Оглавление
Группа авторов. Integration of Cloud Computing with Internet of Things
Table of Contents
List of Figures
List of Tables
Guide
Pages
Integration of Cloud Computing with Internet of Things. Foundations, Analytics, and Applications
Preface
Acknowledgements
1. Internet of Things: A Key to Unfasten Mundane Repetitive Tasks
1.1 Introduction
1.2 The IoT Scenario
1.3 The IoT Domains. 1.3.1 The IoT Policy Domain
1.3.2 The IoT Software Domain
1.3.2.1 IoT in Cloud Computing (CC)
1.3.2.2 IoT in Edge Computing (EC)
1.3.2.3 IoT in Fog Computing (FC)
1.3.2.4 IoT in Telecommuting
1.3.2.5 IoT in Data-Center
1.3.2.6 Virtualization-Based IoT (VBIoT)
1.4 Green Computing (GC) in IoT Framework
1.5 Semantic IoT (SIoT)
1.5.1 Standardization Using oneM2M
1.5.2 Semantic Interoperability (SI)
1.5.3 Semantic Interoperability (SI) Security
1.5.4 Semantic IoT vs Machine Learning
1.6 Conclusions
References
2. Measures for Improving IoT Security
2.1 Introduction
2.2 Perceiving IoT Security
2.3 The IoT Safety Term
2.4 Objectives. 2.4.1 Enhancing Personal Data Access in Public Repositories
2.4.2 Develop and Sustain Ethicality
2.4.3 Maximize the Power of IoT Access
2.4.4 Understanding Importance of Firewalls
2.5 Research Methodology
2.6 Security Challenges
2.6.1 Challenge of Data Management
2.7 Securing IoT. 2.7.1 Ensure User Authentication
2.7.2 Increase User Autonomy
2.7.3 Use of Firewalls
2.7.4 Firewall Features
2.7.5 Mode of Camouflage
2.7.6 Protection of Data
2.7.7 Integrity in Service
2.7.8 Sensing of Infringement
2.8 Monitoring of Firewalls and Good Management. 2.8.1 Surveillance
2.8.2 Forensics
2.8.3 Secure Firewalls for Private
2.8.4 Business Firewalls for Personal
2.8.5 IoT Security Weaknesses
2.9 Conclusion
References
3. An Efficient Fog-Based Model for Secured Data Communication
3.1 Introduction
3.1.1 Fog Computing Model
3.1.2 Correspondence in IoT Devices
3.2 Attacks in IoT
3.2.1 Botnets
3.2.2 Man-In-The-Middle Concept
3.2.3 Data and Misrepresentation
3.2.4 Social Engineering
3.2.5 Denial of Service
3.2.6 Concerns
3.3 Literature Survey
3.4 Proposed Model for Attack Identification Using Fog Computing
3.5 Performance Analysis
3.6 Conclusion
References
4. An Expert System to Implement Symptom Analysis in Healthcare
4.1 Introduction
4.2 Related Work
4.3 Proposed Model Description and Flow Chart. 4.3.1 Flowchart of the Model
4.3.1.1 Value of Symptoms
4.3.1.2 User Interaction Web Module
4.3.1.3 Knowledge-Base
4.3.1.4 Convolution Neural Network
4.3.1.5 CNN-Fuzzy Inference Engine
4.4 UML Analysis of Expert Model
4.4.1 Expert Module Activity Diagram
4.4.2 Ontology Class Collaboration Diagram
4.5 Ontology Model of Expert Systems
4.6 Conclusion and Future Scope
References
5. An IoT-Based Gadget for Visually Impaired People
5.1 Introduction
5.2 Related Work
5.3 System Design
5.4 Results and Discussion
5.5 Conclusion
5.6 Future Work
References
6. IoT Protocol for Inferno Calamity in Public Transport
6.1 Introduction
6.2 Literature Survey
6.3 Methodology
6.3.1 IoT Message Exchange With Cloud MQTT Broker Based on MQTT Protocol
6.3.2 Hardware Requirement
6.4 Implementation
6.4.1 Interfacing Diagram
6.5 Results
6.6 Conclusion and Future Work
References
7. Traffic Prediction Using Machine Learning and IoT
7.1 Introduction
7.1.1 Real Time Traffic
7.1.2 Traffic Simulation
7.2 Literature Review
7.3 Methodology
7.4 Architecture
7.4.1 API Architecture
7.4.2 File Structure
7.4.3 Simulator Architecture
7.4.4 Workflow in Application
7.4.5 Workflow of Google APIs in the Application
7.5 Results
7.5.1 Traffic Scenario
