Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications:
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ
John Wiley & Sons, Incorporated
2021
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| Links: | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119670087 https://ieeexplore.ieee.org/servlet/opac?bknumber=9292526 https://doi.org/10.1002/9781119670087 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (xxxiii, 424 Seiten) |
| ISBN: | 9781119670087 9781119670100 9781119670094 |
| DOI: | 10.1002/9781119670087 |
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| 245 | 1 | 0 | |a Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications |c edited by Deepak Gupta, Aditya Khamparia |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use -- 1.1 Introduction -- 1.2 Why Fog, Edge, and Pervasive Computing? -- 1.3 Technologies Related to Fog and Edge Computing -- 1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era -- 1.5 The Hierarchical Architecture of Fog/Edge Computing -- 1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare -- 1.7 Issues, Challenges, and Opportunity -- 1.7.1 Security and Privacy Issues -- 1.7.2 Resource Management -- 1.7.3 Programming Platform -- 1.8 Conclusion -- Bibliography -- Chapter 2 Future Opportunistic Fog/Edge Computational Models and their Limitations -- 2.1 Introduction -- 2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? -- 2.3 Disadvantages -- 2.4 Challenges -- 2.5 Role in Health Care -- 2.6 Blockchain and Fog, Edge Computing -- 2.7 How Blockchain will Illuminate Human Services Issues -- 2.8 Uses of Blockchain in the Future -- 2.9 Uses of Blockchain in Health Care -- 2.10 Edge Computing Segmental Analysis: -- 2.11 Uses of Fog Computing -- 2.12 Analytics in Fog Computing -- 2.13 Conclusion -- Bibliography -- Chapter 3 Automating Elicitation Technique Selection using Machine Learning -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Model: Requirement Elicitation Technique Selection Model -- 3.3.1 Determining Key Attributes -- 3.3.2 Selection Attributes -- 3.3.2.1 Analyst Experience -- 3.3.2.2 Number of Stakeholders -- 3.3.2.3 Technique Time -- 3.3.2.4 Level of Information -- 3.3.3 Selection Attributes Dataset -- 3.3.3.1 Mapping the Selection Attributes | |
| 505 | 8 | |a 3.3.4 k‐nearest Neighbor Algorithm Application -- 3.4 Analysis and Results -- 3.5 The Error Rate -- 3.6 Validation -- 3.6.1 Discussion of the Results of the Experiment -- 3.7 Conclusion -- Bibliography -- Chapter 4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing -- 4.1 Introduction -- 4.1.1 Fog Computing and Edge Computing -- 4.1.2 Pervasive Computing -- 4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing -- 4.2.1 TensorFlow -- 4.2.2 Keras -- 4.2.3 PyTorch -- 4.2.4 TensorFlow Lite -- 4.2.4.1 Use Pre‐train Models -- 4.2.4.2 Convert the Model -- 4.2.4.3 On‐device Inference -- 4.2.4.4 Model Optimization -- 4.2.5 Machine Learning and Deep Learning Techniques -- 4.2.5.1 Supervised, Unsupervised and Reinforcement Learning -- 4.2.5.2 Machine Learning, Deep Learning Techniques -- 4.2.5.3 Deep Learning Techniques -- 4.2.5.4 Efficient Deep Learning Algorithms for Inference -- 4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing -- 4.2.6.1 Advantages using ML Algorithms -- 4.2.6.2 Disadvantages of using ML Algorithms -- 4.2.7 Hybrid ML Model for Smart IoT Applications -- 4.2.7.1 Multi‐Task Learning -- 4.2.7.2 Ensemble Learning -- 4.2.8 Possible Applications in Fog Era using Machine Learning -- 4.2.8.1 Computer Vision -- 4.2.8.2 ML‐ Assisted Healthcare Monitoring System -- 4.2.8.3 Smart Homes -- 4.2.8.4 Behavior Analyses -- 4.2.8.5 Monitoring in Remote Areas and Industries -- 4.2.8.6 Self‐Driving Cars -- Bibliography -- Chapter 5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications -- 5.1 Introduction -- 5.1.1 Execution Plan for the Mobile Application -- 5.1.2 Main Contribution -- 5.2 Understanding Library Management -- 5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept -- 5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation | |
| 505 | 8 | |a 5.5 IoT Driven Mobile Based Library Management ‐ General Concept -- 5.6 IoT Involved Real Time GUI (Cross Platform) Available to User -- 5.7 IoT Challenges -- 5.7.1 Infrastructure Challenges -- 5.7.2 Security Challenges -- 5.7.3 Societal Challenges -- 5.7.4 Commercial Challenges -- 5.8 Conclusion -- Bibliography -- Chapter 6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Conclusion -- Bibliography -- Chapter 7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation -- 7.1 Introduction to Fog and Edge Computing -- 7.1.1 Need for Fog and Edge Computing -- 7.1.2 Fog Computing -- 7.1.2.1 Application Areas of Fog Computing -- 7.1.3 Edge Computing -- 7.1.3.1 Advantages of Edge Computing -- 7.1.3.2 Application Areas of Fog Computing -- 7.2 Introduction to Transportation System -- 7.3 Route Finding Process -- 7.3.1 Challenges Associated with Land Navigation and Routing Process -- 7.4 Edge Architecture for Route Finding -- 7.5 Technique Used -- 7.6 Algorithms Used for the Location Identification and Route Finding Process -- 7.6.1 Location Identification -- 7.6.2 Path Generation Technique -- 7.7 Results and Discussions -- 7.7.1 Output -- 7.7.2 Benefits of Edge‐based Routing -- 7.8 Conclusion -- Bibliography -- Chapter 8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Vehicle Condition Monitoring through Acoustic Emission -- 8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE -- 8.5 Designing of MEM Sensor -- 8.6 Experimental Setup: -- 8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB -- 8.6.2 Design of MEMS Sensor using COMSOL Multiphysics | |
