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

Inbunden, Engelska, 2021

1 297 kr

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Beskrivning

A practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applicationsWith 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 moreResearchers, 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.

Produktinformation

Utgivningsdatum:2021-03-12
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:464
Förlag:John Wiley & Sons Inc
ISBN:9781119670070

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Mer om författaren

Deepak Gupta, PhD, is an Assistant Professor in the Department of Computer Science and Engineering at the Maharaja Agrasen Institute of Technology, Delhi, India. He has published 158 papers and 3 patents. He is associated with numerous professional bodies, including IEEE, ISTE, IAENG, and IACSIT. He is the convener and organizer of the ICICC, ICDAM Springer Conference Series. Aditya Khamparia, PhD, is Associate Professor of Computer Science at Lovely Professional University, Punjab, India. He has published more than 45 scientific research publications and is a member of CSI, IET, ISTE, IAENG, ACM and IACSIT.

Innehållsförteckning

About the Editors xviiList of Contributors xixPreface xxvAcknowledgments xxxiii1 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 Raza1.1 Introduction 11.2 Why Fog, Edge, and Pervasive Computing? 31.3 Technologies Related to Fog and Edge Computing 61.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era 91.5 The Hierarchical Architecture of Fog/Edge Computing 121.6 Applications of Fog, Edge and Pervasive Computing in IoT-based Healthcare 151.7 Issues, Challenges, and Opportunity 171.7.1 Security and Privacy Issues 181.7.2 Resource Management 191.7.3 Programming Platform 191.8 Conclusion 20Bibliography 202 Future Opportunistic Fog/Edge Computational Models and their Limitations 27Sonia Singla, Naveen Kumar Bhati, and S. Aswath2.1 Introduction 282.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)? 322.3 Disadvantages 342.4 Challenges 342.5 Role in Health Care 352.6 Blockchain and Fog, Edge Computing 382.7 How Blockchain will Illuminate Human Services Issues 402.8 Uses of Blockchain in the Future 412.9 Uses of Blockchain in Health Care 422.10 Edge Computing Segmental Analysis 422.11 Uses of Fog Computing 432.12 Analytics in Fog Computing 442.13 Conclusion 44Bibliography 443 Automating Elicitation Technique Selection using Machine Learning 47Hatim M. Elhassan Ibrahim Dafallaa, Nazir Ahmad, Mohammed Burhanur Rehman, Iqrar Ahmad, and Rizwan khan3.1 Introduction 473.2 Related Work 483.3 Model: Requirement Elicitation Technique Selection Model 523.3.1 Determining Key Attributes 543.3.2 Selection Attributes 543.3.2.1 Analyst Experience 553.3.2.2 Number of Stakeholders 553.3.2.3 Technique Time 563.3.2.4 Level of Information 563.3.3 Selection Attributes Dataset 563.3.3.1 Mapping the Selection Attributes 573.3.4 k-nearest Neighbor Algorithm Application 573.4 Analysis and Results 603.5 The Error Rate 613.6 Validation 613.6.1 Discussion of the Results of the Experiment 623.7 Conclusion 62Bibliography 654 Machine Learning Frameworks and Algorithms for Fog and Edge Computing 67Murali Mallikarjuna Rao Perumalla, Sanjay Kumar Singh, Aditya Khamparia, Anjali Goyal, and Ashish Mishra4.1 Introduction 684.1.1 Fog Computing and Edge Computing 684.1.2 Pervasive Computing 684.2 Overview of Machine Learning Frameworks for Fog and Edge Computing 694.2.1 TensorFlow 694.2.2 Keras 704.2.3 PyTorch 704.2.4 TensorFlow Lite 704.2.4.1 Use Pre-train Models 704.2.4.2 Convert the Model 704.2.4.3 On-device Inference 714.2.4.4 Model Optimization 714.2.5 Machine Learning and Deep Learning Techniques 714.2.5.1 Supervised, Unsupervised and Reinforcement Learning 714.2.5.2 Machine Learning, Deep Learning Techniques 724.2.5.3 Deep Learning Techniques 754.2.5.4 Efficient Deep Learning Algorithms for Inference 774.