The Smart Cyber Ecosystem for Sustainable Development: Principles, Building Blocks, and Paradigms

Description

The Cyber Ecosystem can be a replica of our natural ecosystem where different living and non-living things interact with each other to perform specific tasks. Similarly, the different entities of the cyber ecosystem collaborate digitally with each other to revolutionize our lifestyle by creating smart, intelligent, and automated systems/processes. The main actors of the cyber ecosystem, among others, are the Internet of Things (IoT), Artificial Intelligence (AI), and the mechanisms providing cybersecurity. This book documents how this blend of technologies is powering a digital sustainable socio-economic infrastructure which improves our life quality. It offers advanced automation methods fitted with amended business and audits models, universal authentication schemes, transparent governance, and inventive prediction analysis. Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface Part 1: INTERNET OF THINGS 1 Voyage of Internet of Things in the Ocean of Technology 1.1 Introduction 1.1.1 Characteristics of IoT 1.1.2 IoT Architecture 1.1.3 Merits and Demerits of IoT 1.2 Technological Evolution Toward IoT 1.3 IoT-Associated Technology 1.4 Interoperability in IoT 1.5 Programming Technologies in IoT 1.5.1 Arduino 1.5.2 Raspberry Pi 1.5.3 Python 1.6 IoT Applications Conclusion References 2 AI for Wireless Network Optimization: Challenges and Opportunities 2.1 Introduction to AI 2.2 Self-Organizing Networks 2.2.1 Operation Principle of Self-Organizing Networks 2.2.2 Self-Configuration 2.2.3 Self-Optimization 2.2.4 Self-Healing 2.2.5 Key Performance Indicators 2.2.6 SON Functions 2.3 Cognitive Networks 2.4 Introduction to Machine Learning 2.4.1 ML Types 2.4.2 Components of ML Algorithms 2.4.3 How do Machines Learn? 2.4.4 ML and Wireless Networks 2.5 Software-Defined Networks 2.5.1 SDN Architecture 2.5.2 The OpenFlow Protocol 2.5.3 SDN and ML 2.6 Cognitive Radio Networks 2.6.1 Sensing Methods 2.7 ML for Wireless Networks: Challenges and Solution Approaches 2.7.1 Cellular Networks 2.7.2 Wireless Local Area Networks 2.7.3 Cognitive Radio Networks References 3 An Overview on Internet of Things (IoT) Segments and Technologies 3.1 Introduction 3.2 Features of IoT 3.3 IoT Sensor Devices 3.4 IoT Architecture 3.5 Challenges and Issues in IoT 3.6 Future Opportunities in IoT 3.7 Discussion 3.8 Conclusion References 4 The Technological Shift: AI in Big Data and IoT 4.1 Introduction 4.2 Artificial Intelligence 4.2.1 Machine Learning 4.2.2 Further Development in the Domain of Artificial Intelligence 4.2.3 Programming Languages for Artificial Intelligence 4.2.4 Outcomes of Artificial Intelligence 4.3 Big Data 4.3.1 Artificial Intelligence Methods for Big Data 4.3.2 Industry Perspective of Big Data 4.4 Internet of Things 4.4.1 Interconnection of IoT With AoT 4.4.2 Difference Between IIoT and IoT 4.4.3 Industrial Approach for IoT 4.5 Technical Shift in AI, Big Data, and IoT 4.5.1 Industries Shifting to AI-Enabled Big Data Analytics 4.5.2 Industries Shifting to AI-Powered IoT Devices 4.5.3 Statistical Data of These Shifts 4.6 Conclusion References 5 IoT’s Data Processing Using Spark 5.1 Introduction 5.2 Introduction to Apache Spark 5.2.1 Advantages of Apache Spark 5.2.2 Apache