| 505 | 8 | |a 8.6.3 Electrostatic Study Steps for the Optimized Tri‐plate Comb Structure -- 8.7 Result and Discussions -- 8.8 Conclusion -- Bibliography -- Chapter 9 IoT Driven Healthcare Monitoring System -- 9.1 Introduction -- 9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications -- 9.1.2 Main Contribution -- 9.2 General Concept for IoT Based Healthcare System -- 9.3 View of the Overall IoT Healthcare System‐ Tiers Explained -- 9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation -- 9.5 Models/Frameworks for IoT use in Healthcare -- 9.6 IoT e‐Health System Model -- 9.7 Process Flow for the Overall Model -- 9.8 Conclusion -- Bibliography -- Chapter 10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks -- 10.1 Introduction -- 10.2 Future VANET: Primary Issues and Specifications -- 10.3 Fog Computing -- 10.3.1 Fog Computing Concept -- 10.3.2 Fog Technology Characterization -- 10.4 Related Works in Cloud and Fog Computing -- 10.5 Fog and Cloud Computing‐based Technology Applications in VANET -- 10.6 Challenges of Fog Computing in VANET -- 10.7 Issues of Fog Computing in VANET -- 10.8 Conclusion -- Bibliography -- Chapter 11 An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Overview of the Chapter -- 11.4 Data Collection in the IoT -- 11.5 Fog Computing -- 11.5.1 Why fog Computing for Data Collection in IoT? -- 11.5.2 Architecture of Fog Computing -- 11.5.3 Features of Fog Computing -- 11.5.4 Threats of Fog Computing -- 11.5.5 Applications of Fog Computing with the IoT -- 11.6 Requirements for Designing a Data Collection Method -- 11.7 Conclusion -- Bibliography -- Chapter 12 Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities | |
| 505 | 8 | |a 12.1 Introduction to Fog Computing -- 12.1.1 Hierarchical Fog Computing Architecture -- 12.1.2 Layered Fog Computing Architecture -- 12.1.3 Comparison of Fog and Cloud Computing -- 12.2 Introduction to Internet of Things -- 12.2.1 Overview of Internet of Things -- 12.3 Conceptual Architecture of Internet of Things -- 12.4 Relationship between Internet of Things and Fog Computing -- 12.5 Use of Fog Analytics in Internet of Things -- 12.6 Conclusion -- Bibliography -- Chapter 13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Introduction -- 13.2.2 Fuzzy Sets -- 13.2.3 Intuitionistic Fuzzy Sets -- 13.2.4 Intuitionistic Fuzzy Relation -- 13.2.5 Max‐Min‐Max Composition -- 13.2.6 Linguistic Variable -- 13.2.7 Distance Measure In Intuitionistic Fuzzy Sets -- 13.2.7.1 The Hamming Distance: -- 13.2.7.2 Normalized Hamming Distance: -- 13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix -- 13.2.7.4 Revised Max‐Min Average Composition of A and B (A Φ B) -- 13.3 Max‐Min‐Max Algorithm for Disease Diagnosis -- 13.4 Case Study -- 13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis -- 13.6 Result -- 13.7 Code for Calculation -- 13.8 Conclusion -- 13.9 Acknowledgement -- Bibliography -- Chapter 14 Security Attacks in Internet of Things -- 14.1 Introduction -- 14.2 Reference Model of Internet of Things (IoT) -- 14.3 IoT Communication Protocol -- 14.4 IoT Security -- 14.4.1 Physical Attack -- 14.4.2 Network Attack -- 14.4.3 Software Attack -- 14.4.4 Encryption Attack -- 14.5 Security Challenges in IoT -- 14.5.1 Cryptographic Strategies -- 14.5.2 Key Administration -- 14.5.3 Denial of Service -- 14.5.4 Authentication and Access Control -- 14.6 Conclusion -- Bibliography | |
| 505 | 8 | |a Chapter 15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery | |
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| contents | Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use -- 1.1 Introduction -- 1.2 Why Fog, Edge, and Pervasive Computing? -- 1.3 Technologies Related to Fog and Edge Computing -- 1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era -- 1.5 The Hierarchical Architecture of Fog/Edge Computing -- 1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare -- 1.7 Issues, Challenges, and Opportunity -- 1.7.1 Security and Privacy Issues -- 1.7.2 Resource Management -- 1.7.3 Programming Platform -- 1.8 Conclusion -- Bibliography -- Chapter 2 Future Opportunistic Fog/Edge Computational Models and their Limitations -- 2.1 Introduction -- 2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? -- 2.3 Disadvantages -- 2.4 Challenges -- 2.5 Role in Health Care -- 2.6 Blockchain and Fog, Edge Computing -- 2.7 How Blockchain will Illuminate Human Services Issues -- 2.8 Uses of Blockchain in the Future -- 2.9 Uses of Blockchain in Health Care -- 2.10 Edge Computing Segmental Analysis: -- 2.11 Uses of Fog Computing -- 2.12 Analytics in Fog Computing -- 2.13 Conclusion -- Bibliography -- Chapter 3 Automating Elicitation Technique Selection using Machine Learning -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Model: Requirement Elicitation Technique Selection Model -- 3.3.1 Determining Key Attributes -- 3.3.2 Selection Attributes -- 3.3.2.1 Analyst Experience -- 3.3.2.2 Number of Stakeholders -- 3.3.2.3 Technique Time -- 3.3.2.4 Level of Information -- 3.3.3 Selection Attributes Dataset -- 3.3.3.1 Mapping the Selection Attributes 3.3.4 k‐nearest Neighbor Algorithm Application -- 3.4 Analysis and Results -- 3.5 The Error Rate -- 3.6 Validation -- 3.6.1 Discussion of the Results of the Experiment -- 3.7 Conclusion -- Bibliography -- Chapter 4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing -- 4.1 Introduction -- 4.1.1 Fog Computing and Edge Computing -- 4.1.2 Pervasive Computing -- 4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing -- 4.2.1 TensorFlow -- 4.2.2 Keras -- 4.2.3 PyTorch -- 4.2.4 TensorFlow Lite -- 4.2.4.1 Use Pre‐train Models -- 4.2.4.2 Convert the Model -- 4.2.4.3 On‐device Inference -- 4.2.4.4 Model Optimization -- 4.2.5 Machine Learning and Deep Learning Techniques -- 4.2.5.1 Supervised, Unsupervised and Reinforcement Learning -- 4.2.5.2 Machine Learning, Deep Learning Techniques -- 4.2.5.3 Deep Learning Techniques -- 4.2.5.4 Efficient Deep Learning Algorithms for Inference -- 4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing -- 4.2.6.1 Advantages using ML Algorithms -- 4.2.6.2 Disadvantages of using ML Algorithms -- 4.2.7 Hybrid ML Model for Smart IoT Applications -- 4.2.7.1 Multi‐Task Learning -- 4.2.7.2 Ensemble Learning -- 4.2.8 Possible Applications in Fog Era using Machine Learning -- 4.2.8.1 Computer Vision -- 4.2.8.2 ML‐ Assisted Healthcare Monitoring System -- 4.2.8.3 Smart Homes -- 4.2.8.4 Behavior Analyses -- 4.2.8.5 Monitoring in Remote Areas and Industries -- 4.2.8.6 Self‐Driving Cars -- Bibliography -- Chapter 5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications -- 5.1 Introduction -- 5.1.1 Execution Plan for the Mobile Application -- 5.1.2 Main Contribution -- 5.2 Understanding Library Management -- 5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept -- 5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation 5.5 IoT Driven Mobile Based Library Management ‐ General Concept -- 5.6 IoT Involved Real Time GUI (Cross Platform) Available to User -- 5.7 IoT Challenges -- 5.7.1 Infrastructure Challenges -- 5.7.2 Security Challenges -- 5.7.3 Societal Challenges -- 5.7.4 Commercial Challenges -- 5.8 Conclusion -- Bibliography -- Chapter 6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Conclusion -- Bibliography -- Chapter 7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation -- 7.1 Introduction to Fog and Edge Computing -- 7.1.1 Need for Fog and Edge Computing -- 7.1.2 Fog Computing -- 7.1.2.1 Application Areas of Fog Computing -- 7.1.3 Edge Computing -- 7.1.3.1 Advantages of Edge Computing -- 7.1.3.2 Application Areas of Fog Computing -- 7.2 Introduction to Transportation System -- 7.3 Route Finding Process -- 7.3.1 Challenges Associated with Land Navigation and Routing Process -- 7.4 Edge Architecture for Route Finding -- 7.5 Technique Used -- 7.6 Algorithms Used for the Location Identification and Route Finding Process -- 7.6.1 Location Identification -- 7.6.2 Path Generation Technique -- 7.7 Results and Discussions -- 7.7.1 Output -- 7.7.2 Benefits of Edge‐based Routing -- 7.8 Conclusion -- Bibliography -- Chapter 8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Vehicle Condition Monitoring through Acoustic Emission -- 8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE -- 8.5 Designing of MEM Sensor -- 8.6 Experimental Setup: -- 8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB -- 8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 8.6.3 Electrostatic Study Steps for the Optimized Tri‐plate Comb Structure -- 8.7 Result and Discussions -- 8.8 Conclusion -- Bibliography -- Chapter 9 IoT Driven Healthcare Monitoring System -- 9.1 Introduction -- 9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications -- 9.1.2 Main Contribution -- 9.2 General Concept for IoT Based Healthcare System -- 9.3 View of the Overall IoT Healthcare System‐ Tiers Explained -- 9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation -- 9.5 Models/Frameworks for IoT use in Healthcare -- 9.6 IoT e‐Health System Model -- 9.7 Process Flow for the Overall Model -- 9.8 Conclusion -- Bibliography -- Chapter 10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks -- 10.1 Introduction -- 10.2 Future VANET: Primary Issues and Specifications -- 10.3 Fog Computing -- 10.3.1 Fog Computing Concept -- 10.3.2 Fog Technology Characterization -- 10.4 Related Works in Cloud and Fog Computing -- 10.5 Fog and Cloud Computing‐based Technology Applications in VANET -- 10.6 Challenges of Fog Computing in VANET -- 10.7 Issues of Fog Computing in VANET -- 10.8 Conclusion -- Bibliography -- Chapter 11 An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Overview of the Chapter -- 11.4 Data Collection in the IoT -- 11.5 Fog Computing -- 11.5.1 Why fog Computing for Data Collection in IoT? -- 11.5.2 Architecture of Fog Computing -- 11.5.3 Features of Fog Computing -- 11.5.4 Threats of Fog Computing -- 11.5.5 Applications of Fog Computing with the IoT -- 11.6 Requirements for Designing a Data Collection Method -- 11.7 Conclusion -- Bibliography -- Chapter 12 Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities 12.1 Introduction to Fog Computing -- 12.1.1 Hierarchical Fog Computing Architecture -- 12.1.2 Layered Fog Computing Architecture -- 12.1.3 Comparison of Fog and Cloud Computing -- 12.2 Introduction to Internet of Things -- 12.2.1 Overview of Internet of Things -- 12.3 Conceptual Architecture of Internet of Things -- 12.4 Relationship between Internet of Things and Fog Computing -- 12.5 Use of Fog Analytics in Internet of Things -- 12.6 Conclusion -- Bibliography -- Chapter 13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Introduction -- 13.2.2 Fuzzy Sets -- 13.2.3 Intuitionistic Fuzzy Sets -- 13.2.4 Intuitionistic Fuzzy Relation -- 13.2.5 Max‐Min‐Max Composition -- 13.2.6 Linguistic Variable -- 13.2.7 Distance Measure In Intuitionistic Fuzzy Sets -- 13.2.7.1 The Hamming Distance: -- 13.2.7.2 Normalized Hamming Distance: -- 13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix -- 13.2.7.4 Revised Max‐Min Average Composition of A and B (A Φ B) -- 13.3 Max‐Min‐Max Algorithm for Disease Diagnosis -- 13.4 Case Study -- 13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis -- 13.6 Result -- 13.7 Code for Calculation -- 13.8 Conclusion -- 13.9 Acknowledgement -- Bibliography -- Chapter 14 Security Attacks in Internet of Things -- 14.1 Introduction -- 14.2 Reference Model of Internet of Things (IoT) -- 14.3 IoT Communication Protocol -- 14.4 IoT Security -- 14.4.1 Physical Attack -- 14.4.2 Network Attack -- 14.4.3 Software Attack -- 14.4.4 Encryption Attack -- 14.5 Security Challenges in IoT -- 14.5.1 Cryptographic Strategies -- 14.5.2 Key Administration -- 14.5.3 Denial of Service -- 14.5.4 Authentication and Access Control -- 14.6 Conclusion -- Bibliography Chapter 15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery |