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing 784.2.6.1 Advantages using ML Algorithms 784.2.6.2 Disadvantages of using ML Algorithms 794.2.7 Hybrid ML Model for Smart IoT Applications 794.2.7.1 Multi-Task Learning 794.2.7.2 Ensemble Learning 804.2.8 Possible Applications in Fog Era using Machine Learning 814.2.8.1 Computer Vision 814.2.8.2 ML- Assisted Healthcare Monitoring System 814.2.8.3 Smart Homes 814.2.8.4 Behavior Analyses 824.2.8.5 Monitoring in Remote Areas and Industries 824.2.8.6 Self-Driving Cars 82Bibliography 825 Integrated Cloud Based Library Management in Intelligent IoT driven Applications 85Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal5.1 Introduction 865.1.1 Execution Plan for the Mobile Application 865.1.2 Main Contribution 865.2 Understanding Library Management 875.3 Integration of Mobile Platform with the Physical Library- Brief Concept 885.4 Database (Cloud Based) - A Must have Component for Library Automation 885.5 IoT Driven Mobile Based Library Management - General Concept 895.6 IoT Involved Real Time GUI (Cross Platform) Available to User 935.7 IoT Challenges 985.7.1 Infrastructure Challenges 995.7.2 Security Challenges 995.7.3 Societal Challenges 1005.7.4 Commercial Challenges 1015.8 Conclusion 102Bibliography 1046 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer 105Nikita, Harsh Sadawarti, Balwinder Kaur, and Jimmy Singla6.1 Introduction 1066.2 Related Works 1076.3 Conclusion 119Bibliography 1197 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation 123Saravjeet Singh and Jaiteg Singh7.1 Introduction to Fog and Edge Computing 1247.1.1 Need for Fog and Edge Computing 1247.1.2 Fog Computing 1257.1.2.1 Application Areas of Fog Computing 1257.1.3 Edge Computing 1267.1.3.1 Advantages of Edge Computing 1277.1.3.2 Application Areas of Fog Computing 1297.2 Introduction to Transportation System 1297.3 Route Finding Process 1317.3.1 Challenges Associated with Land Navigation and Routing Process 1327.4 Edge Architecture for Route Finding 1337.5 Technique Used 1357.6 Algorithms Used for the Location Identification and Route Finding Process 1377.6.1 Location Identification 1377.6.2 Path Generation Technique 1387.7 Results and Discussions 1407.7.1 Output 1407.7.2 Benefits of Edge-based Routing 1437.8 Conclusion 145Bibliography 1468 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator 149Natasha Tiwari, Anil Kumar, Pallavi Asthana, Sumita Mishra, and Bramah Hazela8.1 Introduction 1498.2 Related Work 1518.3 Vehicle Condition Monitoring through Acoustic Emission 1518.4 Piezo-resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE 1528.5 Designing of MEM Sensor 1538.6 Experimental Setup 1538.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB 1558.6.2 Design of MEMS Sensor using COMSOL Multiphysics 1558.6.3 Electrostatic Study Steps for the Optimized Tri-plate Comb Structure 1568.7 Result and Discussions 1578.8 Conclusion 158Bibliography 1589 IoT Driven Healthcare Monitoring System 161Md Robiul Alam Robel, Subrato Bharati, Prajoy Podder, and M. Rubaiyat Hossain Mondal9.1 Introduction 1619.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications 1629.1.2 Main Contribution 1649.2 General Concept for IoT Based Healthcare System 1649.3 View of the Overall IoT Healthcare System- Tiers Explained 1659.4 A Brief Design of the IoT Healthcare Architecture-individual Block Explanation 1669.5 Models/Frameworks for IoT use in Healthcare 1689.6 IoT e-Health System Model 1719.7 Process Flow for the Overall Model 1729.8 Conclusion 173Bibliography 17510 Fog Computing as Future Perspective in Vehicular Ad hoc Networks 177Harjit Singh, Dr. Vijay Laxmi, Dr. Arun Malik, and Dr. Isha10.1 Introduction 17810.2 Future VANET: Primary Issues and Specifications 18010.3 Fog Computing 18110.3.1 Fog Computing Concept 18310.3.2 Fog Technology Characterization 18310.4 Related Works in Cloud and Fog Computing 18510.5 Fog and Cloud Computing-based Technology Applications in VANET 18610.6 Challenges of Fog Computing in VANET 18810.7 Issues of Fog Computing in VANET 18910.8 Conclusion 190Bibliography 19111 An Overview to Design an Efficient and Secure Fog-assisted Data Collection Method in