 Glow Worm Algorithm 317\u003c\/p\u003e \u003cp\u003e17.2.1 Introduction to Glow Worm 317\u003c\/p\u003e \u003cp\u003e17.2.2 Glow Worm Swarm Optimization Algorithm (GSO) 317\u003c\/p\u003e \u003cp\u003e17.3 Wasp Swarm Optimization 321\u003c\/p\u003e \u003cp\u003e17.3.1 Introduction to Wasp Swarm and Wasp Swarm Algorithm (WSO) 321\u003c\/p\u003e \u003cp\u003e17.3.2 Fish Swarm Optimization (FSO) 322\u003c\/p\u003e \u003cp\u003e17.3.3 Fruit Fly Optimization (FLO) 322\u003c\/p\u003e \u003cp\u003e17.3.4 Cockroach Swarm Optimization 324\u003c\/p\u003e \u003cp\u003e17.3.5 Bumblebee Algorithm 324\u003c\/p\u003e \u003cp\u003e17.3.6 Dolphin Echolocation 325\u003c\/p\u003e \u003cp\u003e17.3.7 Shuffled Frog-leaping Algorithm 326\u003c\/p\u003e \u003cp\u003e17.3.8 Paddy Field Algorithm 327\u003c\/p\u003e \u003cp\u003e17.4 Real World Applications Area 328\u003c\/p\u003e \u003cp\u003eSummary 329\u003c\/p\u003e \u003cp\u003eBibliography 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Optimization Techniques for Intelligent IoT Applications in Transport Processes \u003c\/b\u003e\u003cb\u003e333\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMuzafer Sara\u003c\/i\u003e\u003ci\u003ečević, Zoran Lon\u003c\/i\u003e\u003ci\u003ečarević, and Adnan Hasanović\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 333\u003c\/p\u003e \u003cp\u003e18.2 Related Works 335\u003c\/p\u003e \u003cp\u003e18.3 TSP Optimization Techniques 336\u003c\/p\u003e \u003cp\u003e18.4 Implementation and Testing of Proposed Solution 338\u003c\/p\u003e \u003cp\u003e18.5 Experimental Results 342\u003c\/p\u003e \u003cp\u003e18.5.1 Example Test with 50 Cities 343\u003c\/p\u003e \u003cp\u003e18.5.2 Example Test with 100 Cities 344\u003c\/p\u003e \u003cp\u003e18.6 Conclusion and Further Works 346\u003c\/p\u003e \u003cp\u003eBibliography 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities \u003c\/b\u003e\u003cb\u003e351\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyanka Rajan Kumar and Sonia Goel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Fog Computing 352\u003c\/p\u003e \u003cp\u003e19.1.1 Need of Fog computing 352\u003c\/p\u003e \u003cp\u003e19.1.2 Architecture of Fog Computing 353\u003c\/p\u003e \u003cp\u003e19.1.3 Fog Computing Reference Architecture 354\u003c\/p\u003e \u003cp\u003e19.1.4 Processing on Fog 355\u003c\/p\u003e \u003cp\u003e19.2 Concept of Intelligent IoT Applications in Smart Computing Era 355\u003c\/p\u003e \u003cp\u003e19.3 Components of Edge and Fog Driven Algorithm 356\u003c\/p\u003e \u003cp\u003e19.4 Working of Edge and Fog Driven Algorithms 357\u003c\/p\u003e \u003cp\u003e19.5 Future Opportunistic Fog\/Edge Computational Models 360\u003c\/p\u003e \u003cp\u003e19.5.1 Future Opportunistic Techniques 361\u003c\/p\u003e \u003cp\u003e19.6 Challenges of Fog Computing for Intelligent IoT Applications 361\u003c\/p\u003e \u003cp\u003e19.7 Applications of Cloud Based Computing for Smart Devices 363\u003c\/p\u003e \u003cp\u003eBibliography 364\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Security and Privacy Issues in Fog\/Edge\/Pervasive Computing \u003c\/b\u003e\u003cb\u003e369\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShweta Kaushik and Charu Gandhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction to Data Security and Privacy in Fog Computing 370\u003c\/p\u003e \u003cp\u003e20.2 Data Protection\/ Security 375\u003c\/p\u003e \u003cp\u003e20.3 Great Security Practices In Fog Processing Condition 377\u003c\/p\u003e \u003cp\u003e20.4 Developing Patterns in Security and Privacy 381\u003c\/p\u003e \u003cp\u003e20.5 Conclusion 385\u003c\/p\u003e \u003cp\u003eBibliography 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Fog and Edge Driven Security \u0026amp; Privacy Issues in IoT Devices \u003c\/b\u003e\u003cb\u003e389\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDeepak Kumar Sharma, Aarti Goel, and Pragun Mangla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction to Fog Computing 390\u003c\/p\u003e \u003cp\u003e21.1.1 Architecture of Fog 390\u003c\/p\u003e \u003cp\u003e21.1.2 Benefits of Fog Computing 392\u003c\/p\u003e \u003cp\u003e21.1.3 Applications of Fog with IoT 393\u003c\/p\u003e \u003cp\u003e21.1.4 Major Challenges for Fog with IoT 394\u003c\/p\u003e \u003cp\u003e21.1.5 Security and Privacy Issues in Fog Computing 395\u003c\/p\u003e \u003cp\u003e21.2 Introduction to Edge Computing 399\u003c\/p\u003e \u003cp\u003e21.2.1 Architecture and Working 400\u003c\/p\u003e \u003cp\u003e21.2.2 Applications and use Cases 400\u003c\/p\u003e \u003cp\u003e21.2.3 Characteristics of Edge Computing 403\u003c\/p\u003e \u003cp\u003e21.2.4 Challenges of Edge Computing 404\u003c\/p\u003e \u003cp\u003e21.2.5 How to Protect Devices “On the Edge”? 405\u003c\/p\u003e \u003cp\u003e21.2.6 Comparison with Fog Computing 405\u003c\/p\u003e \u003cp\u003eBibliography 406\u003c\/p\u003e \u003cp\u003eIndex 409\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eDeepak Gupta, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAditya Khamparia, PhD,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eWith 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. \u003ci\u003eFog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eDescribes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing\u003c\/li\u003e \u003cli\u003eConsiders probabilistic storage systems and proven optimization techniques for intelligent IoT\u003c\/li\u003e \u003cli\u003eCovers 5G edge network slicing and virtual network systems that utilize new networking capacity\u003c\/li\u003e \u003cli\u003eExplores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications\u003c\/li\u003e \u003cli\u003ePresents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearchers, 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 \u003ci\u003eFog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications\u003c\/i\u003e provides the background, orientation, and inspiration needed to begin.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989225160933,"sku":"NP9781119670070","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119670070.jpg?v=1761783276","url":"https:\/\/k12savings.com\/products\/fog-edge-and-pervasive-computing-in-intelligent-iot-driven-applications-isbn-9781119670070","provider":"K12savings","version":"1.0","type":"link"}