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| dewey-full | 004.678 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.678 |
| dewey-search | 004.678 |
| dewey-sort | 14.678 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1002/9781119670087 |
| format | Electronic eBook |
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code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use -- 1.1 Introduction -- 1.2 Why Fog, Edge, and Pervasive Computing? -- 1.3 Technologies Related to Fog and Edge Computing -- 1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era -- 1.5 The Hierarchical Architecture of Fog/Edge Computing -- 1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare -- 1.7 Issues, Challenges, and Opportunity -- 1.7.1 Security and Privacy Issues -- 1.7.2 Resource Management -- 1.7.3 Programming Platform -- 1.8 Conclusion -- Bibliography -- Chapter 2 Future Opportunistic Fog/Edge Computational Models and their Limitations -- 2.1 Introduction -- 2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? -- 2.3 Disadvantages -- 2.4 Challenges -- 2.5 Role in Health Care -- 2.6 Blockchain and Fog, Edge Computing -- 2.7 How Blockchain will Illuminate Human Services Issues -- 2.8 Uses of Blockchain in the Future -- 2.9 Uses of Blockchain in Health Care -- 2.10 Edge Computing Segmental Analysis: -- 2.11 Uses of Fog Computing -- 2.12 Analytics in Fog Computing -- 2.13 Conclusion -- Bibliography -- Chapter 3 Automating Elicitation Technique Selection using Machine Learning -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Model: Requirement Elicitation Technique Selection Model -- 3.3.1 Determining Key Attributes -- 3.3.2 Selection Attributes -- 3.3.2.1 Analyst Experience -- 3.3.2.2 Number of Stakeholders -- 3.3.2.3 Technique Time -- 3.3.2.4 Level of Information -- 3.3.3 Selection Attributes Dataset -- 3.3.3.1 Mapping the Selection Attributes</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3.4 k‐nearest Neighbor Algorithm Application -- 3.4 Analysis and Results -- 3.5 The Error Rate -- 3.6 Validation -- 3.6.1 Discussion of the Results of the Experiment -- 3.7 Conclusion -- Bibliography -- Chapter 4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing -- 4.1 Introduction -- 4.1.1 Fog Computing and Edge Computing -- 4.1.2 Pervasive Computing -- 4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing -- 4.2.1 TensorFlow -- 4.2.2 Keras -- 4.2.3 PyTorch -- 4.2.4 TensorFlow Lite -- 4.2.4.1 Use Pre‐train Models -- 4.2.4.2 Convert the Model -- 4.2.4.3 On‐device Inference -- 4.2.4.4 Model Optimization -- 4.2.5 Machine Learning and Deep Learning Techniques -- 4.2.5.1 Supervised, Unsupervised and Reinforcement Learning -- 4.2.5.2 Machine Learning, Deep Learning Techniques -- 4.2.5.3 Deep Learning Techniques -- 4.2.5.4 Efficient Deep Learning Algorithms for Inference -- 4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing -- 4.2.6.1 Advantages using ML Algorithms -- 4.2.6.2 Disadvantages of using ML Algorithms -- 4.2.7 Hybrid ML Model for Smart IoT Applications -- 4.2.7.1 Multi‐Task Learning -- 4.2.7.2 Ensemble Learning -- 4.2.8 Possible Applications in Fog Era using Machine Learning -- 4.2.8.1 Computer Vision -- 4.2.8.2 ML‐ Assisted Healthcare Monitoring System -- 4.2.8.3 Smart Homes -- 4.2.8.4 Behavior Analyses -- 4.2.8.5 Monitoring in Remote Areas and Industries -- 4.2.8.6 Self‐Driving Cars -- Bibliography -- Chapter 5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications -- 5.1 Introduction -- 5.1.1 Execution Plan for the Mobile Application -- 5.1.2 Main Contribution -- 5.2 Understanding Library Management -- 5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept -- 5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.5 IoT Driven Mobile Based Library Management ‐ General Concept -- 5.6 IoT Involved Real Time GUI (Cross Platform) Available to User -- 5.7 IoT Challenges -- 5.7.1 Infrastructure Challenges -- 5.7.2 Security Challenges -- 5.7.3 Societal Challenges -- 5.7.4 Commercial Challenges -- 5.8 Conclusion -- Bibliography -- Chapter 6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Conclusion -- Bibliography -- Chapter 7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation -- 7.1 Introduction to Fog and Edge Computing -- 7.1.1 Need for Fog and Edge Computing -- 7.1.2 Fog Computing -- 7.1.2.1 Application Areas of Fog Computing -- 7.1.3 Edge Computing -- 7.1.3.1 Advantages of Edge Computing -- 7.1.3.2 Application Areas of Fog Computing -- 7.2 Introduction to Transportation System -- 7.3 Route Finding Process -- 7.3.1 Challenges Associated with Land Navigation and Routing Process -- 7.4 Edge Architecture for Route Finding -- 7.5 Technique Used -- 7.6 Algorithms Used for the Location Identification and Route Finding Process -- 7.6.1 Location Identification -- 7.6.2 Path Generation Technique -- 7.7 Results and Discussions -- 7.7.1 Output -- 7.7.2 Benefits of Edge‐based Routing -- 7.8 Conclusion -- Bibliography -- Chapter 