Network Optimization in Intelligent Internet of Things Applications: Principles and Challenges

✍ Scribed by Payal Khurana Batra (editor), Pawan Singh Mehra (editor), Sudeep Tanwar (editor)

• Publisher: Chapman and Hall/CRC
• Year: 2024
• Tongue: English
• Leaves: 349
• Edition: 1
• Category: Library

✦ Synopsis

Network Optimization in Intelligent Internet of Things Applications: Principles and Challenges sheds light on the optimization methods that form the basis of effectivecommunication between networked devices. It is an excellent resource as it providesreaders with a thorough understanding of the methods, ideas, and tactics essential toattaining seamless connectivity and improving performance.

This book presents the fundamental ideas that govern network optimization, from maximizing throughput and lowering latency to handling a variety of communication protocols and minimizing energy use. It also addresses scalability issues, security flaws, and constantly changing IoT environments along with optimization techniques.

This book uses cutting-edge research and real-world examples to give readers the knowledge and skills to address the complex problems associated with network optimization in intelligent IoT applications. It also examines machine learning-driven predictive analytics, robust security protocols, flexible routing algorithms, and the integration of edge computing - all crucial instruments for overcoming obstacles and attaining peak performance.

This book provides a comprehensive understanding of the principles, challenges, and cutting-edge solutions in IoT network optimization for all kinds of readers, whether it is students, academicians, researchers, or industry professionals. This book unleashes the potential of networked smart devices, which can be unleashed in various sectors.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Part I Introduction to Iot, Network Objectives, and Optimization Techniques
1 Iot Networks: Issues, Challenges, and Objectives
1.1 Introduction
1.2 Iot Network Architecture
1.3 Applications of Iot Networks and Devices
1.3.1 Applications of Iot Network
1.3.1.1 Smart Home Automation
1.3.1.2 Industrial Automation
1.3.1.3 Agriculture and Farming
1.3.1.4 Transportation and Logistics
1.3.1.5 Smart Cities
1.3.1.6 Wearable Devices
1.3.1.7 Healthcare
1.3.1.8 Energy Management
1.3.1.9 Retail and Inventory Management
1.3.1.10 Environmental Monitoring
1.3.2 Case Studies: Application of Iot Devices and Their Social Impacts
1.3.2.1 Agriculture: Precision Farming for Improved Crop Management
1.3.2.2 Industrial Manufacturing: Predictive Maintenance for Enhanced Efficiency
1.3.2.3 Healthcare: Remote Patient Monitoring for Personalized Care
1.3.2.4 Smart Cities: Transforming Urban Parking with Smart Parking Systems
1.3.2.5 Energy Management: Optimizing Building Efficiency Through Iot Solutions
1.4 Objectives, Issues, Challenges, and Importance of Iot Networks
1.4.1 Objectives of Iot Network
1.4.2 Issues
1.4.3 Challenges of Iot Network
1.4.4 Importance of Iot Network
1.4.4.1 Increased Efficiency
1.4.4.2 Cost Saving
1.4.4.3 Improved Decision-making
1.4.4.4 Enhanced Customer Experiences
1.4.4.5 New Business Models
1.4.4.6 Improved Safety and Security
1.4.5 Overview of the Current State of Development
1.5 Advancement of Tools and Technologies in Iot
1.5.1 Recently Used Tools in Iot
1.5.1.1 Arduino
1.5.1.2 Raspberry Pi
1.5.1.3 Aws Iot Core
1.5.1.4 Google Cloud Iot Core
1.5.1.5 Microsoft Azure Iot Hub
1.5.1.6 Particle
1.5.1.7 Ibm Watson Iot Platform
1.5.1.8 Ubidots
1.5.1.9 Thingsboard
1.5.1.10 Losant
1.5.2 Technology Used in the Field of Iot
1.6 Future Scope and Conclusion
1.6.1 Future Scope
1.6.2 Conclusion
References
2 6g for Intelligent Internet of Things
2.1 Introduction
2.2 Literature Review
2.3 the Rise of 6g Technology
2.3.1 the Transition from 5g to 6g Innovation
2.3.2 6th Generation
2.4 Internet of Things
2.5 Requisites of 6g for Intelligent Iot
2.6 the Out-and-out Network
2.7 Contemporary Technologies in Conjunction with Intelligent Iot
