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

427 Pages·2022·7.615 MB·English

by  Sunil Kumar, Glenford Mapp, Korhan Cengiz

Intelligent Network Design Driven by Big Data
Analytics, IoT, AI and Cloud Computing
As enterprise access networks evolve with a larger number of mobile users, a wide range of AIn Intelligent Network Design
devices and new cloud-based applications, managing user performance on an end-to-end nt
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are being used to ingest, analyse and correlate a myriad of big data across the entire network
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reliability in intelligent networks, and discuss a range of emerging applications.
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This book will be useful to researchers, scientists, engineers, professionals, advanced students losi Edited by
and faculty members in ICTs, data science, networking, AI, machine learning and sensing. It udgn Sunil Kumar, Glenford Mapp and Korhan Cengiz
will also be of interest to professionals in data science, AI, cloud and IoT start-up companies, C D
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About the Editors tb
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Sunil Kumar is an associate professor of computer science and engineering at Amity gB
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Glenford Mapp is an associate professor at Middlesex University, London, UK. a
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Korhan Cengiz is an assistant professor of electrical and electronics engineering at Trakya a

University, Turkey.
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The Institution of Engineering and Technology n
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theiet.org
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978-1-83953-533-8 z
Intelligent Network Design
Driven by Big Data Analytics,
IoT, AI and Cloud Computing
Other volumes in this series:
Volume 1 Knowledge Discovery and Data Mining M.A. Bramer (Editor)
Volume 3 Troubled IT Projects: Prevention and turnaround J.M. Smith
Volume 4 UML for Systems Engineering: Watching the wheels, 2nd Edition J. Holt
Volume 5 Intelligent Distributed Video Surveillance Systems S.A. Velastin and P. Remagnino (Editors)
Volume 6 Trusted Computing C. Mitchell (Editor)
Volume 7 SysML for Systems Engineering J. Holt and S. Perry
Volume 8 Modelling Enterprise Architectures J. Holt and S. Perry
Volume 9 Model-Based Requirements Engineering J. Holt, S. Perry and M. Bownsword
Volume 13 Trusted Platform Modules: Why, when and how to use them A. Segall
Volume 14 F oundations for Model-based Systems Engineering: From Patterns to Models J. Holt, S. Perry and
M. Bownsword
Volume 15 Big Data and Software Defined Networks J. Taheri (Editor)
Volume 18 Modeling and Simulation of Complex Communication M. A. Niazi (Editor)
Volume 20 SysML for Systems Engineering: A Model-Based Approach, 3rd Edition J. Holt and S. Perry
Volume 22 Virtual Reality and Light Field Immersive Video Technologies for Real-World Applications G. Lafruit
and M. Tehrani
Volume 23 Data as Infrastructure for Smart Cities L. Suzuki and A. Finkelstein
Volume 24 Ultrascale Computing Systems J. Carretero, E. Jeannot and A. Zomaya
Volume 25 Big Data-Enabled Internet of Things M. Khan, S. Khan, A. Zomaya (Editors)
Volume 26 Handbook of Mathematical Models for Languages and Computation A. Meduna, P. Horáček, M. Tomko
Volume 29 Blockchains for Network Security: Principles, technologies and applications H. Huang, L. Wang, Y. Wu,
K. R. Choo (Editors)
Volume 32 Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and
classification Z. Tari, A. Fahad, A. Almalawi and X. Yi
Volume 33 Edge Computing: Models, technologies and applications J.Taheri and S. Deng (Editors)
Volume 34 AI for Emerging Verticals: Human-robot computing, sensing and networking M. Z. Shakir and N. Ramzan
(Editors)
Volume 35 Big Data Recommender Systems Vol 1 & 2 O. Khalid, S. U. Khan, A. Y. Zomaya (Editors)
Volume 37 Handbook of Big Data Analytics Vol 1 & 2 V. Ravi and A. K. Cherukuri (Editors)
Volume 39 ReRAM-based Machine Learning H. Y, L. Ni and S. M. P. Dinakarrao
Volume 40 E-learning Methodologies: Fundamentals, technologies and applications M. Goyal, R. Krishnamurthi and
D. Yadav (Editors)
Volume 44 Streaming Analytics: Concepts, architectures, platforms, use cases and applications P. Raj,
C. Surianarayanan, K. Seerangan and G. Ghinea (Editors)
Volume 44 Streaming Analytics: Concepts, architectures, platforms, use cases and applications P. Raj, A. Kumar,
V. García Díaz and N. Muthuraman (Editors)
Volume 115 Ground Penetrating Radar: Improving sensing and imaging through numerical modelling
X. L. Travassos, M. F. Pantoja and N. Ida
Intelligent Network Design
Driven by Big Data Analytics,
IoT, AI and Cloud Computing
Edited by
Sunil Kumar, Glenford Mapp and Korhan Cengiz
The Institution of Engineering and Technology
Published by The Institution of Engineering and Technology, London, United Kingdom
The Institution of Engineering and Technology is registered as a Charity in England &
Wales (no. 211014) and Scotland (no. SC038698).