Spark’s Components 5.3 Apache Hadoop MapReduce 5.3.1 Limitations of MapReduce 5.4 Resilient Distributed Dataset (RDD) 5.4.1 Features and Limitations of RDDs 5.5 DataFrames 5.6 Datasets 5.7 Introduction to Spark SQL 5.7.1 Spark SQL Architecture 5.7.2 Spark SQL Libraries 5.8 SQL Context Class in Spark 5.9 Creating DataFrames 5.9.1 Operations on DataFrames 5.10 Aggregations 5.11 Running SQL Queries on DataFrames 5.12 Integration With RDDs 5.12.1 Inferring the Schema Using Reflection 5.12.2 Specifying the Schema Programmatically 5.13 Data Sources 5.13.1 JSON Datasets 5.13.2 Hive Tables 5.13.3 Parquet Files 5.14 Operations on Data Sources 5.15 Industrial Applications 5.16 Conclusion References 6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs 6.1 Introduction 6.1.1 Components of WSNs 6.1.2 Trust 6.1.3 Major Contribution 6.2 Related Work 6.3 Network Topology and Assumptions 6.4 Proposed Trust Model 6.4.1 CM to CM (Direct) Trust Evaluation Scheme 6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PR (.t)) 6.4.3 CH-to-CH Direct Trust Estimation 6.4.4 BS-to-CH Feedback Trust Calculation 6.5 Result and Analysis 6.5.1 Severity Analysis 6.5.2 Malicious Node Detection 6.6 Conclusion and Future Work References 7 Smart Applications of IoT 7.1 Introduction 7.2 Background 7.2.1 Enabling Technologies for Building Intelligent Infrastructure 7.3 Smart City 7.3.1 Benefits of a Smart City 7.3.2 Smart City Ecosystem 7.3.3 Challenges in Smart Cities 7.4 Smart Healthcare 7.4.1 Smart Healthcare Applications 7.4.2 Challenges in Healthcare 7.5 Smart Agriculture 7.5.1 Environment Agriculture Controlling 7.5.2 Advantages 7.5.3 Challenges 7.6 Smart Industries 7.6.1 Advantages 7.6.2 Challenges 7.7 Future Research Directions 7.8 Conclusions References 8 Sensor-Based Irrigation System: Introducing Technology in Agriculture 8.1 Introduction 8.1.1 Technology in Agriculture 8.1.2 Use and Need for Low-Cost Technology in Agriculture 8.2 Proposed System 8.3 Flow Chart 8.4 Use Case 8.5 System Modules 8.5.1 Raspberry Pi 8.5.2 Arduino Uno 8.5.3 DHT 11 Humidity and Temperature Sensor 8.5.4 Soil Moisture Sensor 8.5.5 Solenoid Valve 8.5.6 Drip Irrigation Kit 8.5.7 433 MHz RF Module 8.5.8 Mobile Application 8.5.9 Testing Phase 8.6 Limitations 8.7 Suggestions 8.8 Future Scope 8.9 Conclusion Acknowledgement References Suggested Additional Readings Key Terms and Definitions Appendix Example Code 9 Artificial Intelligence: An Imaginary World of Machine 9.1 The Dawn of Artificial Intelligence 9.2 Introduction 9.3 Components of AI 9.3.1 Machine Reasoning 9.3.2 Natural Language Processing 9.3.3 Automated Planning 9.3.4 Machine Learning 9.4 Types of Artificial Intelligence 9.4.1 Artificial Narrow Intelligence 9.4.2 Artificial General Intelligence 9.4.3 Artificial Super Intelligence 9.5 Application Area of AI 9.6 Challenges in Artificial Intelligence 9.7 Future Trends in Artificial Intelligence 9.8 Practical Implementation of AI Application References 10 Impact of Deep Learning Techniques in IoT 10.1 Introduction 10.2 Internet of Things 10.2.1 Characteristics of IoT 10.2.2 Architecture of IoT 10.3 Deep Learning 10.3.1 Models of Deep Learning 10.3.2 Applications of Deep Learning 10.3.3 Advantages of Deep Learning 10.3.4 Disadvantages of Deep Learning 10.3.5 Deployment of Deep Learning in IoT 10.3.6 Deep Learning