2.7.1 Mobile Edge Computing
2.7.2 Fog Computing
2.7.3 Federated Learning: an Ai Component
2.7.4 Quantum Cryptography
2.7.5 Incorporation of Wireless Information and Energy Transfer
2.7.6 the Amalgamation of Sensing and Communication
2.7.7 the Dynamic Network Slicing
2.7.8 Proactive Caching: a Solution for Base Station Traffic Congestion
2.7.9 Terahertz Communication
2.7.10 Optical Wireless Communication
2.7.11 Mimo-cell-free Communication
2.7.12 Big Data Analytics: a Component of Machine Learning
2.7.13 Blockchain Technology
2.7.14 Blockchain and Artificial Intelligence: the Prospective Combo
2.7.14.1 Blockchain Empowering Ai
2.7.14.2 Ai Enhancing Blockchain
2.8 Employment of 6g for Intelligent Iot
2.8.1 Healthcare
2.8.2 Transport
2.8.3 Satellite Communication
2.8.4 Unmanned Aerial Vehicles
2.9 Exploration of Future Avenues
2.9.1 Reliability and Confidentiality of 6g for Intelligent Iot
2.9.2 Energy Efficiency in 6g for Intelligent Iot
2.9.3 Hardware Limitations of Iot Devices
2.9.4 Standard Pre-requisites for 6g for Intelligent Iot
2.10 Pros and Cons of Contemporary Technologies in 6g for Intelligent Iot
2.11 Conclusion
References
3 a Systematic Study of Intelligent Toll Payment Gateways
3.1 Introduction
3.2 Evolution of Tolling
3.2.1 Corridor or Manual Tolling
3.2.2 Cordon Tolling
3.2.3 Area-wide Mileage Tolling
3.2.4 Integrated System Management
3.3 Existing Toll Collection Models
3.3.1 Electronic Toll Collection
3.3.1.1 Rfid
3.3.1.2 Fastag
3.3.1.3 Automatic Number Plate Recognition (anpr)
3.3.1.4 Dedicated Short-range Communication (dsrc)
3.3.2 Countries Using etc
3.4 Architecture of etc
3.4.1 Vehicle Detectors (in-road Sensors)
3.4.2 Overhead Cameras
3.4.3 Vehicle-to-roadside Communication
3.4.4 Central System
3.4.5 Toll Plaza Infrastructure
3.4.6 Payment Gateway
3.4.7 Data Management and Analytics
3.4.8 Key Features and Functionalities of etc
3.5 Upcoming Technologies
3.5.1 Gnss-based Tolling System
3.5.1.1 Working of Gnss-based Tolling System
3.5.1.2 Advantages of Gnss-based Tolling System
3.5.1.3 Limitations of Gnss-based Tolling System
3.6 Conclusion
References
4 Iot-based Healthcare System for Early Prediction of Alzheimer’s Disease
4.1 Introduction
4.2 Literature Survey
4.3 Effects of Alzheimer’s Disease
4.4 Symptoms of People with Ad
4.5 Different Stages of Ad Development and Cognitive Decline
4.5.1 Initial Stage
4.5.2 Middle Stage
4.5.3 Final Stage
4.6 the Importance of Early Detection for Effective Management and Intervention
4.7 Approaches and Methods for Early Detection and Prediction
4.8 Benefits of Iot in Healthcare
4.9 Techniques for Alzheimer’s Disease Prediction
4.10 Data Mining Architecture
4.11 Future Directions in Early Detection and Prediction of Ad
4.12 Data Sources for Alzheimer’s Disease Prediction
4.13 Machine Learning Models for Alzheimer’s Disease Prediction
4.14 Results and Discussion
4.15 Implications and Future Directions
4.16 Conclusion
References
5 Ethical and Social Implications of Iot-integrated Autonomous Vehicles
5.1 Automated Vehicle
5.1.1 History of Automated Vehicles
5.1.2 Features of Avs
5.1.3 Current State of Autonomous Car Industry
5.1.4 Avs as Public Transport
5.1.5 Ethical and Social Implications of Autonomous Vehicles
5.1.6 Integration of Iot with Automated Vehicles
5.2 Future Scope
5.3 Conclusion
References
Part Ii Energy Conservation for Intelligent Iot Networks
6 Green Internet of Things
6.1 Introduction
6.1.1 Contribution of Chapter
6.1.2 Organization of the Chapter
6.1.3 Internet of Things
6.1.4 Green Internet of Things
Green Rfid
Green Wsn
Green Cloud Computing
Green M2m
Green Data Centre
6.2 Comparison Between Various Types of Green Iot Technology
6.3 Literature Survey of Recent Trends in G-iot
6.3.1 New Developments in Green Iot Technology
6.3.2 Current Market Scenarios of Green Iot
6.3.3 Case Study: Current and Future Trends in the Effect of Cell Phones on the Environment
6.4 Opportunities and Challenges of the Green Internet of Things
6.5 Conclusion and Future Scope
