Fog Computing for Intelligent Cloud IoT Systems

• Record Nr.: UNINA9911019372203321
• Autore: Banerjee Chandan
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• ISBN: 9781394175345; 1394175345; 9781394175338; 1394175337
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (453 pages)
• Collana: Advances in Learning Analytics for Intelligent Cloud-IoT Systems Series
• Altri autori (Persone): GhoshAnupam; ChakrabortyRajdeep; ElngarAhmed A
• Disciplina: 004.67/82
• Soggetti: Internet of things; Cloud computing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Study of Fog Computing and Machine Learning -- Chapter 1 Fog Computing: Architecture and Application -- 1.1 Introduction -- 1.2 Fog Computing: An Overview -- 1.3 Fog Computing for Intelligent Cloud-IoT System -- 1.4 Fog Computing Architecture -- 1.5 Basic Modules of Fog Computing -- 1.6 Cloud Computing vs. Fog Computing -- 1.7 Fog Computing vs. IoT -- 1.8 Applications of Fog Computing -- 1.9 Will the Fog Be Taken Over by the Cloud? -- 1.10 Challenges in Fog Computing -- 1.11 Future of Fog Computing -- 1.12 Conclusion -- References -- Chapter 2 A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Computation Offloading Techniques -- 2.3.1 MAUI Architecture -- 2.3.2 Clone-Cloud Based Model -- 2.3.3 Cuckoo Design -- 2.3.4 MACS Architecture -- 2.3.5 AHP and TOPSIS Design Technique -- 2.3.6 Energy Aware Design for Workflows -- 2.3.7 MCSOS Architecture -- 2.3.8 Cloudlet -- 2.3.9 Jade -- 2.3.10 Phone2Cloud -- 2.4 Conclusion ; -- 2.5 Future Scope -- 2.6 Acknowledgement -- References -- Chapter 3 Fog Computing for Intelligent Cloud-IoT System: Optimization of Fog Computing in Industry 4.0 -- 3.1 Introduction -- 3.1.1 Industry 4.0 -- 3.1.2 Fog Computing -- 3.1.3 Fog Nodes -- 3.2 How Fog Computing with IIoT Brings Revolution -- 3.2.1 Hierarchical Fog Computing Architecture -- 3.2.2 Layered Fog Computing Architecture -- 3.3 Applications of Fog Computing on Which Industries Rely -- 3.3.1 In the Field of Agriculture -- 3.3.2 In Healthcare Industry -- 3.3.3 In Smart Cities -- 3.3.4 In Education -- 3.3.5 In Entertainment -- 3.4 Data Analysis -- 3.5 Illustration of Fog Computing and Application -- 3.5.1 Figures -- 3.6 Conclusion -- 3.7 Future Scope/Acknowledgement -- References.; Chapter 4 Machine Learning Integration in Agriculture Domain: Concepts and Applications -- 4.1 Introduction -- 4.2 Fog Computing in Agriculture -- 4.2.1 Smart Farming -- 4.3 Methodology -- 4.3.1 Data Source -- 4.3.2 Data Analysis and Pre-Processing -- 4.3.3 Feature Extraction -- 4.3.4 Model Selection -- 4.3.5 Hyper-Parameter Tuning -- 4.3.6 Train-Test Split -- 4.4 Results and Discussion -- 4.4.1 Modeling Algorithms -- 4.5 Conclusion -- 4.6 Future Scope -- References -- Chapter 5 Role of Intelligent IoT Applications in Fog Computing -- 5.1 Introduction -- 5.1.1 PaaS/SaaS Platforms Have Various Benefits That are Crucial to the Success of Many Small IoT Startup Businesses -- 5.2 Cloud Service Model's Drawbacks -- 5.3 Fog Computation -- 5.3.1 Standardization -- 5.3.2 Growing Use Cases for Fog Computing -- 5.3.3 IoT Applications with Intelligence -- 5.3.4 Graphics Processing Units -- 5.4 Recompenses of FoG -- 5.5 Limitation of Fog Computing -- 5.6 Fog Computing with IoT -- 5.6.1 Benefits of Fog Computing with IoT -- 5.6.2 Challenges of Fog Computing with IoT -- 5.7 Edge AI Embedded -- 5.7.1 Key Software Characteristics in Fog Computing -- 5.7.2 Fog Cluster Management -- 5.7.3 Technology for Computing in the Fog -- 5.7.4 Concentrating Intelligence -- 5.7.5 Device-Driven Intelligence -- 5.8 Network Intelligence Objectives -- 5.9 Farming with Fog Computation (Case Study) -- 5.10 Conclusion -- References -- Chapter 6 SaaS-Based Data Visualization Platform-A Study in COVID-19 Perspective -- 6.1 Introduction -- 6.1.1 Motivation and the Problem of Interest -- 6.2 Summary of Objectives -- 6.3 What is a Pandemic? -- 6.4 COVID-19 and Information Gap -- 6.5 Data Visualization and its Importance -- 6.6 Data Management with Data Visualization -- 6.7 What is Power BI? -- 6.7.1 Data Collection &amp -- Wrangling -- 6.7.2 Data Description &amp -- Source.; 6.7.3 Data Transformation -- 6.8 Output Data -- 6.9 Design &amp -- Implementation -- 6.9.1 Integration Design -- 6.9.2 High-Level Process Flow -- 6.9.3 Solution Flow -- 6.10 Dashboard Development -- 6.10.1 Landing Page -- 6.10.2 Approach and Design -- 6.10.3 Helpline Information -- 6.10.3.1 Approach and Design -- 6.10.4 Symptom Detection -- 6.10.4.1 Approach and Design -- 6.10.5 Testing Lab Information -- 6.10.5.1 Approach and Design -- 6.10.6 Hospital Information -- 6.10.6.1 Approach and Design -- 6.10.7 Oxygen Suppliers Information -- 6.10.7.1 Approach and Design -- 6.10.8 COVID Cases Information -- 6.10.8.1 Approach and Design -- 6.10.9 Vaccination Information -- 6.10.9.1 Approach and Design -- 6.10.10 Patients' Information -- 6.10.10.1 Approach and Design -- 6.11 Advantages and its Impact -- 6.12 Conclusion and Future Scope -- References -- Chapter 