Preface xxi Part 1: Internet of Things 1 1 Voyage of Internet of Things in the Ocean of Technology 3Tejaskumar R. Ghadiyali, Bharat C. Patel and Manish M. Kayasth 1.1 Introduction 3 1.1.1 Characteristics of IoT 4 1.1.2 IoT Architecture 5 1.1.3 Merits and Demerits of IoT 6 1.2 Technological Evolution Toward IoT 7 1.3 IoT-Associated Technology 8 1.4 Interoperability in IoT 14 1.5 Programming Technologies in IoT 15 1.5.1 Arduino 15 1.5.2 Raspberry Pi 17 1.5.3 Python 18 1.6 IoT Applications 19 Conclusion 22 References 22 2 AI for Wireless Network Optimization: Challenges and Opportunities 25Murad Abusubaih 2.1 Introduction to AI 25 2.2 Self-Organizing Networks 27 2.2.1 Operation Principle of Self-Organizing Networks 27 2.2.2 Self-Configuration 28 2.2.3 Self-Optimization 28 2.2.4 Self-Healing 28 2.2.5 Key Performance Indicators 29 2.2.6 SON Functions 29 2.3 Cognitive Networks 29 2.4 Introduction to Machine Learning 30 2.4.1 ML Types 31 2.4.2 Components of ML Algorithms 31 2.4.3 How do Machines Learn? 32 2.4.3.1 Supervised Learning 32 2.4.3.2 Unsupervised Learning 33 2.4.3.3 Semi-Supervised Learning 35 2.4.3.4 Reinforcement Learning 35 2.4.4 ML and Wireless Networks 36 2.5 Software-Defined Networks 36 2.5.1 SDN Architecture 37 2.5.2 The OpenFlow Protocol 38 2.5.3 SDN and ML 39 2.6 Cognitive Radio Networks 39 2.6.1 Sensing Methods 41 2.7 ML for Wireless Networks: Challenges and Solution Approaches 41 2.7.1 Cellular Networks 42 2.7.1.1 Energy Saving 42 2.7.1.2 Channel Access and Assignment 42 2.7.1.3 User Association and Load Balancing 43 2.7.1.4 Traffic Engineering 44 2.7.1.5 QoS/QoE Prediction 45 2.7.1.6 Security 45 2.7.2 Wireless Local Area Networks 46 2.7.2.1 Access Point Selection 47 2.7.2.2 Interference Mitigation 48 2.7.2.3 Channel Allocation and Channel Bonding 49 2.7.2.4 Latency Estimation and Frame Length Selection 49 2.7.2.5 Handover 49 2.7.3 Cognitive Radio Networks 50 References 50 3 An Overview on Internet of Things (IoT) Segments and Technologies 57Amarjit Singh 3.1 Introduction 57 3.2 Features of IoT 59 3.3 IoT Sensor Devices 59 3.4 IoT Architecture 61 3.5 Challenges and Issues in IoT 62 3.6 Future Opportunities in IoT 63 3.7 Discussion 64 3.8 Conclusion 65 References 65 4 The Technological Shift: AI in Big Data and IoT 69Deepti Sharma, Amandeep Singh and Sanyam Singhal 4.1 Introduction 69 4.2 Artificial Intelligence 71 4.2.1 Machine Learning 71 4.2.2 Further Development in the Domain of Artificial Intelligence 73i 4.2.3 Programming Languages for Artificial Intelligence 74 4.2.4 Outcomes of Artificial Intelligence 74 4.3 Big Data 75 4.3.1 Artificial Intelligence Methods for Big Data 77 4.3.2 Industry Perspective of Big Data 77 4.3.2.1 In Medical Field 78 4.3.2.2 In Meteorological Department 78 4.3.2.3 In Industrial/Corporate Applications and Analytics 79 4.3.2.4 In Education 79 4.3.2.5 In Astronomy 79 4.4 Internet of Things 80 4.4.1 Interconnection of IoT With AoT 81 4.4.2 Difference Between IIoT and IoT 81 4.4.3 Industrial Approach for IoT 82 4.5 Technical Shift in AI, Big Data, and IoT 82 4.5.1 Industries Shifting to AI-Enabled Big Data Analytics 83 4.5.2 Industries Shifting to AI-Powered IoT Devices 84 4.5.3 Statistical Data of These Shifts 84 4.6 Conclusion 85 References 86 5 IoT’s Data Processing Using Spark 91Ankita Bansal and Aditya Atri 5.1 Introduction 91 5.2 Introduction to Apache Spark 92 5.2.1 Advantages of Apache Spark 93 5.2.2 Apache Spark’s Components 93 5.3 Apache Hadoop MapReduce 94 5.3.1 Limitations of MapReduce 94 5.4 