Applications in IoT 10.3.7 Deep Learning Techniques on IoT Devices 10.4 IoT Challenges on Deep Learning and Future Directions 10.4.1 Lack of IoT Dataset 10.4.2 Pre-Processing 10.4.3 Challenges of 6V’s 10.4.4 Deep Learning Limitations 10.5 Future Directions of Deep Learning 10.5.1 IoT Mobile Data 10.5.2 Integrating Contextual Information 10.5.3 Online Resource Provisioning for IoT Analytics 10.5.4 Semi-Supervised Analytic Framework 10.5.5 Dependable and Reliable IoT Analytics 10.5.6 Self-Organizing Communication Networks 10.5.7 Emerging IoT Applications 10.6 Common Datasets for Deep Learning in IoT 10.7 Discussion 10.8 Conclusion References Part 2: ARTIFICIAL INTELLIGENCE IN HEALTHCARE 11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques 11.1 Introduction 11.2 Existing Methods Review 11.3 Methodology 11.3.1 Architecture of Stride U-Net 11.3.2 Loss Function 11.4 Databases and Evaluation Metrics 11.4.1 CNN Implementation Details 11.5 Results and Analysis 11.5.1 Evaluation on DRIVE and STARE Databases 11.5.2 Comparative Analysis 11.6 Concluding Remarks References 12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review 12.1 Introduction 12.2 Methodology 12.3 IoT in Mental Health 12.4 Mental Healthcare Applications and Services Based on IoT 12.5 Benefits of IoT in Mental Health 12.5.1 Reduction in Treatment Cost 12.5.2 Reduce Human Error 12.5.3 Remove Geographical Barriers 12.5.4 Less Paperwork and Documentation 12.5.5 Early Stage Detection of Chronic Disorders 12.5.6 Improved Drug Management 12.5.7 Speedy Medical Attention 12.5.8 Reliable Results of Treatment 12.6 Challenges in IoT-Based Mental Healthcare Applications 12.6.1 Scalability 12.6.2 Trust 12.6.3 Security and Privacy Issues 12.6.4 Interoperability Issues 12.6.5 Computational Limits 12.6.6 Memory Limitations 12.6.7 Communications Media 12.6.8 Devices Multiplicity 12.6.9 Standardization 12.6.10 IoT-Based Healthcare Platforms 12.6.11 Network Type 12.6.12 Quality of Service 12.7 Blockchain in IoT for Healthcare 12.8 Results and Discussion 12.9 Limitations of the Survey 12.10 Conclusion References 13 Monitoring Technologies for Precision Health 13.1 Introduction 13.2 Applications of Monitoring Technologies 13.2.1 Everyday Life Activities 13.2.2 Sleeping and Stress 13.2.3 Breathing Patterns and Respiration 13.2.4 Energy and Caloric Consumption 13.2.5 Diabetes, Cardiac, and Cognitive Care 13.2.6 Disability and Rehabilitation 13.2.7 Pregnancy and Post-Procedural Care 13.3 Limitations 13.3.1 Quality of Data and Reliability 13.3.2 Safety, Privacy, and Legal Concerns 13.4 Future Insights 13.4.1 Consolidating Frameworks 13.4.2 Monitoring and Intervention 13.4.3 Research and Development 13.5 Conclusions References 14 Impact of Artificial Intelligence in Cardiovascular Disease 14.1 Artificial Intelligence 14.2 Machine Learning 14.3 The Application of AI in CVD 14.3.1 Precision Medicine 14.3.2 Clinical Prediction 14.3.3 Cardiac Imaging Analysis 14.4 Future Prospect 14.5 PUAI and Novel Medical Mode 14.5.1 Phenomenon of PUAI 14.5.2 Novel Medical Model 14.6 Traditional Mode 14.6.1 Novel Medical Mode Plus PUAI 14.7 Representative Calculations of AI 14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis References 15 Healthcare Transformation With Clinical Big Data Predictive Analytics 15.1 Introduction 15.1.1 Big