8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Vehicle Condition Monitoring through Acoustic Emission -- 8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE -- 8.5 Designing of MEM Sensor -- 8.6 Experimental Setup: -- 8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB -- 8.6.2 Design of MEMS Sensor using COMSOL Multiphysics</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.6.3 Electrostatic Study Steps for the Optimized Tri‐plate Comb Structure -- 8.7 Result and Discussions -- 8.8 Conclusion -- Bibliography -- Chapter 9 IoT Driven Healthcare Monitoring System -- 9.1 Introduction -- 9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications -- 9.1.2 Main Contribution -- 9.2 General Concept for IoT Based Healthcare System -- 9.3 View of the Overall IoT Healthcare System‐ Tiers Explained -- 9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation -- 9.5 Models/Frameworks for IoT use in Healthcare -- 9.6 IoT e‐Health System Model -- 9.7 Process Flow for the Overall Model -- 9.8 Conclusion -- Bibliography -- Chapter 10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks -- 10.1 Introduction -- 10.2 Future VANET: Primary Issues and Specifications -- 10.3 Fog Computing -- 10.3.1 Fog Computing Concept -- 10.3.2 Fog Technology Characterization -- 10.4 Related Works in Cloud and Fog Computing -- 10.5 Fog and Cloud Computing‐based Technology Applications in VANET -- 10.6 Challenges of Fog Computing in VANET -- 10.7 Issues of Fog Computing in VANET -- 10.8 Conclusion -- Bibliography -- Chapter 11 An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Overview of the Chapter -- 11.4 Data Collection in the IoT -- 11.5 Fog Computing -- 11.5.1 Why fog Computing for Data Collection in IoT? -- 11.5.2 Architecture of Fog Computing -- 11.5.3 Features of Fog Computing -- 11.5.4 Threats of Fog Computing -- 11.5.5 Applications of Fog Computing with the IoT -- 11.6 Requirements for Designing a Data Collection Method -- 11.7 Conclusion -- Bibliography -- Chapter 12 Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">12.1 Introduction to Fog Computing -- 12.1.1 Hierarchical Fog Computing Architecture -- 12.1.2 Layered Fog Computing Architecture -- 12.1.3 Comparison of Fog and Cloud Computing -- 12.2 Introduction to Internet of Things -- 12.2.1 Overview of Internet of Things -- 12.3 Conceptual Architecture of Internet of Things -- 12.4 Relationship between Internet of Things and Fog Computing -- 12.5 Use of Fog Analytics in Internet of Things -- 12.6 Conclusion -- Bibliography -- Chapter 13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Introduction -- 13.2.2 Fuzzy Sets -- 13.2.3 Intuitionistic Fuzzy Sets -- 13.2.4 Intuitionistic Fuzzy Relation -- 13.2.5 Max‐Min‐Max Composition -- 13.2.6 Linguistic Variable -- 13.2.7 Distance Measure In Intuitionistic Fuzzy Sets -- 13.2.7.1 The Hamming Distance: -- 13.2.7.2 Normalized Hamming Distance: -- 13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix -- 13.2.7.4 Revised Max‐Min Average Composition of A and B (A Φ B) -- 13.3 Max‐Min‐Max Algorithm for Disease Diagnosis -- 13.4 Case Study -- 13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis -- 13.6 Result -- 13.7 Code for Calculation -- 13.8 Conclusion -- 13.9 Acknowledgement -- Bibliography -- Chapter 14 Security Attacks in Internet of Things -- 14.1 Introduction -- 14.2 Reference Model of Internet of Things (IoT) -- 14.3 IoT Communication Protocol -- 14.4 IoT Security -- 14.4.1 Physical Attack -- 14.4.2 Network Attack -- 14.4.3 Software Attack -- 14.4.4 Encryption Attack -- 14.5 Security Challenges in IoT -- 14.5.1 Cryptographic Strategies -- 14.5.2 Key Administration -- 14.5.3 Denial of Service -- 14.5.4 Authentication and Access Control -- 14.6 Conclusion -- 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| id | DE-604.BV047442484 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-31T19:02:24Z |
| institution | BVB |
| isbn | 9781119670087 9781119670100 9781119670094 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844636 |
| oclc_num | 1226586133 |
| open_access_boolean | |
| owner | DE-Aug4 DE-573 |
| owner_facet | DE-Aug4 DE-573 |
| physical | 1 Online-Ressource (xxxiii, 424 Seiten) |
| psigel | ZDB-30-PQE ZDB-35-WEL ZDB-35-WIC FHA_PDA_WIC_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Gupta, Deepak Verfasser (DE-588)1204268657 aut Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications edited by Deepak Gupta, Aditya Khamparia Hoboken, NJ John Wiley & Sons, Incorporated 2021 ©2021 1 Online-Ressource (xxxiii, 424 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use -- 1.1 Introduction -- 1.2 Why Fog, Edge, and Pervasive Computing? -- 1.3 Technologies Related to Fog and Edge Computing -- 1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era -- 1.5 The Hierarchical Architecture of Fog/Edge Computing -- 1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare -- 1.7 Issues, Challenges, and Opportunity -- 1.7.1 Security and Privacy Issues -- 1.7.2 Resource Management -- 1.7.3 Programming Platform -- 1.8 Conclusion -- Bibliography -- Chapter 2 Future Opportunistic Fog/Edge Computational Models and their Limitations -- 2.1 Introduction -- 2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? -- 2.3 Disadvantages -- 2.4 Challenges -- 2.5 Role in Health Care -- 2.6 Blockchain and Fog, Edge Computing -- 2.7 How Blockchain will Illuminate Human Services Issues -- 2.8 Uses of Blockchain in the Future -- 2.9 Uses of Blockchain in Health Care -- 2.10 Edge Computing Segmental Analysis: -- 2.11 Uses of Fog Computing -- 2.12 Analytics in Fog Computing -- 2.13 Conclusion -- Bibliography -- Chapter 3 Automating