Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing

Contents
About the Editors
1 Introduction to intelligent network design driven by big data analytics, IoT, AI and cloud computing
Preface
Chapter 2: Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks
Chapter 3: An intelligent verification management approach for efficient VLSI computing system
Chapter 4: Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques
Chapter 5: Accurate management and progression of Big Data analysis
Chapter 6: Cram on data recovery and backup cloud computing techniques
Chapter 7: An adaptive software defined networking (SDN) for load balancing in cloud computing
Chapter 8: Emerging security challenges in cloud computing: An insight
Chapter 9: Factors responsible and phases of speaker recognition system
Chapter 10: IoT-based water quality assessment using fuzzy logic controller
Chapter 11: Design and analysis of wireless sensor network for intelligent transportation and industry automation
Chapter 12: A review of edge computing in healthcare Internet of Things: theories, practices, and challenges
Chapter 13: Image processing for medical images on the basis of intelligence and bio computing
Chapter 14: IoT-based architecture for smart health-care systems
Chapter 15: IoT-based heart disease prediction system
Chapter 16: DIAIF: detection of interest flooding using artificial intelligence-based framework in NDN android
Chapter 17: Intelligent and cost-effective mechanism for monitoring road quality using machine learning
References
2 Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks
2.1 Evolution of networks: everything is connected
2.1.1 Intelligent devices
2.1.2 Intelligent devices connection with networks
2.2 Huge volume of data generation by intelligent devices
2.2.1 Issues and challenges of Big Data Analytics
2.2.2 Storage of Big Data
2.3 Need of data analysis by business
2.3.1 Sources of information
2.3.2 Data visualization
2.3.3 Analyzing Big Data for effective use of business
2.3.4 Intelligent devices thinking intelligently
2.4 Artificial intelligence and machine learning in networking
2.4.1 Role of ML in networks
2.5 Intent-based networking
2.6 Role of programming
2.6.1 Basic programming using Blockly
2.6.2 Blockly games
2.7 Role of technology to design a model
2.7.1 Electronic toolkits
2.7.2 Programming resources
2.8 Relation of AI, ML, and IBN
2.9 Business challenges and opportunities
2.9.1 The evolving job market
2.10 Security
2.10.1 Challenges to secure device and networks
2.11 Summary
References
3 An intelligent verification management approach for efficient VLSI computing system
3.1 Introduction
3.2 Literature study
3.3 Verification management approach: Case Study 1
3.3.1 The pseudo random number generator in a verification environment
3.3.2 Implementation of PRNG in higher abstraction language and usage of DPI
3.4 Verification management approach: Case Study 2
3.5 Challenges and research direction
3.5.1 Challenges in intelligent systems
3.6 Conclusion
References
4 Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques
4.1 Introduction
4.1.1 EDM
4.1.2 EDM process
4.1.3 Methods and techniques
4.1.4 Application areas of data mining
4.2 Literature survey
4.3 Materials and methods
4.3.1 Dataset description
4.3.2 Classification algorithms
4.3.3 FS algorithms
4.3.4 Data preprocessing phase
4.4 Implementation of the proposed algorithms
4.4.1 Model construction for the standard classifier
4.4.2 Implementation after attribute selection using ranker method
4.5 Result analysis and discussion
4.6 Conclusion
References
5 Accurate management and progression of Big Data Analysis
5.1 Introduction
5.1.1 Examples of Big Data
5.2 Big Data Analysis
5.2.1 Life cycle of Big Data
5.2.2 Classification of the Big Data
5.2.3 Working of Big Data Analysis
5.2.4 Common flaws that undermine Big Data Analysis
5.2.5 Advantages of Big Data Analysis
5.3. Processing techniques
5.3.1 Traditional method
5.3.2 MapReduce
5.3.3 Advantages of MapReduce
5.4 Cyber crime
5.4.1 Different strategies in Big Data to help in various circumstances
5.4.2 Big Data Analytics and cybercrime
5.4.3 Security issues associated with Big Data
5.4.4 Big Data Analytics in digital forensics
5.5 Real-time edge analytics for Big Data in IoT
5.6 Conclusion
References
6 Cram on data recovery and backup cloud computing techniques
6.1 Introduction
6.1.1 Origin of cloud
6.1.2 Sole features of cloud computing
6.1.3 Advantages of cloud computing
6.1.4 Disadvantages of cloud computing
6.2 Classification of data recovery and backup
6.2.1 Recovery
6.2.2 Backup
6.3 Study on data recovery and backup cloud computing techniques
6.3.1 Backup of real-time data and recovery using cloud computing
6.3.2 Data recovery and security in cloud
6.3.3 Amoeba: An autonomous backup and recovery solid-state drives for ransomware attack defense
6.3.4 A cloud-based automatic recovery and backup system for video compression
6.3.5 Efficient and reliable data recovery techniques in cloud computing
6.3.6 Cost-efficient remote backup services for enterprise cloud
6.3.7 DR-cloud: Multi-cloud-based disaster recovery service
6.4 Conclusion
References
7 An adaptive software-defined networking (SDN) for load balancing in cloud computing
7.1 Introduction
7.2 Related works
7.3 Architecture overview of SDN
7.4 Load-balancing framework in SDN
7.4.1 Classification of SDN controller architectures
7.5 Problem statement
7.5.1 Selection strategy of controller head
7.5.2 Network setup