Resilient Distributed Dataset (RDD) 95 5.4.1 Features and Limitations of RDDs 95 5.5 DataFrames 96 5.6 Datasets 97 5.7 Introduction to Spark SQL 98 5.7.1 Spark SQL Architecture 99 5.7.2 Spark SQL Libraries 100 5.8 SQL Context Class in Spark 100 5.9 Creating Dataframes 101 5.9.1 Operations on DataFrames 102 5.10 Aggregations 103 5.11 Running SQL Queries on Dataframes 103 5.12 Integration With RDDs 104 5.12.1 Inferring the Schema Using Reflection 104 5.12.2 Specifying the Schema Programmatically 104 5.13 Data Sources 104 5.13.1 JSON Datasets 105 5.13.2 Hive Tables 105 5.13.3 Parquet Files 106 5.14 Operations on Data Sources 106 5.15 Industrial Applications 107 5.16 Conclusion 108 References 108 6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 111Tayyab Khan and Karan Singh 6.1 Introduction 111 6.1.1 Components of WSNs 113 6.1.2 Trust 115 6.1.3 Major Contribution 120 6.2 Related Work 121 6.3 Network Topology and Assumptions 122 6.4 Proposed Trust Model 122 6.4.1 CM to CM (Direct) Trust Evaluation Scheme 123 6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx,y(∆t)) 124 6.4.3 CH-to-CH Direct Trust Estimation 125 6.4.4 BS-to-CH Feedback Trust Calculation 125 6.5 Result and Analysis 126 6.5.1 Severity Analysis 126 6.5.2 Malicious Node Detection 127 6.6 Conclusion and Future Work 128 References 128 7 Smart Applications of IoT 131Pradeep Kamboj, T. Ratha Jeyalakshmi, P. Thillai Arasu, S. Balamurali and A. Murugan 7.1 Introduction 131 7.2 Background 132 7.2.1 Enabling Technologies for Building Intelligent Infrastructure 132 7.3 Smart City 136 7.3.1 Benefits of a Smart City 137 7.3.2 Smart City Ecosystem 137 7.3.3 Challenges in Smart Cities 138 7.4 Smart Healthcare 139 7.4.1 Smart Healthcare Applications 140 7.4.2 Challenges in Healthcare 141 7.5 Smart Agriculture 142 7.5.1 Environment Agriculture Controlling 143 7.5.2 Advantages 143 7.5.3 Challenges 144 7.6 Smart Industries 145 7.6.1 Advantages 147 7.6.2 Challenges 148 7.7 Future Research Directions 149 7.8 Conclusions 149 References 149 8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 153Rohit Rastogi, Krishna Vir Singh, Mihir Rai, Kartik Sachdeva, Tarun Yadav and Harshit Gupta 8.1 Introduction 153 8.1.1 Technology in Agriculture 154 8.1.2 Use and Need for Low-Cost Technology in Agriculture 154 8.2 Proposed System 154 8.3 Flow Chart 157 8.4 Use Case 158 8.5 System Modules 158 8.5.1 Raspberry Pi 158 8.5.2 Arduino Uno 158 8.5.3 DHT 11 Humidity and Temperature Sensor 158 8.5.4 Soil Moisture Sensor 160 8.5.5 Solenoid Valve 160 8.5.6 Drip Irrigation Kit 160 8.5.7 433 MHz RF Module 160 8.5.8 Mobile Application 160 8.5.9 Testing Phase 161 8.6 Limitations 162 8.7 Suggestions 162 8.8 Future Scope 162 8.9 Conclusion 163 Acknowledgement 163 References 163 Suggested Additional Readings 164 Key Terms and Definitions 164 Appendix 165 Example Code 166 9 Artificial Intelligence: An Imaginary World of Machine 167Bharat C. Patel, Manish M. Kaysth and Tejaskumar R. Ghadiyali 9.1 The Dawn of Artificial Intelligence 167 9.2 Introduction 169 9.3 Components of AI 170 9.3.1 Machine Reasoning 170 9.3.2 Natural Language Processing 171 9.3.3 Automated Planning 171 9.3.4 Machine Learning 171 9.4 Types of Artificial Intelligence 172 9.4.1 Artificial Narrow Intelligence 172 9.4.2 Artificial General Intelligence 173 9.4.3 Artificial Super Intelligence 174 9.5 Application Area of AI 175 9.6 Challenges in Artificial Intelligence 176 9.7 Future Trends in Artificial Intelligence 177 9.8 Practical Implementation of AI