7.5.1.1 Low Traffic
7.5.1.2 Moderate Traffic
7.5.1.3 High Traffic
7.5.2 Speed Viewer
7.5.3 Traffic Simulator
7.5.3.1 1st View
7.5.3.2 2nd View
7.5.3.3 3rd View
7.6 Conclusion and Future Scope
References
8. Application of Machine Learning in Precision Agriculture
8.1 Introduction
8.2 Machine Learning
8.2.1 Supervised Learning
8.2.2 Unsupervised Learning
8.2.3 Reinforcement Learning
8.3 Agriculture
8.4 ML Techniques Used in Agriculture
8.4.1 Soil Mapping
8.4.2 Seed Selection
8.4.3 Irrigation/Water Management
8.4.4 Crop Quality
8.4.5 Disease Detection
8.4.6 Weed Detection
8.4.7 Yield Prediction
8.5 Conclusion
References
9. An IoT-Based Multi Access Control and Surveillance for Home Security
9.1 Introduction
9.2 Related Work
9.3 Hardware Description
9.3.1 Float Sensor
9.3.2 Map Matching
9.3.3 USART Cable
9.4 Software Design
9.5 Conclusion
References
10. Application of IoT in Industry 4.0 for Predictive Analytics
10.1 Introduction
10.2 Past Literary Works. 10.2.1 Maintenance-Based Monitoring
10.2.2 Data Driven Approach to RUL Finding in Industry
10.2.3 Philosophy of Industrial-IoT Systems and its Advantages in Different Domain
10.3 Methodology and Results
10.4 Conclusion
References
11. IoT and Its Role in Performance Enhancement in Business Organizations
11.1 Introduction
11.1.1 Scientific Issues in IoT
11.1.2 IoT in Organizations
11.1.3 Technology and Business
11.1.4 Rewards of Technology in Business
11.1.5 Shortcomings of Technology in Business
11.1.6 Effect of IoT on Work and Organization
11.2 Technology and Productivity
11.3 Technology and Future of Human Work
11.4 Technology and Employment
11.5 Conclusion
References
12. An Analysis of Cloud Computing Based on Internet of Things
12.1 Introduction
12.1.1 Generic Architecture
12.2 Challenges in IoT
12.3 Technologies Used in IoT
12.4 Cloud Computing
12.4.1 Service Models of Cloud Computing
12.5 Cloud Computing Characteristics
12.6 Applications of Cloud Computing
12.7 Cloud IoT
12.8 Necessity for Fusing IoT and Cloud Computing
12.9 Cloud-Based IoT Architecture
12.10 Applications of Cloud-Based IoT
12.11 Conclusion
References
13. Importance of Fog Computing in Emerging Technologies-IoT
13.1 Introduction
13.2 IoT Core
13.3 Need of Fog Computing
References
14. Convergence of Big Data and Cloud Computing Environment
14.1 Introduction
14.2 Big Data: Historical View
14.2.1 Big Data: Definition
14.2.2 Big Data Classification
14.2.3 Big Data Analytics
14.3 Big Data Challenges
14.4 The Architecture
14.4.1 Storage or Collection System
14.4.2 Data Care
14.4.3 Analysis
14.5 Cloud Computing: History in a Nutshell
14.5.1 View on Cloud Computing and Big Data
14.6 Insight of Big Data and Cloud Computing
14.6.1 Cloud-Based Services
14.6.2 At a Glance: Cloud Services
14.7 Cloud Framework
14.7.1 Hadoop
14.7.2 Cassandra
14.7.2.1 Features of Cassandra
14.7.3 Voldemort
14.7.3.1 A Comparison With Relational Databases and Benefits
14.8 Conclusions
14.9 Future Perspective
References
15. Data Analytics Framework Based on Cloud Environment
15.1 Introduction
15.2 Focus Areas of the Chapter
15.3 Cloud Computing
15.3.1 Cloud Service Models
15.3.1.1 Software as a Service (SaaS)
15.3.1.2 Platform as a Service (PaaS)
15.3.1.3 Infrastructure as a Service (IaaS)
15.3.1.4 Desktop as a Service (DaaS)
15.3.1.5 Analytics as a Service (AaaS)
15.3.1.6 Artificial Intelligence as a Service (AIaaS)
15.3.2 Cloud Deployment Models
15.3.3 Virtualization of Resources
15.3.4 Cloud Data Centers
15.4 Data Analytics
15.4.1 Data Analytics Types. 15.4.1.1 Descriptive Analytics
15.4.1.2 Diagnostic Analytics