the Internet of Things 193Sofia, Arun Malik, Isha, and Aditya Khamparia11.1 Introduction 19311.2 Related Works 19411.3 Overview of the Chapter 19611.4 Data Collection in the IoT 19711.5 Fog Computing 19711.5.1 Why fog Computing for Data Collection in IoT? 19711.5.2 Architecture of Fog Computing 20011.5.3 Features of Fog Computing 20011.5.4 Threats of Fog Computing 20211.5.5 Applications of Fog Computing with the IoT 20311.6 Requirements for Designing a Data Collection Method 20411.7 Conclusion 206Bibliography 20612 Role of Fog Computing Platform in Analytics of Internet of Things- Issues, Challenges and Opportunities 209Mamoon Rashid and Umer Iqbal Wani12.1 Introduction to Fog Computing 20912.1.1 Hierarchical Fog Computing Architecture 21012.1.2 Layered Fog Computing Architecture 21212.1.3 Comparison of Fog and Cloud Computing 21312.2 Introduction to Internet of Things 21412.2.1 Overview of Internet of Things 21412.3 Conceptual Architecture of Internet of Things 21612.4 Relationship between Internet of Things and Fog Computing 21712.5 Use of Fog Analytics in Internet of Things 21812.6 Conclusion 218Bibliography 21813 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets 221Prabjot Kaur and Maria Jamal13.1 Introduction 22113.2 Preliminaries 22313.2.1 Introduction 22313.2.2 Fuzzy Sets 22313.2.3 Intuitionistic Fuzzy Sets 22413.2.4 Intuitionistic Fuzzy Relation 22413.2.5 Max-Min-Max Composition 22413.2.6 Linguistic Variable 22413.2.7 Distance Measure In Intuitionistic Fuzzy Sets 22413.2.7.1 The Hamming Distance 22413.2.7.2 Normalized Hamming Distance 22413.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix 22513.2.7.4 Revised Max-Min Average Composition of A and B (A Φ B) 22513.3 Max-Min-Max Algorithm for Disease Diagnosis 22513.4 Case Study 22613.5 Intuitionistic Fuzzy Max-Min Average Algorithm for Disease Diagnosis 22713.6 Result 22813.7 Code for Calculation 22913.8 Conclusion 23313.9 Acknowledgement 234Bibliography 23414 Security Attacks in Internet of Things 237Rajit Nair, Preeti Sharma, and Dileep Kumar Singh14.1 Introduction 23814.2 Reference Model of Internet of Things (IoT) 23814.3 IoT Communication Protocol 24614.4 IoT Security 24714.4.1 Physical Attack 24814.4.2 Network Attack 25214.4.3 Software Attack 25414.4.4 Encryption Attack 25514.5 Security Challenges in IoT 25614.5.1 Cryptographic Strategies 25614.5.2 Key Administration 25614.5.3 Denial of Service 25614.5.4 Authentication and Access Control 25714.6 Conclusion 257Bibliography 25715 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery 263Inderpreet Kaur, Kamaljit Singh Saini, and Jaiteg Singh Khaira15.1 Introduction 26415.2 Associated Work and Dimensions 26615.3 Need of Security in Telemedicine Domain and Internet of Things (IoT) 26715.3.1 Analytics Reports 26815.4 Fog Integrated Architecture for Telehealth Delivery 26815.5 Research Dimensions 26915.5.1 Benchmark Datasets 26915.6 Research Methodology and Implementation on Software Defined Networking 27015.6.1 Key Tools and Frameworks for IoT, Fog Computing and Edge Computing 27415.6.2 Simulation Analysis 27615.7 Conclusion 282Bibliography 28216 Fruit Fly Optimization Algorithm for Intelligent IoT Applications 287Satinder Singh Mohar, Sonia Goyal, and Ranjit Kaur16.1 An Introduction to the Internet of Things 28716.2 Background of the IoT 28816.2.1 Evolution of the IoT 28816.2.2 Elements Involved in IoT Communication 28816.3 Applications of the IoT 28916.3.1 Industrial 29016.3.2 Smart Parking 29016.3.3 Health Care 29016.3.4 Smart Offices and Homes 29016.3.5 Augment Maps 29116.3.6 Environment Monitoring 29116.3.7 Agriculture 29116.4 Challenges in the IoT 29116.4.1 Addressing Schemes 29116.4.2 Energy Consumption 29216.4.3 Transmission Media 29216.4.4 Security 29216.4.5 Quality of Service (QoS) 29216.5 Introduction to Optimization 29316.6 Classification of Optimization Algorithms 29316.6.1 Particle Swarm Optimization (PSO) Algorithm 29316.6.2 Genetic Algorithms 29416.6.3 Heuristic Algorithms 29416.6.4 Bio-inspired Algorithms 29416.6.5 Evolutionary Algorithms (EA) 29416.7 Network