Application 179 References 182 10 Impact of Deep Learning Techniques in IoT 185M. Chandra Vadhana, P. Shanthi Bala and Immanuel Zion Ramdinthara 10.1 Introduction 185 10.2 Internet of Things 186 10.2.1 Characteristics of IoT 187 10.2.2 Architecture of IoT 187 10.2.2.1 Smart Device/Sensor Layer 187 10.2.2.2 Gateways and Networks 187 10.2.2.3 Management Service Layer 188 10.2.2.4 Application Layer 188 10.2.2.5 Interoperability of IoT 188 10.2.2.6 Security Requirements at a Different Layer of IoT 190 10.2.2.7 Future Challenges for IoT 190 10.2.2.8 Privacy and Security 190 10.2.2.9 Cost and Usability 191 10.2.2.10 Data Management 191 10.2.2.11 Energy Preservation 191 10.2.2.12 Applications of IoT 191 10.2.2.13 Essential IoT Technologies 193 10.2.2.14 Enriching the Customer Value 195 10.2.2.15 Evolution of the Foundational IoT Technologies 196 10.2.2.16 Technical Challenges in the IoT Environment 196 10.2.2.17 Security Challenge 197 10.2.2.18 Chaos Challenge 197 10.2.2.19 Advantages of IoT 198 10.2.2.20 Disadvantages of IoT 198 10.3 Deep Learning 198 10.3.1 Models of Deep Learning 199 10.3.1.1 Convolutional Neural Network 199 10.3.1.2 Recurrent Neural Networks 199 10.3.1.3 Long Short-Term Memory 200 10.3.1.4 Autoencoders 200 10.3.1.5 Variational Autoencoders 201 10.3.1.6 Generative Adversarial Networks 201 10.3.1.7 Restricted Boltzmann Machine 201 10.3.1.8 Deep Belief Network 201 10.3.1.9 Ladder Networks 202 10.3.2 Applications of Deep Learning 202 10.3.2.1 Industrial Robotics 202 10.3.2.2 E-Commerce Industries 202 10.3.2.3 Self-Driving Cars 202 10.3.2.4 Voice-Activated Assistants 202 10.3.2.5 Automatic Machine Translation 202 10.3.2.6 Automatic Handwriting Translation 203 10.3.2.7 Predicting Earthquakes 203 10.3.2.8 Object Classification in Photographs 203 10.3.2.9 Automatic Game Playing 203 10.3.2.10 Adding Sound to Silent Movies 203 10.3.3 Advantages of Deep Learning 203 10.3.4 Disadvantages of Deep Learning 203 10.3.5 Deployment of Deep Learning in IoT 203 10.3.6 Deep Learning Applications in IoT 204 10.3.6.1 Image Recognition 204 10.3.6.2 Speech/Voice Recognition 204 10.3.6.3 Indoor Localization 204 10.3.6.4 Physiological and Psychological Detection 205 10.3.6.5 Security and Privacy 205 10.3.7 Deep Learning Techniques on IoT Devices 205 10.3.7.1 Network Compression 205 10.3.7.2 Approximate Computing 206 10.3.7.3 Accelerators 206 10.3.7.4 Tiny Motes 206 10.4 IoT Challenges on Deep Learning and Future Directions 206 10.4.1 Lack of IoT Dataset 206 10.4.2 Pre-Processing 207 10.4.3 Challenges of 6V’s 207 10.4.4 Deep Learning Limitations 207 10.5 Future Directions of Deep Learning 207 10.5.1 IoT Mobile Data 207 10.5.2 Integrating Contextual Information 208 10.5.3 Online Resource Provisioning for IoT Analytics 208 10.5.4 Semi-Supervised Analytic Framework 208 10.5.5 Dependable and Reliable IoT Analytics 208 10.5.6 Self-Organizing Communication Networks 208 10.5.7 Emerging IoT Applications 208 10.5.7.1 Unmanned Aerial Vehicles 209 10.5.7.2 Virtual/Augmented Reality 209 10.5.7.3 Mobile Robotics 209 10.6 Common Datasets for Deep Learning in IoT 209 10.7 Discussion 209 10.8 Conclusion 211 References 211 Part 2: Artificial Intelligence in Healthcare 215 11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 217Toufique A. Soomro, Ahmed J. Afifi, Pardeep Kumar, Muhammad Usman Keerio, Saleem Ahmed and Ahmed Ali 11.1 Introduction 217 11.2 Existing Methods Review 221 11.3 Methodology 223 11.3.1 Architecture of Stride U-Net 223 11.3.2 Loss Function 225 11.4 