15.4.1.3 Predictive Analytics
15.4.1.4 Prescriptive Analytics
15.4.1.5 Big Data Analytics
15.4.1.6 Augmented Analytics
15.4.1.7 Cloud Analytics
15.4.1.8 Streaming Analytics
15.4.2 Data Analytics Tools
15.5 Real-Time Data Analytics Support in Cloud
15.6 Framework for Data Analytics in Cloud
15.6.1 Data Analysis Software as a Service (DASaaS)
15.6.2 Data Analysis Platform as a Service (DAPaaS)
15.6.3 Data Analysis Infrastructure as a Service (DAIaaS)
15.7 Data Analytics Work-Flow
15.8 Cloud-Based Data Analytics Tools
15.8.1 Amazon Kinesis Services
15.8.2 Amazon Kinesis Data Firehose
15.8.3 Amazon Kinesis Data Streams
15.8.4 Amazon Textract
15.8.5 Azure Stream Analytics
15.9 Experiment Results
15.10 Conclusion
References
16. Neural Networks for Big Data Analytics
16.1 Introduction
16.2 Neural Networks—An Overview
16.3 Why Study Neural Networks?
16.4 Working of Artificial Neural Networks. 16.4.1 Single-Layer Perceptron
16.4.2 Multi-Layer Perceptron
16.4.3 Training a Neural Network
16.4.4 Gradient Descent Algorithm
16.4.5 Activation Functions
16.5 Innovations in Neural Networks. 16.5.1 Convolutional Neural Network (ConvNet)
16.5.2 Recurrent Neural Network
16.5.3 LSTM
16.6 Applications of Deep Learning Neural Networks
16.7 Practical Application of Neural Networks Using Computer Codes
16.8 Opportunities and Challenges of Using Neural Networks
16.9 Conclusion
References
17. Meta-Heuristic Algorithms for Best IoT Cloud Service Platform Selection
17.1 Introduction
17.2 Selection of a Cloud Provider in Federated Cloud
17.3 Algorithmic Solution
17.3.1 TLBO Algorithm (Teaching-Learning-Based Optimization Algorithm)
17.3.1.1 Teacher Phase: Generation of a New Solution
17.3.1.2 Learner Phase: Generation of New Solution
17.3.1.3 Representation of the Solution
17.3.2 JAYA Algorithm
17.3.2.1 Representation of the Solution
17.3.3 Bird Swarm Algorithm
17.3.3.1 Forging Behavior
17.3.3.2 Vigilance Behavior
17.3.3.3 Flight Behavior
17.3.3.4 Representation of the Solution
17.4 Analyzing the Algorithms
17.5 Conclusion
References
18. Legal Entanglements of Cloud Computing In India
18.1 Cloud Computing Technology
18.2 Cyber Security in Cloud Computing
18.3 Security Threats in Cloud Computing
18.3.1 Data Breaches
18.3.2 Denial of Service (DoS)
18.3.3 Botnets
18.3.4 Crypto Jacking
18.3.5 Insider Threats
18.3.6 Hijacking Accounts
18.3.7 Insecure Applications
18.3.8 Inadequate Training
18.3.9 General Vulnerabilities
18.4 Cloud Security Probable Solutions. 18.4.1 Appropriate Cloud Model for Business
18.4.2 Dedicated Security Policies Plan
18.4.3 Multifactor Authentication
18.4.4 Data Accessibility
18.4.5 Secure Data Destruction
18.4.6 Encryption of Backups
18.4.7 Regulatory Compliance
18.4.8 External Third-Party Contracts and Agreements
18.5 Cloud Security Standards
18.6 Cyber Security Legal Framework in India
18.7 Privacy in Cloud Computing—Data Protection Standards
18.8 Recognition of Right to Privacy
18.9 Government Surveillance Power vs Privacy of Individuals
18.10 Data Ownership and Intellectual Property Rights
18.11 Cloud Service Provider as an Intermediary
18.12 Challenges in Cloud Computing. 18.12.1 Classification of Data
18.12.2 Jurisdictional Issues
18.12.3 Interoperability of the Cloud
18.12.4 Vendor Agreements
18.13 Conclusion
References
19. Securing the Pharma Supply Chain Using Blockchain
19.1 Introduction
19.2 Literature Review
19.2.1 Current Scenario
19.2.2 Proposal
19.3 Methodology
19.4 Results
19.5 Conclusion and Future Scope
References
Index
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