Elicitation Technique Selection using Machine Learning -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Model: Requirement Elicitation Technique Selection Model -- 3.3.1 Determining Key Attributes -- 3.3.2 Selection Attributes -- 3.3.2.1 Analyst Experience -- 3.3.2.2 Number of Stakeholders -- 3.3.2.3 Technique Time -- 3.3.2.4 Level of Information -- 3.3.3 Selection Attributes Dataset -- 3.3.3.1 Mapping the Selection Attributes 3.3.4 k‐nearest Neighbor Algorithm Application -- 3.4 Analysis and Results -- 3.5 The Error Rate -- 3.6 Validation -- 3.6.1 Discussion of the Results of the Experiment -- 3.7 Conclusion -- Bibliography -- Chapter 4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing -- 4.1 Introduction -- 4.1.1 Fog Computing and Edge Computing -- 4.1.2 Pervasive Computing -- 4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing -- 4.2.1 TensorFlow -- 4.2.2 Keras -- 4.2.3 PyTorch -- 4.2.4 TensorFlow Lite -- 4.2.4.1 Use Pre‐train Models -- 4.2.4.2 Convert the Model -- 4.2.4.3 On‐device Inference -- 4.2.4.4 Model Optimization -- 4.2.5 Machine Learning and Deep Learning Techniques -- 4.2.5.1 Supervised, Unsupervised and Reinforcement Learning -- 4.2.5.2 Machine Learning, Deep Learning Techniques -- 4.2.5.3 Deep Learning Techniques -- 4.2.5.4 Efficient Deep Learning Algorithms for Inference -- 4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing -- 4.2.6.1 Advantages using ML Algorithms -- 4.2.6.2 Disadvantages of using ML Algorithms -- 4.2.7 Hybrid ML Model for Smart IoT Applications -- 4.2.7.1 Multi‐Task Learning -- 4.2.7.2 Ensemble Learning -- 4.2.8 Possible Applications in Fog Era using Machine Learning -- 4.2.8.1 Computer Vision -- 4.2.8.2 ML‐ Assisted Healthcare Monitoring System -- 4.2.8.3 Smart Homes -- 4.2.8.4 Behavior Analyses -- 4.2.8.5 Monitoring in Remote Areas and Industries -- 4.2.8.6 Self‐Driving Cars -- Bibliography -- Chapter 5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications -- 5.1 Introduction -- 5.1.1 Execution Plan for the Mobile Application -- 5.1.2 Main Contribution -- 5.2 Understanding Library Management -- 5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept -- 5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation 5.5 IoT Driven Mobile Based Library Management ‐ General Concept -- 5.6 IoT Involved Real Time GUI (Cross Platform) Available to User -- 5.7 IoT Challenges -- 5.7.1 Infrastructure Challenges -- 5.7.2 Security Challenges -- 5.7.3 Societal Challenges -- 5.7.4 Commercial Challenges -- 5.8 Conclusion -- Bibliography -- Chapter 6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Conclusion -- Bibliography -- Chapter 7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation -- 7.1 Introduction to Fog and Edge Computing -- 7.1.1 Need for Fog and Edge Computing -- 7.1.2 Fog Computing -- 7.1.2.1 Application Areas of Fog Computing -- 7.1.3 Edge Computing -- 7.1.3.1 Advantages of Edge Computing -- 7.1.3.2 Application Areas of Fog Computing -- 7.2 Introduction to Transportation System -- 7.3 Route Finding Process -- 7.3.1 Challenges Associated with Land Navigation and Routing Process -- 7.4 Edge Architecture for Route Finding -- 7.5 Technique Used -- 7.6 Algorithms Used for the Location Identification and Route Finding Process -- 7.6.1 Location Identification -- 7.6.2 Path Generation Technique -- 7.7 Results and Discussions -- 7.7.1 Output -- 7.7.2 Benefits of Edge‐based Routing -- 7.8 Conclusion -- Bibliography -- Chapter 8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Vehicle Condition Monitoring through Acoustic Emission -- 8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE -- 8.5 Designing of MEM Sensor -- 8.6 Experimental Setup: -- 8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB -- 8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 8.6.3 Electrostatic Study Steps for the Optimized Tri‐plate Comb Structure -- 8.7 Result and Discussions -- 8.8 Conclusion -- Bibliography -- Chapter 9 IoT Driven Healthcare Monitoring System -- 9.1 Introduction -- 9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications -- 9.1.2 Main Contribution -- 9.2 General Concept for IoT Based Healthcare System -- 9.3 View of the Overall IoT Healthcare System‐ Tiers Explained -- 9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation -- 9.5 Models/Frameworks for IoT use in Healthcare -- 9.6 IoT e‐Health System Model -- 9.7 Process Flow for the Overall Model -- 9.8 Conclusion -- Bibliography -- Chapter 10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks -- 10.1 Introduction -- 10.2 Future VANET: Primary Issues and Specifications -- 10.3 Fog Computing -- 10.3.1 Fog Computing Concept -- 10.3.2 Fog Technology Characterization -- 10.4 Related Works in Cloud and Fog Computing -- 10.5 Fog and Cloud Computing‐based Technology Applications in VANET -- 10.6 Challenges of Fog Computing in VANET -- 10.7 Issues of Fog Computing in VANET -- 10.8 Conclusion -- Bibliography -- Chapter 11 An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Overview of the Chapter -- 11.4 Data Collection in the IoT -- 11.5 Fog Computing -- 11.5.1 Why fog Computing for Data Collection in IoT? -- 11.5.2 Architecture of Fog Computing -- 11.5.3 Features of Fog Computing -- 11.5.4 Threats of Fog Computing -- 11.5.5 Applications of Fog Computing with the IoT -- 11.6 Requirements for Designing a Data Collection Method -- 11.7 Conclusion -- Bibliography -- Chapter 12 Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities 12.1 Introduction to Fog Computing -- 12.1.1 Hierarchical Fog Computing Architecture -- 12.1.2 Layered Fog Computing Architecture -- 12.1.3 Comparison of Fog and Cloud