Databases and Evaluation Metrics 225 11.4.1 CNN Implementation Details 226 11.5 Results and Analysis 227 11.5.1 Evaluation on DRIVE and STARE Databases 227 11.5.2 Comparative Analysis 227 11.6 Concluding Remarks 229 References 230 12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 235Muhammad Ali Nizamani, Muhammad Ali Memon and Pirah Brohi 12.1 Introduction 235 12.2 Methodology 237 12.3 IoT in Mental Health 238 12.4 Mental Healthcare Applications and Services Based on IoT 238 12.5 Benefits of IoT in Mental Health 241 12.5.1 Reduction in Treatment Cost 241 12.5.2 Reduce Human Error 241 12.5.3 Remove Geographical Barriers 241 12.5.4 Less Paperwork and Documentation 241 12.5.5 Early Stage Detection of Chronic Disorders 241 12.5.6 Improved Drug Management 242 12.5.7 Speedy Medical Attention 242 12.5.8 Reliable Results of Treatment 242 12.6 Challenges in IoT-Based Mental Healthcare Applications 242 12.6.1 Scalability 242 12.6.2 Trust 242 12.6.3 Security and Privacy Issues 243 12.6.4 Interoperability Issues 243 12.6.5 Computational Limits 243 12.6.6 Memory Limitations 243 12.6.7 Communications Media 244 12.6.8 Devices Multiplicity 244 12.6.9 Standardization 244 12.6.10 IoT-Based Healthcare Platforms 244 12.6.11 Network Type 244 12.6.12 Quality of Service 245 12.7 Blockchain in IoT for Healthcare 245 12.8 Results and Discussion 246 12.9 Limitations of the Survey 247 12.10 Conclusion 247 References 247 13 Monitoring Technologies for Precision Health 251Rehab A. Rayan and Imran Zafar 13.1 Introduction 251 13.2 Applications of Monitoring Technologies 252 13.2.1 Everyday Life Activities 253 13.2.2 Sleeping and Stress 253 13.2.3 Breathing Patterns and Respiration 254 13.2.4 Energy and Caloric Consumption 254 13.2.5 Diabetes, Cardiac, and Cognitive Care 254 13.2.6 Disability and Rehabilitation 254 13.2.7 Pregnancy and Post-Procedural Care 255 13.3 Limitations 255 13.3.1 Quality of Data and Reliability 255 13.3.2 Safety, Privacy, and Legal Concerns 256 13.4 Future Insights 256 13.4.1 Consolidating Frameworks 256 13.4.2 Monitoring and Intervention 256 13.4.3 Research and Development 257 13.5 Conclusions 257 References 257 14 Impact of Artificial Intelligence in Cardiovascular Disease 261Mir Khan, Saleem Ahmed, Pardeep Kumar and Dost Muhammad Saqib Bhatti 14.1 Artificial Intelligence 261 14.2 Machine Learning 262 14.3 The Application of AI in CVD 263 14.3.1 Precision Medicine 263 14.3.2 Clinical Prediction 263 14.3.3 Cardiac Imaging Analysis 264 14.4 Future Prospect 264 14.5 PUAI and Novel Medical Mode 265 14.5.1 Phenomenon of PUAI 265 14.5.2 Novel Medical Model 266 14.6 Traditional Mode 266 14.6.1 Novel Medical Mode Plus PUAI 266 14.7 Representative Calculations of AI 268 14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis 268 References 270 15 Healthcare Transformation With Clinical Big Data Predictive Analytics 273Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Sumera Naz Pathan and Asia Khatoon Soomro 15.1 Introduction 273 15.1.1 Big Data in Health Sector 275 15.1.2 Data Structure Produced in Health Sectors 275 15.2 Big Data Challenges in Healthcare 276 15.2.1 Big Data in Computational Healthcare 276 15.2.2 Big Data Predictive Analytics in Healthcare 276 15.2.3 Big Data for Adapted Healthcare 277 15.3 Cloud Computing and Big Data in Healthcare 278 15.4 Big Data Healthcare and IoT 278 15.5 Wearable Devices for Patient Health Monitoring 282 15.6 Big Data and Industry 4.0 283 15.7 Conclusion 283 References 284 16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats 287Rohit Rastogi, Mamta Saxena, D.K. Chaturvedi, Mayank Gupta, Mukund Rastogi, Prajwal Srivatava, Mohit Jain, Pradeep Kumar, Ujjawal Sharma, Rohan Choudhary and Neha Gupta 16.1 Introduction 287 16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate 287 16.1.2 Precautionary Guidelines Followed in Indian Continent 288 16.1.3 Spiritual Guidelines in Indian Society 289 16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India 289 16.1.4 Veda Vigyaan: Ancient Vedic Knowledge 289 16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon 289 16.1.6 The Yagya Samagri 290 16.2 Literature Survey 290 16.2.1 Technical Aspects of Yajna and Mantra Therapy 290 16.2.2 Mantra Chanting and Its Science 290 16.2.3 Yagya Medicine (Yagyopathy) 290 16.2.4 The Medicinal HavanSamagri Components 291 16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases 291 16.2.5 Scientific Benefits of Havan 291 16.3 Experimental Setup Protocols With Results 292 16.3.1 Subject Sample Distribution 295 16.3.1.1 Area Wise Distribution 295 16.3.2 Conclusion and Discussion Through Experimental Work 295 16.4 Future Scope and Limitations 297 16.5 Novelty 298 16.6 Recommendations 298 16.7 Applications of Yajna Therapy 299 16.8 Conclusions 299 Acknowledgement 299 References 299 Key Terms and Definitions 304 17 Extraction of Depression Symptoms From Social Networks 307Bhavna Chilwal and Amit Kumar Mishra 17.1 Introduction 307 17.1.1 Diagnosis and Treatments 309 17.2 Data Mining in Healthcare 310 17.2.1 Text Mining 310 17.3 Social Network Sites 311 17.4 Symptom Extraction Tool 312 17.4.1 Data Collection 313 17.4.2 Data Processing 313 17.4.3 Data Analysis 314 17.5 Sentiment Analysis 316 17.5.1 Emotion Analysis 318 17.5.2 Behavioral Analysis 318 17.6 Conclusion 319 References 320 Part 3: Cybersecurity 323 18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 325C. Kaviyazhiny, P. Shanthi Bala and A.S. Gowri 18.1 Introduction 325 18.2 Characteristics of Fog Computing 326 18.3 Reference Architecture of Fog Computing 328 18.4 CISCO IOx Framework 329 18.5 Security Practices in CISCO IOx 330 18.5.1 Potential Attacks on IoT Architecture 330 18.5.2 Perception Layer (Sensing) 331 18.5.3 Network Layer 331 18.5.4 Service Layer (Support) 332 18.5.5 Application Layer (Interface) 333 18.6 Security Issues in Fog Computing 333 18.6.1 Virtualization Issues 333 18.6.2 Web Security Issues 334 18.6.3 Internal/External Communication Issues 335 18.6.4 Data Security Related Issues 336 18.6.5 Wireless Security Issues 337 18.6.6 Malware Protection 338 18.7 Machine Learning for Secure Fog Computing 338 18.7.1 Layer 1 Cloud 339 18.7.2 Layer 2 Fog Nodes For The Community 340 18.7.3 Layer 3 Fog Node for Their Neighborhood 340 18.7.4 Layer 4 Sensors 341 18.8 Existing Security Solution in Fog Computing 341 18.8.1 Privacy-Preserving in Fog Computing 341 18.8.2 Pseudocode for Privacy Preserving in Fog Computing 342 18.8.3 Pseudocode for Feature Extraction 343 18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature 343 18.8.5 Pseudocode for Encrypting Data 344 18.8.6 Pseudocode for Data Partitioning 344 18.8.7 Encryption Algorithms in Fog Computing 345 18.9 Recommendation and Future Enhancement 345 18.9.1 Data Encryption 345 18.9.2 Preventing from Cache Attacks 346 18.9.3 Network Monitoring 346 18.9.4 Malware Protection 347 18.9.5 Wireless Security 347 18.9.6 Secured Vehicular Network 347 18.9.7 Secure Multi-Tenancy 348 18.9.8 Backup and Recovery 348 18.9.9 Security with Performance 348 