© The Institution of Engineering and Technology 2022
First published 2022
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The moral rights of the author to be identified as author of this work have been asserted by
him in accordance with the Copyright, Designs and Patents Act 1988.
British Library Cataloguing in Publication Data
A catalogue record for this product is available from the British Library
ISBN 978-1-83953-533-8 (hardback)
ISBN 978-1-83953-534-5 (PDF)
Typeset in India by Exeter Premedia Services Private Limited
Printed in the UK by CPI Group (UK) Ltd, Croydon
Cover Image: Yuichiro Chino via Getty Images
Contents
About the Editors xv
1 Introduction to intelligent network design driven by big data analytics,
IoT, AI and cloud computing 1
Sunil Kumar , Glenford Mapp , and Korhan Cengiz
Preface 1
Chapter 2: Role of automation, Big Data, AI, ML IBN, and
cloud computing in intelligent networks 2
Chapter 3: An intelligent verification management approach for
efficient VLSI computing system 2
Chapter 4: Evaluation of machine learning algorithms on academic
big dataset by using feature selection techniques 3
Chapter 5: Accurate management and progression of Big Data analysis 4
Chapter 6: Cram on data recovery and backup cloud computing techniques 4
Chapter 7: An adaptive software defined networking (SDN) for load
balancing in cloud computing 5
Chapter 8: Emerging security challenges in cloud computing: An insight 5
Chapter 9: Factors responsible and phases of speaker recognition system 6
Chapter 10: IoT-based water quality assessment using fuzzy logic controller 6
Chapter 11: Design and analysis of wireless sensor network for
intelligent transportation and industry automation 7
Chapter 12: A review of edge computing in healthcare Internet of
Things: theories, practices, and challenges 7
Chapter 13: Image processing for medical images on the basis of
intelligence and bio computing 8
Chapter 14: IoT-based architecture for smart health-care systems 8
Chapter 15: IoT-based heart disease prediction system 9
Chapter 16: DIAIF: detection of interest flooding using artificial
intelligence-based framework in NDN android 9
Chapter 17: Intelligent and cost-effective mechanism for
monitoring road quality using machine learning 10
References 10
vi Intelligent network design driven by Big Data analytics
2 Role of automation, Big Data, AI, ML IBN, and cloud computing in
intelligent networks 13
Sunil Kumar and Priya Ranjan
2.1 Evolution of networks: everything is connected 13
2.1.1 Intelligent devices 14
2.1.2 Intelligent devices connection with networks 14
2.2 Huge volume of data generation by intelligent devices 15
2.2.1 Issues and challenges of Big Data Analytics 16
2.2.2 Storage of Big Data 16
2.3 Need of data analysis by business 18
2.3.1 Sources of information 19
2.3.2 Data visualization 20
2.3.3 Analyzing Big Data for effective use of business 20
2.3.4 Intelligent devices thinking intelligently 20
2.4 Artificial intelligence and machine learning in networking 21
2.4.1 Role of ML in networks 21
2.5 Intent-based networking 22
2.6 Role of programming 23
2.6.1 Basic programming using Blockly 23
2.6.2 Blockly games 24
2.7 Role of technology to design a model 24
2.7.1 Electronic toolkits 25
2.7.2 Programming resources 26
2.8 Relation of AI, ML, and IBN 26
2.9 Business challenges and opportunities 26
2.9.1 The evolving job market 27
2.10 Security 28
2.10.1 Challenges to secure device and networks 28
2.11 Summary 31
References 31
3 An intelligent verification management approach for efficient VLSI
computing system 35
Konasagar Achyut , Swati K Kulkarni , Akshata A Raut , Siba Kumar Panda ,
and Lakshmi Nair
3.1 Introduction 36
3.2 Literature study 38
3.3 Verification management approach: Case Study 1 46
3.3.1 The pseudo random number generator in a verification
environment 47
3.3.2 Implementation of PRNG in higher abstraction language and
usage of DPI 49
3.4 Verification management approach: Case Study 2 51
Contents vii
3.5 Challenges and research direction 54
3.5.1 Challenges in intelligent systems 54
3.6 Conclusion 55
References 55
4 Evaluation of machine learning algorithms on academic big dataset by
using feature selection techniques 61
Mukesh Kumar , Amar Jeet Singh , Bhisham Sharma , and Korhan Cengiz
4.1 Introduction 62
4.1.1 EDM 64
4.1.2 EDM process 65
4.1.3 Methods and techniques 66
4.1.4 Application areas of data mining 68