7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis -- 7.1 Introduction -- 7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis -- 7.2 Pre-Processing Medical Data for Machine Learning -- 7.3 Supervised Learning Algorithms for Medical Data Analysis -- 7.3.1 ; Linear Regression Algorithm -- 7.3.2 Logistic Regression Algorithm -- 7.3.3 Decision Trees Algorithm -- 7.3.3.1 Advantages of Decision Tree Algorithm -- 7.3.3.2 Limitations of Decision Tree Algorithm -- 7.3.4 Random Forest Algorithm -- 7.3.4.1 Advantages of Random Forest Algorithm -- 7.3.4.2 Limitations of Random Forest Algorithm -- 7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis -- 7.3.5 Support Vector Machine Algorithm -- 7.3.5.1 Advantages of SVM Algorithm -- 7.3.5.2 Limitations of SVM Algorithm -- 7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis -- 7.3.6 Naive Bayes Algorithm -- 7.3.7 KNN (K-Nearest Neighbor Algorithm) -- 7.3.7.1 Applications of K-NN Algorithm.; 7.3.8 Deep Learning Algorithm -- 7.3.9 Deep Learning Application -- 7.4 Unsupervised Learning Algorithms for Medical Data Analysis -- 7.4.1 Clustering Algorithm -- 7.4.2 Principal Component Analysis Algorithm -- 7.4.3 Independent Component Analysis Algorithm -- 7.4.4 Association Rule Mining Algorithm -- 7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis -- 7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis -- 7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis -- 7.8 Conclusion -- References -- Part II: Applications and Analytics -- Chapter 8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities -- 8.1 Introduction -- 8.2 Research Methodology -- 8.3 Application Taxonomy in FC-Based Healthcare -- 8.3.1 Diagnosis -- 8.3.2 Monitoring -- 8.3.3 Notification -- 8.3.4 Zest of Applications of FC in Healthcare -- 8.4 Challenges in FC-Based Healthcare -- 8.4.1 QoS Optimization -- 8.4.2 Patient Authentication and Access Control -- 8.4.3 Data Processing -- 8.4.4 Data Privacy Preservation -- 8.4.5 Energy Efficiency -- 8.5 Research Opportunities -- 8.5.1 Research Opportunity in Computing -- 8.5.2 Research Opportunity in Security -- 8.5.3 Research Opportunity in Services -- 8.5.4 Research Opportunity in Implementation -- 8.6 Conclusion -- References -- Chapter 9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application -- 9.1 Introduction -- 9.2 Review of Related Research Articles -- 9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0 -- 9.2.1.1 Magnetostrictive Material -- 9.2.1.2 Magneto-Optical (MO) Materials -- 9.2.1.3 Magnetic Fluid (MF) Materials -- 9.2.1.4 Magnetically Sensitive Materials and Their Application -- 9.2.1.5 Optical Fiber Current Sensors.; 9.3 Research Gaps -- 9.4 Emerging Research Directions -- 9.5 The Broad Concept of FBG Sensor Applications in Industry 4.0 -- 9.6 Conclusion -- References -- Chapter 10 Fog Computing-Enabled Cancer Cell Detection System Using Convolution Neural Network in Internet of Medical Things -- 10.1 Introduction -- 10.2 Fog Computing: Approach of IoMT -- 10.3 Relationship Between IoMT and Deep Neural Network -- 10.4 Fog Computing Enabled CNN for Medical Imaging -- 10.5 Algorithm Approach of Proposed Model -- 10.6 Result and Analysis -- 10.7 Conclusion -- References -- Chapter 11 Application of IoT in Smart Farming and Precision Farming: A Review -- 11.1 Introduction -- 11.2 Methodologies Used in Precision Agriculture -- 11.3 Contribution of IoT in Agriculture -- 11.4 IoT Enabled Smart Farming -- 11.5 IoT Enabled Precision Farming -- 11.6 Machine Learning Enable Precision Farming -- 11.7 Application of Operational Research Method in Farming System -- 11.8 Conclusion -- 11.9 Future Scope -- References -- Chapter 12 Big IoT Data Analytics in Fog Computing -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Motivation -- 12.4 Fog Computing -- 12.4.1 Fog Node -- 12.4.2 Characteristics of Fog Computing -- 12.4.3 Attributes of Fog Node -- ; 12.4.4 Fog Computing Service Model -- 12.4.5 Fog Computing Architecture -- 12.4.6 Data Flow and Control Flow in Fog Architecture -- 12.4.7 Fog Deployment Models -- 12.5 Big Data -- 12.5.1 What is Big Data? -- 12.5.2 Source of Big Data -- 12.5.3 Characteristic of Big Data -- 12.6 Big Data Analytics Using Fog Computing -- 12.7 Conclusion -- References -- Chapter 13 IOT-Based Patient Monitoring System in Real Time -- 13.1 Introduction -- 13.2 Components Used -- 13.2.1 Node MCU -- 13.2.2 Heart Rate/Pulse Sensor -- 13.2.3 Temperature Sensor (LM35) -- 13.3 IoT Platform -- 13.3.1 ThingSpeak-IoT Platform Used in This Work.; 13.4 Proposed Method.
• Sommario/riassunto: FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources. Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user's experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service. Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing's help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage. This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered. Audience The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.