Computing -- 12.2 Introduction to Internet of Things -- 12.2.1 Overview of Internet of Things -- 12.3 Conceptual Architecture of Internet of Things -- 12.4 Relationship between Internet of Things and Fog Computing -- 12.5 Use of Fog Analytics in Internet of Things -- 12.6 Conclusion -- Bibliography -- Chapter 13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Introduction -- 13.2.2 Fuzzy Sets -- 13.2.3 Intuitionistic Fuzzy Sets -- 13.2.4 Intuitionistic Fuzzy Relation -- 13.2.5 Max‐Min‐Max Composition -- 13.2.6 Linguistic Variable -- 13.2.7 Distance Measure In Intuitionistic Fuzzy Sets -- 13.2.7.1 The Hamming Distance: -- 13.2.7.2 Normalized Hamming Distance: -- 13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix -- 13.2.7.4 Revised Max‐Min Average Composition of A and B (A Φ B) -- 13.3 Max‐Min‐Max Algorithm for Disease Diagnosis -- 13.4 Case Study -- 13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis -- 13.6 Result -- 13.7 Code for Calculation -- 13.8 Conclusion -- 13.9 Acknowledgement -- Bibliography -- Chapter 14 Security Attacks in Internet of Things -- 14.1 Introduction -- 14.2 Reference Model of Internet of Things (IoT) -- 14.3 IoT Communication Protocol -- 14.4 IoT Security -- 14.4.1 Physical Attack -- 14.4.2 Network Attack -- 14.4.3 Software Attack -- 14.4.4 Encryption Attack -- 14.5 Security Challenges in IoT -- 14.5.1 Cryptographic Strategies -- 14.5.2 Key Administration -- 14.5.3 Denial of Service -- 14.5.4 Authentication and Access Control -- 14.6 Conclusion -- Bibliography Chapter 15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery Khamparia, Aditya 1988- Sonstige (DE-588)1186305169 oth Erscheint auch als Druck-Ausgabe Gupta, Deepak Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications Newark : John Wiley & Sons, Incorporated,c2021 9781119670070 https://doi.org/10.1002/9781119670087 Verlag Volltext |
| spellingShingle | Gupta, Deepak Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use -- 1.1 Introduction -- 1.2 Why Fog, Edge, and Pervasive Computing? -- 1.3 Technologies Related to Fog and Edge Computing -- 1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era -- 1.5 The Hierarchical Architecture of Fog/Edge Computing -- 1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare -- 1.7 Issues, Challenges, and Opportunity -- 1.7.1 Security and Privacy Issues -- 1.7.2 Resource Management -- 1.7.3 Programming Platform -- 1.8 Conclusion -- Bibliography -- Chapter 2 Future Opportunistic Fog/Edge Computational Models and their Limitations -- 2.1 Introduction -- 2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? -- 2.3 Disadvantages -- 2.4 Challenges -- 2.5 Role in Health Care -- 2.6 Blockchain and Fog, Edge Computing -- 2.7 How Blockchain will Illuminate Human Services Issues -- 2.8 Uses of Blockchain in the Future -- 2.9 Uses of Blockchain in Health Care -- 2.10 Edge Computing Segmental Analysis: -- 2.11 Uses of Fog Computing -- 2.12 Analytics in Fog Computing -- 2.13 Conclusion -- Bibliography -- Chapter 3 Automating Elicitation Technique Selection using Machine Learning -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Model: Requirement Elicitation Technique Selection Model -- 3.3.1 Determining Key Attributes -- 3.3.2 Selection Attributes -- 3.3.2.1 Analyst Experience -- 3.3.2.2 Number of Stakeholders -- 3.3.2.3 Technique Time -- 3.3.2.4 Level of Information -- 3.3.3 Selection Attributes Dataset -- 3.3.3.1 Mapping the Selection Attributes 3.3.4 k‐nearest Neighbor Algorithm Application -- 3.4 Analysis and Results -- 3.5 The Error Rate -- 3.6 Validation -- 3.6.1 Discussion of the Results of the Experiment -- 3.7 Conclusion -- Bibliography -- Chapter 4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing -- 4.1 Introduction -- 4.1.1 Fog Computing and Edge Computing -- 4.1.2 Pervasive Computing -- 4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing -- 4.2.1 TensorFlow -- 4.2.2 Keras -- 4.2.3 PyTorch -- 4.2.4 TensorFlow Lite -- 4.2.4.1 Use Pre‐train Models -- 4.2.4.2 Convert the Model -- 4.2.4.3 On‐device Inference -- 4.2.4.4 Model Optimization -- 4.2.5 Machine Learning and Deep Learning Techniques -- 4.2.5.1 Supervised, Unsupervised and Reinforcement Learning -- 4.2.5.2 Machine Learning, Deep Learning Techniques -- 4.2.5.3 Deep Learning Techniques -- 4.2.5.4 Efficient Deep Learning Algorithms for Inference -- 4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing -- 4.2.6.1 Advantages using ML Algorithms -- 4.2.6.2 Disadvantages of using ML Algorithms -- 4.2.7 Hybrid ML Model for Smart IoT Applications -- 4.2.7.1 Multi‐Task Learning -- 4.2.7.2 Ensemble Learning -- 4.2.8 Possible Applications in Fog Era using Machine Learning -- 4.2.8.1 Computer Vision -- 4.2.8.2 ML‐ Assisted Healthcare Monitoring System -- 4.2.8.3 Smart Homes -- 4.2.8.4 Behavior Analyses -- 4.2.8.5 Monitoring in Remote Areas and Industries -- 4.2.8.6 Self‐Driving Cars -- Bibliography -- Chapter 5 Integrated Cloud Based Library Management in Intelligent IoT driven Applications -- 5.1 Introduction -- 5.1.1 Execution Plan for the Mobile Application -- 5.1.2 Main Contribution -- 5.2 Understanding Library Management -- 5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept -- 5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation 5.5 IoT Driven Mobile Based Library Management ‐ General Concept -- 5.6 IoT Involved Real Time GUI (Cross Platform) Available to User -- 5.7 IoT Challenges -- 5.7.1 Infrastructure Challenges -- 5.7.2 Security Challenges -- 5.7.3 Societal Challenges -- 5.7.4 Commercial Challenges -- 5.8 Conclusion -- Bibliography -- Chapter 6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Conclusion -- Bibliography -- Chapter 7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation -- 7.1 Introduction to Fog and Edge Computing -- 7.1.1 Need for Fog and Edge Computing -- 7.1.2 Fog Computing -- 7.1.2.1 Application Areas of Fog Computing -- 7.1.3 Edge Computing -- 7.1.3.1 Advantages of Edge Computing -- 7.1.3.2 Application Areas of Fog Computing -- 7.2 Introduction to Transportation System -- 7.3 Route Finding Process -- 7.3.1 Challenges Associated with Land Navigation and Routing Process -- 7.4 Edge Architecture for Route Finding -- 7.5 Technique Used -- 7.6 Algorithms Used for the Location Identification and Route Finding Process -- 7.6.1 Location Identification -- 7.6.2 Path Generation Technique -- 7.7 Results and Discussions -- 7.7.1 Output -- 7.7.2 Benefits of Edge‐based Routing -- 7.8 Conclusion -- Bibliography -- Chapter 8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Vehicle Condition Monitoring through Acoustic Emission -- 8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE -- 8.5 Designing of MEM Sensor -- 8.6 Experimental Setup: -- 8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB -- 8.6.2 Design of MEMS Sensor using COMSOL Multiphysics 8.6.3 Electrostatic Study Steps for the Optimized Tri‐plate Comb Structure -- 8.7 Result and Discussions -- 8.8 Conclusion -- Bibliography -- Chapter 9 IoT Driven Healthcare Monitoring System -- 9.1 Introduction -- 9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications -- 9.1.2 Main Contribution -- 9.2 General Concept for IoT Based Healthcare System -- 9.3 View of the Overall IoT Healthcare System‐ Tiers Explained -- 9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation -- 9.5 Models/Frameworks for IoT use in Healthcare -- 9.6 IoT e‐Health System Model -- 9.7 Process Flow for the Overall Model -- 9.8 Conclusion -- Bibliography -- Chapter 10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks -- 10.1 Introduction -- 10.2 Future VANET: Primary Issues and Specifications -- 10.3 Fog Computing -- 10.3.1 Fog Computing Concept -- 10.3.2 Fog Technology Characterization -- 10.4 Related Works in Cloud and Fog Computing -- 10.5 Fog and Cloud Computing‐based Technology Applications in VANET -- 10.6 Challenges of Fog Computing in VANET -- 10.7 Issues of Fog Computing in VANET -- 10.8 Conclusion -- Bibliography -- Chapter 11 An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Overview of the Chapter -- 11.4 Data Collection in the IoT -- 11.5 Fog Computing -- 11.5.1 Why fog Computing for Data Collection in IoT? -- 11.5.2 Architecture of Fog Computing -- 11.5.3 Features of Fog Computing -- 11.5.4 Threats of Fog Computing -- 11.5.5 Applications of Fog Computing with the IoT -- 11.6 Requirements for Designing a Data Collection Method -- 11.7 Conclusion -- Bibliography -- Chapter 12 Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities 12.1 Introduction to Fog Computing -- 12.1.1 Hierarchical Fog Computing Architecture -- 12.1.2 Layered Fog Computing Architecture -- 12.1.3 Comparison of Fog and Cloud Computing -- 12.2 Introduction to Internet of Things -- 12.2.1 Overview of Internet of Things -- 12.3 Conceptual Architecture of Internet of Things -- 12.4 Relationship between Internet of Things and Fog Computing -- 12.5 Use of Fog Analytics in Internet of Things -- 12.6 Conclusion -- Bibliography -- Chapter 13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Introduction -- 13.2.2 Fuzzy Sets -- 13.2.3 Intuitionistic Fuzzy Sets -- 13.2.4 Intuitionistic Fuzzy Relation -- 13.2.5 Max‐Min‐Max Composition -- 13.2.6 Linguistic Variable -- 13.2.7 Distance Measure In Intuitionistic Fuzzy Sets -- 13.2.7.1 The Hamming Distance: -- 13.2.7.2 Normalized Hamming Distance: -- 13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix -- 13.2.7.4 Revised Max‐Min Average Composition of A and B (A Φ B) -- 13.3 Max‐Min‐Max Algorithm for Disease Diagnosis -- 13.4 Case Study -- 13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis -- 13.6 Result -- 13.7 Code for Calculation -- 13.8 Conclusion -- 13.9 Acknowledgement -- Bibliography -- Chapter 14 Security Attacks in Internet of Things -- 14.1 Introduction -- 14.2 Reference Model of Internet of Things (IoT) -- 14.3 IoT Communication Protocol -- 14.4 IoT Security -- 14.4.1 Physical Attack -- 14.4.2 Network Attack -- 14.4.3 Software Attack -- 14.4.4 Encryption Attack -- 14.5 Security Challenges in IoT -- 14.5.1 Cryptographic Strategies -- 14.5.2 Key Administration -- 14.5.3 Denial of Service -- 14.5.4 Authentication and Access Control -- 14.6 Conclusion -- Bibliography Chapter 15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery |
| title | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications |
| title_auth | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications |
| title_exact_search | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications |
| title_full | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications edited by Deepak Gupta, Aditya Khamparia |
| title_fullStr | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications edited by Deepak Gupta, Aditya Khamparia |
| title_full_unstemmed | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications edited by Deepak Gupta, Aditya Khamparia |
| title_short | Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications |
| title_sort | fog edge and pervasive computing in intelligent iot driven applications |
| url | https://doi.org/10.1002/9781119670087 |
| work_keys_str_mv | AT guptadeepak fogedgeandpervasivecomputinginintelligentiotdrivenapplications AT khampariaaditya fogedgeandpervasivecomputinginintelligentiotdrivenapplications |