7.6 Illustration
7.7 Results and discussion
7.7.1 Comparison of throughput
7.7.2 Comparison of PTR
7.7.3 Comparison of number of packet loss
7.8 Conclusion
References
8 Emerging security challenges in cloud computing: an insight
8.1 Introduction
8.1.1 An introduction to cloud computing and its security
8.2 The security issues in different cloud models and threat management techniques
8.2.1 Five most indispensable characteristics
8.2.2 The security issues in cloud service model
8.2.3 Security issues in cloud deployment models
8.2.4 Security challenges in the cloud environment
8.2.5 The threat management techniques
8.3 Review on existing proposed models
8.3.1 SeDaSC
8.3.2 The ‘SecCloud’ protocol
8.3.3 Data accountability and auditing for secure cloud data storage
8.4 Conclusion and future prospectives
References
9 Factors responsible and phases of speaker recognition system
9.1 Study of related research
9.2 Phases of speaker recognition system
9.2.1 Speaker database collection
9.2.2 Feature extraction
9.2.3 Feature mapping
9.3 Basics of speech signals
9.3.1 Speech production system
9.3.2 Speech perception
9.3.3 Speech signals
9.3.4 Properties of the sinusoids
9.3.5 Windowing signals
9.3.6 Zero-crossing rate
9.3.7 Autocorrelation
9.4 Features of speech signals
9.4.1 Physical features
9.4.2 Perceptual features
9.4.3 Signal features
9.5 Localization of speaker
9.6 Conclusion
References
10 IoT-based water quality assessment using fuzzy logic controller
10.1 Introduction
10.2 Experimental procedures
10.3 Working
10.4 Results and discussions
10.5 Conclusion
References
11 Design and analysis of wireless sensor network for intelligent transportation and industry automation
11.1 Introduction
11.2 Wireless sensor network
11.3 WSN application
11.4 Limitations of WSN
11.5 Literature survey
11.6 Related work
11.7 Methodology
11.7.1 Throughput
11.7.2 Delay
11.7.3 Packet delivery ratio
11.7.4 Design of WiMAX-based WSN system
11.8 Related results
11.9 Conclusion
11.10 Future scope
References
12 A review of edge computing in healthcare Internet of things: theories, practices and challenges
12.1 Introduction
12.2 Cloud computing in healthcare and its limitations
12.2.1 Public cloud
12.2.2 Private cloud
12.2.3 Hybrid cloud
12.2.4 Community cloud
12.3 Edge computing and its advantages over cloud computing
12.3.1 Advantages of edge/fog computing
12.3.2 Disadvantages of edge/fog computing
12.4 IoT in healthcare
12.5 Edge computing in healthcare
12.6 Machine learning in healthcare
12.7 Integrated role of IOT, ML and edge computing in healthcare
12.7.1 Patient care during surgical procedure
12.7.2 Patient care at home
12.7.3 Patient care in ambulance
12.8 Modelling and simulation tools for edge/fog computing
12.9 Edge computing in Covid-19 pandemic era
12.10 Challenges of edge computing
12.11 Conclusion
References
13 Image Processing for medical images on the basis of intelligence and biocomputing
13.1 Introduction
13.1.1 What is an image?
13.2 Image processing
13.2.1 Equivalent image processing
13.2.2 Digital image processing
13.2.3 Digital image
13.2.4 Applications of color models
13.2.5 Applications of digital image processing
13.2.6 Fundamental steps in digital image processing
13.2.7 Components of an image processing system
13.3 Medical imaging
13.4 Deep learning techniques
13.4.1 Uses of image processing
13.5 Convolutional neural network
13.6 Convolution layers
13.6.1 Training phase
13.6.2 Training strategies
13.6.3 CNN performance
13.6.4 Convolutional neutral networks with AI
13.6.5 CNN layers
13.6.6 CNN image classifier
13.7 Deep learning for lung disease detection
13.7.1 Preprocessing of images
13.7.2 Training
13.7.3 Classification
13.8 Conclusion
References
14 IoT-based architecture for smart health-care systems
14.1 Introduction
14.2 Literature survey
14.3 Related works
14.4 Hardware components and sensors
14.4.1 Development boards
14.4.2 Sensors
14.4.3 Other modules
14.5 Proposed work
14.5.1 Hardware components used
14.6 Implementation and results
14.7 Conclusion
References
15 IoT-based heart disease prediction system
15.1 Introduction
15.1.1 Deep learning
15.2 Related work
15.3 Proposed system
15.3.1 Arduino UNO
15.3.2 Heartbeat sensor
15.3.3 Temperature sensor
15.3.4 Pressure sensor
15.3.5 Liquid crystal display (LCD) display (16 × 2)
15.4 Advantages of proposed system
15.5 Limitations of proposed system
15.6 Results and discussion
15.7 Conclusion
References
16 DIAIF: Detection of Interest Flooding using Artificial Intelligence-based Framework in NDN android
16.1 Introduction
16.2 Background
16.2.1 ICN communication model
16.2.2 Generating IFA in NDN android
16.3 Proposed methodology: RD Iterative Adaptive Inverse Filtering (RD-IAIF)
16.3.1 Attack detection
16.4 Real-time deployment on emergency applications
16.4.1 Communication establishment through NFD
16.4.2 NDN application on android
16.4.3 Attack detection on NFD android using AI
16.5 Conclusion
References
17 Intelligent and cost-effective mechanism for monitoring road quality using machine learning
17.1 Introduction
17.1.1 Definition of TMS
17.1.2 Issues and challenges faced in TMS
17.2 Literature review of machine learning for road condition detection
17.3 Gaps identified in the literature
17.4 Proposed methodology
17.5 Implementation
17.5.1 Flutter-based application
17.5.2 Machine learning models
17.6 Results
17.6.1 Comparison with the existing models
17.7 Conclusion and future scope
References
18 Conclusion
18.1 Conclusion
References
Index