18.10 Conclusion 349 References 349 19 Cybersecurity and Privacy Fundamentals 353Ravi Verma 19.1 Introduction 353 19.2 Historical Background and Evolution of Cyber Crime 354 19.3 Introduction to Cybersecurity 355 19.3.1 Application Security 356 19.3.2 Information Security 356 19.3.3 Recovery From Failure or Disaster 356 19.3.4 Network Security 357 19.4 Classification of Cyber Crimes 357 19.4.1 Internal Attacks 357 19.4.2 External Attacks 358 19.4.3 Unstructured Attack 358 19.4.4 Structured Attack 358 19.5 Reasons Behind Cyber Crime 358 19.5.1 Making Money 359 19.5.2 Gaining Financial Growth and Reputation 359 19.5.3 Revenge 359 19.5.4 For Making Fun 359 19.5.5 To Recognize 359 19.5.6 Business Analysis and Decision Making 359 19.6 Various Types of Cyber Crime 359 19.6.1 Cyber Stalking 360 19.6.2 Sexual Harassment or Child Pornography 360 19.6.3 Forgery 360 19.6.4 Crime Related to Privacy of Software and Network Resources 360 19.6.5 Cyber Terrorism 360 19.6.6 Phishing, Vishing, and Smishing 360 19.6.7 Malfunction 361 19.6.8 Server Hacking 361 19.6.9 Spreading Virus 361 19.6.10 Spamming, Cross Site Scripting, and Web Jacking 361 19.7 Various Types of Cyber Attacks in Information Security 361 19.7.1 Web-Based Attacks in Information Security 362 19.7.2 System-Based Attacks in Information Security 364 19.8 Cybersecurity and Privacy Techniques 365 19.8.1 Authentication and Authorization 365 19.8.2 Cryptography 366 19.8.2.1 Symmetric Key Encryption 367 19.8.2.2 Asymmetric Key Encryption 367 19.8.3 Installation of Antivirus 367 19.8.4 Digital Signature 367 19.8.5 Firewall 369 19.8.6 Steganography 369 19.9 Essential Elements of Cybersecurity 370 19.10 Basic Security Concerns for Cybersecurity 371 19.10.1 Precaution 372 19.10.2 Maintenance 372 19.10.3 Reactions 373 19.11 Cybersecurity Layered Stack 373 19.12 Basic Security and Privacy Check List 374 19.13 Future Challenges of Cybersecurity 374 References 376 20 Changing the Conventional Banking System through Blockchain 379Khushboo Tripathi, Neha Bhateja and Ashish Dhillon 20.1 Introduction 379 20.1.1 Introduction to Blockchain 379 20.1.2 Classification of Blockchains 381 20.1.2.1 Public Blockchain 381 20.1.2.2 Private Blockchain 382 20.1.2.3 Hybrid Blockchain 382 20.1.2.4 Consortium Blockchain 382 20.1.3 Need for Blockchain Technology 383 20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary 383 20.1.4 Comparison of Blockchain and Cryptocurrency 384 20.1.4.1 Distributed Ledger Technology (DLT) 384 20.1.5 Types of Consensus Mechanism 385 20.1.5.1 Consensus Algorithm: A Quick Background 385 20.1.6 Proof of Work 386 20.1.7 Proof of Stake 387 20.1.7.1 Delegated Proof of Stake 387 20.1.7.2 Byzantine Fault Tolerance 388 20.2 Literature Survey 388 20.2.1 The History of Blockchain Technology 388 20.2.2 Early Years of Blockchain Technology: 1991–2008 389 20.2.2.1 Evolution of Blockchain: Phase 1—Transactions 389 20.2.2.2 Evolution of Blockchain: Phase 2—Contracts 390 20.2.2.3 Evolution of Blockchain: Phase 3—Applications 390 20.2.3 Literature Review 391 20.2.4 Analysis 392 20.3 Methodology and Tools 392 20.3.1 Methodology 392 20.3.2 Flow Chart 393 20.3.3 Tools and Configuration 394 20.4 Experiment 394 20.4.1 Steps of Implementation 394 20.4.2 Screenshots of Experiment 397 20.5 Results 398 20.6 Conclusion 400 20.7 Future Scope 401 20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises 401 References 402 21 A Secured Online Voting System by Using Blockchain as the Medium 405Leslie Mark, Vasaki Ponnusamy, Arya