Optimization and IoT 29516.8 Network Parameters optimized by Different Optimization Algorithms 29516.8.1 Load Balancing 29516.8.2 Maximizing Network Lifetime 29516.8.3 Link Failure Management 29616.8.4 Quality of the Link 29616.8.5 Energy Efficiency 29616.8.6 Node Deployment 29616.9 Fruit Fly Optimization Algorithm 29716.9.1 Steps Involved in FOA 29716.9.2 Flow Chart of Fruit Fly Optimization Algorithm 29816.10 Applicability of FOA in IoT Applications 30016.10.1 Cloud Service Distribution in Fog Computing 30016.10.2 Cluster Head Selection in IoT 30016.10.3 Load Balancing in IoT 30016.10.4 Quality of Service in Web Services 30016.10.5 Electronics Health Records in Cloud Computing 30116.10.6 Intrusion Detection System in Network 30116.10.7 Node Capture Attack in WSN 30116.10.8 Node Deployment in WSN 30216.11 Node Deployment Using Fruit Fly Optimization Algorithm 30216.12 Conclusion 304Bibliography 30417 Optimization Techniques for Intelligent IoT Applications 311Priyanka Pattnaik, Subhashree Mishra, and Bhabani Shankar Prasad Mishra17.1 Cuckoo Search 31217.1.1 Introduction to Cuckoo 31217.1.2 Natural Cuckoo 31217.1.3 Artificial Cuckoo Search 31317.1.4 Cuckoo Search Algorithm 31317.1.5 Cuckoo Search Variants 31417.1.6 Discrete Cuckoo Search 31417.1.7 Binary Cuckoo Search 31417.1.8 Chaotic Cuckoo Search 31617.1.9 Parallel Cuckoo Search 31717.1.10 Application of Cuckoo Search 31717.2 Glow Worm Algorithm 31717.2.1 Introduction to Glow Worm 31717.2.2 Glow Worm Swarm Optimization Algorithm (GSO) 31717.3 Wasp Swarm Optimization 32117.3.1 Introduction to Wasp Swarm and Wasp Swarm Algorithm (WSO) 32117.3.2 Fish Swarm Optimization (FSO) 32217.3.3 Fruit Fly Optimization (FLO) 32217.3.4 Cockroach Swarm Optimization 32417.3.5 Bumblebee Algorithm 32417.3.6 Dolphin Echolocation 32517.3.7 Shuffled Frog-leaping Algorithm 32617.3.8 Paddy Field Algorithm 32717.4 Real World Applications Area 328Summary 329Bibliography 32918 Optimization Techniques for Intelligent IoT Applications in Transport Processes 333Muzafer Saračević, Zoran Lončarević, and Adnan Hasanović18.1 Introduction 33318.2 Related Works 33518.3 TSP Optimization Techniques 33618.4 Implementation and Testing of Proposed Solution 33818.5 Experimental Results 34218.5.1 Example Test with 50 Cities 34318.5.2 Example Test with 100 Cities 34418.6 Conclusion and Further Works 346Bibliography 34719 Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities 351Priyanka Rajan Kumar and Sonia Goel19.1 Fog Computing 35219.1.1 Need of Fog computing 35219.1.2 Architecture of Fog Computing 35319.1.3 Fog Computing Reference Architecture 35419.1.4 Processing on Fog 35519.2 Concept of Intelligent IoT Applications in Smart Computing Era 35519.3 Components of Edge and Fog Driven Algorithm 35619.4 Working of Edge and Fog Driven Algorithms 35719.5 Future Opportunistic Fog/Edge Computational Models 36019.5.1 Future Opportunistic Techniques 36119.6 Challenges of Fog Computing for Intelligent IoT Applications 36119.7 Applications of Cloud Based Computing for Smart Devices 363Bibliography 36420 Security and Privacy Issues in Fog/Edge/Pervasive Computing 369Shweta Kaushik and Charu Gandhi20.1 Introduction to Data Security and Privacy in Fog Computing 37020.2 Data Protection/ Security 37520.3 Great Security Practices In Fog Processing Condition 37720.4 Developing Patterns in Security and Privacy 38120.5 Conclusion 385Bibliography 38521 Fog and Edge Driven Security & Privacy Issues in IoT Devices 389Deepak Kumar Sharma, Aarti Goel, and Pragun Mangla21.1 Introduction to Fog Computing 39021.1.1 Architecture of Fog 39021.1.2 Benefits of Fog Computing 39221.1.3 Applications of Fog with IoT 39321.1.4 Major Challenges for Fog with IoT 39421.1.5 Security and Privacy Issues in Fog Computing 39521.2 Introduction to Edge Computing 39921.2.1 Architecture and Working 40021.2.2 Applications and use Cases 40021.2.3 Characteristics of Edge Computing 40321.2.4 Challenges of Edge Computing 40421.2.5 How to Protect Devices “On the Edge”? 40521.2.6 Comparison with Fog Computing 405Bibliography 406Index 409