Data in Health Sector 15.1.2 Data Structure Produced in Health Sectors 15.2 Big Data Challenges in Healthcare 15.2.1 Big Data in Computational Healthcare 15.2.2 Big Data Predictive Analytics in Healthcare 15.2.3 Big Data for Adapted Healthcare 15.3 Cloud Computing and Big Data in Healthcare 15.4 Big Data Healthcare and IoT 15.5 Wearable Devices for Patient Health Monitoring 15.6 Big Data and Industry 4.0 15.7 Conclusion References 16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic 16.1 Introduction 16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate 16.1.2 Precautionary Guidelines Followed in Indian Continent 16.1.3 Spiritual Guidelines in Indian Society 16.1.4 Veda Vigyaan: Ancient Vedic Knowledge 16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon 16.1.6 The Yagya Samagri 16.2 Literature Survey 16.2.1 Technical Aspects of Yajna and Mantra Therapy 16.2.2 Mantra Chanting and Its Science 16.2.3 Yagya Medicine (Yagyopathy) 16.2.4 The Medicinal HavanSamagri Components 16.2.5 Scientific Benefits of Havan 16.3 Experimental Setup Protocols With Results 16.3.1 Subject Sample Distribution 16.3.2 Conclusion and Discussion Through Experimental Work 16.4 Future Scope and Limitations 16.5 Novelty 16.6 Recommendations 16.7 Applications of Yajna Therapy 16.8 Conclusions Acknowledgement References Key Terms and Definitions 17 Extraction of Depression Symptoms From Social Networks 17.1 Introduction 17.1.1 Diagnosis and Treatments 17.2 Data Mining in Healthcare 17.2.1 Text Mining 17.3 Social Network Sites 17.4 Symptom Extraction Tool 17.4.1 Data Collection 17.4.2 Data Processing 17.4.3 Data Analysis 17.5 Sentiment Analysis 17.5.1 Emotion Analysis 17.5.2 Behavioral Analysis 17.6 Conclusion References Part 3: CYBERSECURITY 18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations 18.1 Introduction 18.2 Characteristics of Fog Computing 18.3 Reference Architecture of Fog Computing 18.4 CISCO IOx Framework 18.5 Security Practices in CISCO IOx 18.5.1 Potential Attacks on IoT Architecture 18.5.2 Perception Layer (Sensing) 18.5.3 Network Layer 18.5.4 Service Layer (Support) 18.5.5 Application Layer (Interface) 18.6 Security Issues in Fog Computing 18.6.1 Virtualization Issues 18.6.2 Web Security Issues 18.6.3 Internal/External Communication Issues 18.6.4 Data Security Related Issues 18.6.5 Wireless Security Issues 18.6.6 Malware Protection 18.7 Machine Learning for Secure Fog Computing 18.7.1 Layer 1 Cloud 18.7.2 Layer 2 Fog Nodes For The Community 18.7.3 Layer 3 Fog Node for Their Neighborhood 18.7.4 Layer 4 Sensors 18.8 Existing Security Solution in Fog Computing 18.8.1 Privacy-Preserving in Fog Computing 18.8.2 Pseudocode for Privacy Preserving in Fog Computing 18.8.3 Pseudocode for Feature Extraction 18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature 18.8.5 Pseudocode for Encrypting Data 18.8.6 Pseudocode for Data Partitioning 18.8.7 Encryption Algorithms in Fog Computing 18.9 Recommendation and Future Enhancement 18.9.1 Data Encryption 18.9.2 Preventing from Cache Attacks 18.9.3 Network Monitoring 18.9.4 Malware Protection 18.9.5 Wireless Security 18.9.6 Secured Vehicular Network 18.9.7 Secure Multi-Tenancy 18.9.8 Backup and Recovery 18.9.9 Security with Performance 18.10 Conclusion References 19 Cybersecurity and Privacy Fundamentals 19.1 Introduction 