Wicaksana, Basilius Bias Christyono and Moeljono Widjaja 21.1 Blockchain-Based Online Voting System 405 21.1.1 Introduction 405 21.1.2 Structure of a Block in a Blockchain System 406 21.1.3 Function of Segments in a Block of the Blockchain 406 21.1.4 SHA-256 Hashing on the Blockchain 407 21.1.5 Interaction Involved in Blockchain-Based Online Voting System 409 21.1.6 Online Voting System Using Blockchain – Framework 409 21.2 Literature Review 410 21.2.1 Literature Review Outline 410 21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model 410 21.2.1.2 Online Voting System Based on Visual Cryptography 411 21.2.1.3 Online Voting System Using Biometric Security and Steganography 412 21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption 414 21.2.1.5 An Online Voting System Based on a Secured Blockchain 416 21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach 417 21.2.1.7 Online Voting System Using Iris Recognition 418 21.2.1.8 Online Voting System Based on NID and SIM 420 21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography 422 21.2.1.10 Online Voting System Using Secret Sharing–Based Authentication 425 21.2.2 Comparing the Existing Online Voting System 427 References 430 22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 431Abhinav Juneja, Sapna Juneja, Vikram Bali, Vishal Jain and Hemant Upadhyay 22.1 Introduction 431 22.2 Literature Review 432 22.3 Different Variants of Cybersecurity in Action 432 22.4 Importance of Cybersecurity in Action 433 22.5 Methods for Establishing a Strategy for Cybersecurity 434 22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity 434 22.7 Where AI Is Actually Required to Deal With Cybersecurity 437 22.8 Challenges for Cybersecurity in Current State of Practice 438 22.9 Conclusion 438 References 438 Index 443

As the entire ecosystem is moving towards a sustainable goal, technology driven smart cyber system is the enabling factor to make this a success, and the current book documents how this can be attained. The cyber ecosystem consists of a huge number of different entities that work and interact with each other in a highly diversified manner. In this era, when the world is surrounded by many unseen challenges and when its population is increasing and resources are decreasing, scientists, researchers, academicians, industrialists, government agencies and other stakeholders are looking toward smart and intelligent cyber systems that can guarantee sustainable development for a better and healthier ecosystem. The main actors of this cyber ecosystem include the Internet of Things (IoT), artificial intelligence (AI), and the mechanisms providing cybersecurity. This book attempts to collect and publish innovative ideas, emerging trends, implementation experiences, and pertinent user cases for the purpose of serving mankind and societies with sustainable societal development. The 22 chapters of the book are divided into three sections: Section I deals with the Internet of Things, Section II focuses on artificial intelligence and especially its applications in healthcare, whereas Section III investigates the different cyber security mechanisms. Audience This book will attract researchers and graduate students working in the areas of artificial intelligence, blockchain, Internet of Things, information technology, as well as industrialists, practitioners, technology developers, entrepreneurs, and professionals who are interested in exploring, designing and implementing these technologies.