4.2 Literature survey 69
4.3 Materials and methods 72
4.3.1 Dataset description 72
4.3.2 Classification algorithms 73
4.3.3 FS algorithms 75
4.3.4 Data preprocessing phase 77
4.4 Implementation of the proposed algorithms 78
4.4.1 Model construction for the standard classifier 78
4.4.2 Implementation after attribute selection using ranker method 79
4.5 Result analysis and discussion 84
4.6 Conclusion 86
References 86
5 Accurate management and progression of Big Data Analysis 93
Tanmay Sinha, Narsepalli Pradyumna , and K B Sowmya
5.1 Introduction 93
5.1.1 Examples of Big Data 94
5.2 Big Data Analysis 96
5.2.1 Life cycle of Big Data 96
5.2.2 Classification of the Big Data 97
5.2.3 Working of Big Data Analysis 99
5.2.4 Common flaws that undermine Big Data Analysis 100
5.2.5 Advantages of Big Data Analysis 100
5.3. Processing techniques 101
5.3.1 Traditional method 101
5.3.2 MapReduce 102
5.3.3 Advantages of MapReduce 103
5.4 Cyber crime 105
5.4.1 Different strategies in Big Data to help in various
circumstances 106
viii Intelligent network design driven by Big Data analytics
5.4.2 Big Data Analytics and cybercrime 107
5.4.3 Security issues associated with Big Data 108
5.4.4 Big Data Analytics in digital forensics 108
5.5 Real-time edge analytics for Big Data in IoT 109
5.6 Conclusion 111
References 112
6 Cram on data recovery and backup cloud computing techniques 115
Dharanyadevi P , Julie Therese M , Senthilnayaki B ,
Devi A , and Venkatalakshmi K
6.1 Introduction 115
6.1.1 Origin of cloud 116
6.1.2 Sole features of cloud computing 116
6.1.3 Advantages of cloud computing 116
6.1.4 Disadvantages of cloud computing 117
6.2 Classification of data recovery and backup 118
6.2.1 Recovery 118
6.2.2 Backup 119
6.3 Study on data recovery and backup cloud computing techniques 120
6.3.1 Backup of real-time data and recovery using cloud computing 120
6.3.2 Data recovery and security in cloud 122
6.3.3 Amoeba: An autonomous backup and recovery solid-state
drives for ransomware attack defense 123
6.3.4 A cloud-based automatic recovery and backup system for
video compression 124
6.3.5 Efficient and reliable data recovery techniques in
cloud computing 126
6.3.6 Cost-efficient remote backup services for enterprise cloud 127
6.3.7 DR-cloud: Multi-cloud-based disaster recovery service 128
6.4 Conclusion 131
References 131
7 An adaptive software- defined networking (SDN) for load balancing in
cloud computing 135
Swati Lipsa , Ranjan Kumar Dash , and Korhan Cengiz
7.1 Introduction 135
7.2 Related works 138
7.3 Architecture overview of SDN 140
7.4 Load-balancing framework in SDN 141
7.4.1 Classification of SDN controller architectures 142
7.5 Problem statement 144
7.5.1 Selection strategy of controller head 144
7.5.2 Network setup 146
7.6 Illustration 147
Contents ix
7.7 Results and discussion 150
7.7.1 Comparison of throughput 150
7.7.2 Comparison of PTR 151
7.7.3 Comparison of number of packet loss 152
7.8 Conclusion 152
References 153
8 Emerging security challenges in cloud computing: an insight 159
Gaurav Aggarwal , Kavita Jhajharia , Dinesh Kumar Saini , and Mehak Khurana
8.1 Introduction 159
8.1.1 An introduction to cloud computing and its security 159
8.2 The security issues in different cloud models and threat management
techniques 161
8.2.1 Five most indispensable characteristics 161
8.2.2 The security issues in cloud service model 162
8.2.3 Security issues in cloud deployment models 165
8.2.4 Security challenges in the cloud environment 166
8.2.5 The threat management techniques 169
8.3 Review on existing proposed models 174
8.3.1 SeDaSC 174
8.3.2 The ‘SecCloud’ protocol 176
8.3.3 Data accountability and auditing for secure cloud data storage 179
8.4 Conclusion and future prospectives 180
References 180
9 Factors responsible and phases of speaker recognition system 185
Hunny and Ayush Goyal
9.1 Study of related research 185
9.2 Phases of speaker recognition system 187
9.2.1 Speaker database collection 187
9.2.2 Feature extraction 192
9.2.3 Feature mapping 195
9.3 Basics of speech signals 197
9.3.1 Speech production system 197
9.3.2 Speech perception 198
9.3.3 Speech signals 198
9.3.4 Properties of the sinusoids 198
9.3.5 Windowing signals 199
9.3.6 Zero-crossing rate 199
9.3.7 Autocorrelation 200
9.4 Features of speech signals 200
9.4.1 Physical features 200
9.4.2 Perceptual features 202
9.4.3 Signal features 202