19.2 Historical Background and Evolution of Cyber Crime 19.3 Introduction to Cybersecurity 19.3.1 Application Security 19.3.2 Information Security 19.3.3 Recovery From Failure or Disaster 19.3.4 Network Security 19.4 Classification of Cyber Crimes 19.4.1 Internal Attacks 19.4.2 External Attacks 19.4.3 Unstructured Attack 19.4.4 Structured Attack 19.5 Reasons Behind Cyber Crime 19.5.1 Making Money 19.5.2 Gaining Financial Growth and Reputation 19.5.3 Revenge 19.5.4 For Making Fun 19.5.5 To Recognize 19.5.6 Business Analysis and Decision Making 19.6 Various Types of Cyber Crime 19.6.1 Cyber Stalking 19.6.2 Sexual Harassment or Child Pornography 19.6.3 Forgery 19.6.4 Crime Related to Privacy of Software and Network Resources 19.6.5 Cyber Terrorism 19.6.6 Phishing, Vishing, and Smishing 19.6.7 Malfunction 19.6.8 Server Hacking 19.6.9 Spreading Virus 19.6.10 Spamming, Cross Site Scripting, and Web Jacking 19.7 Various Types of Cyber Attacks in Information Security 19.7.1 Web-Based Attacks in Information Security 19.7.2 System-Based Attacks in Information Security 19.8 Cybersecurity and Privacy Techniques 19.8.1 Authentication and Authorization 19.8.2 Cryptography 19.8.3 Installation of Antivirus 19.8.4 Digital Signature 19.8.5 Firewall 19.8.6 Steganography 19.9 Essential Elements of Cybersecurity 19.10 Basic Security Concerns for Cybersecurity 19.10.1 Precaution 19.10.2 Maintenance 19.10.3 Reactions 19.11 Cybersecurity Layered Stack 19.12 Basic Security and Privacy Check List 19.13 Future Challenges of Cybersecurity References 20 Changing the Conventional Banking System through Blockchain 20.1 Introduction 20.1.1 Introduction to Blockchain 20.1.2 Classification of Blockchains 20.1.3 Need for Blockchain Technology 20.1.4 Comparison of Blockchain and Cryptocurrency 20.1.5 Types of Consensus Mechanism 20.1.6 Proof of Work 20.1.7 Proof of Stake 20.2 Literature Survey 20.2.1 The History of Blockchain Technology 20.2.2 Early Years of Blockchain Technology: 1991–2008 20.2.3 Literature Review 20.2.4 Analysis 20.3 Methodology and Tools 20.3.1 Methodology 20.3.2 Flow Chart 20.3.3 Tools and Configuration 20.4 Experiment 20.4.1 Steps of Implementation 20.4.2 Screenshots of Experiment 20.5 Results 20.6 Conclusion 20.7 Future Scope 20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises References 21 A Secured Online Voting System by Using Blockchain as the Medium 21.1 Blockchain-Based Online Voting System 21.1.1 Introduction 21.1.2 Structure of a Block in a Blockchain System 21.1.3 Function of Segments in a Block of the Blockchain 21.1.4 SHA-256 Hashing on the Blockchain 21.1.5 Interaction Involved in Blockchain-Based Online Voting System 21.1.6 Online Voting System Using Blockchain – Framework 21.2 Literature Review 21.2.1 Literature Review Outline 21.2.2 Comparing the Existing Online Voting System References 22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects 22.1 Introduction 22.2 Literature Review 22.3 Different Variants of Cybersecurity in Action 22.4 Importance of Cybersecurity in Action 22.5 Methods for Establishing a Strategy for Cybersecurity 22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity 22.7 Where AI Is Actually Required to Deal With Cybersecurity 22.8 Challenges for Cybersecurity in Current State of Practice 22.9 Conclusion References Index EULA