References
7 Network Optimization for Internet of Things (iot): Techniques and Algorithms
7.1 Introduction
7.1.1 Overview
7.2 Types of Optimization Techniques Implemented to Optimize Iot Devices
7.2.1 Resource Optimization
7.2.2 Network Optimization
7.2.3 Data Optimization
7.3 Algorithms Used for Optimization
7.3.1 Particle Swarm Optimization (pso)
7.3.2 Genetic Algorithms
7.3.3 Ant Colony Optimization
7.3.4 Neural Networks
7.4 Limitations in Optimization Techniques
7.4.1 Lack of Standardization in Iot Devices
7.4.2 Limitations in Processing Power of Iot Devices
7.4.3 Scalability of Iot Networks
7.4.4 Dynamic Nature of Iot Environments
References
Part Iii Congestion Control and Heterogeneity in Intelligent Iot Network
8 Network Optimization on Internet of Medical Things
8.1 Introduction
8.1.1 Need for Network Optimization in Medical Iot
8.1.2 Importance of Network Optimization in Medical Iot
8.2 Understanding Network Congestion in Medical Iot
8.2.1 Indicators of Network Congestion
8.2.2 Causes of Network Congestion
8.2.3 Network Congestion on Medical Impact of Iot Systems
8.3 Network Optimization Strategies
8.3.1 Hardware Solutions
8.3.1.1 Upgrading Network Infrastructure
8.3.1.2 Implementing Traffic Management Techniques
8.3.2 Software Solutions
8.3.2.1 Dynamic Resource Allocation
8.3.2.2 Machine Learning Algorithms
8.3.3 Combination of Hardware and Software Solutions
8.4 Conclusions
References
Part Iv Scalability, Reliability, and Qos in Intelligent Iot Network
9 Scalable Iot Architecture and Services: Challenges and Opportunities in Iot-enabled Healthcare Monitoring Frameworks
9.1 Iot: 4th Industrial Revolution in Healthcare
9.1.1 Healthcare 4.0 and Industry 4.0
9.1.2 Internet of Health Things (ioht)
9.1.3 Fog Computing and Healthcare 4.0: Www (why, Where, and When)
9.1.4 Fog Computing Architecture
9.2 Healthcare Industry and Their Challenges
9.2.1 Fog Computing-enabled Healthcare 4.0
9.2.2 Recent State of Frameworks in Healthcare
9.2.3 Why to Analyse Healthcare Monitoring Architectures and Frameworks?
9.3 Related Work
9.3.1 Research Challenges of Present State of Proposed Monitoring Frameworks
9.4 Proposed Framework
9.5 Conclusion and Future Scope
References
10 a Study on Intrusion Detection System for Rpl Protocol Attacks in Internet of Things
10.1 Introduction
10.2 Outline of Rpl Protocol
10.3 Rpl Protocol Security Attacks
10.3.1 Attacks on Resources
10.3.2 Attacks on Topology
10.3.3 Attacks on Traffic
10.4 Security Solutions Based on Intrusion Detection System
10.4.1 Signature-based Ids
10.4.2 Anomaly-based Ids
10.4.3 Hybrid Ids
10.4.4 Machine Learning (ml)-based Ids
10.5 Performance Metrics
10.5.1 Accuracy
10.5.2 Precision
10.5.3 Recall
10.5.4 F1-score
10.6 Limitation with Dl and Ml
10.7 Conclusion
References
Part V Security and Privacy in Intelligent Iot Network
11 Lightweight Cryptography Algorithms, Authorization, and Authentication Techniques in Iot
11.1 Introduction
11.2 Literature Survey
11.3 a Systematic Approach to Iot
11.3.1 Iot Architecture
11.3.1.1 Iot Sensor Layer
11.3.1.2 Network Layer
11.3.1.3 Middleware Layer
11.3.1.4 Application Layer
11.3.2 Iot Technologies
11.3.3 Iot Applications
11.3.3.1 Smart Cities
11.3.3.2 Smart Health
11.3.3.3 Smart Agriculture
11.4 Iot Security Issues, Requirements, and Threats
11.4.1 Security Issues
11.4.2 Key Requirements for Iot Security
11.4.3 Iot Threats
11.5 Recent Lightweight Cryptography for Iot Security
11.5.1 Asymmetric Encryption
11.5.2 Symmetric Key Encryption
11.5.3 Hashing
11.6 Lightweight Schemes for Iot
11.6.1 Lightweight Cryptography
11.6.2 Lightweight Hash Function
11.6.3 Lightweight Integrity Schemes
11.7 Lightweight Authentication Schemes for Iot
11.7.1 Two-factor Authentication Schemes
11.7.2 Three-factor Authentication Schemes
11.7.3 Multi-factor Authentication Schemes
11.8 Lightweight Cryptographically Secure Authentication Model
11.9 Limitations of Existing Lightweight Cryptography
11.10 Conclusion and Future Work
References
12 Deep Learning Techniques for Iot Security: a Comprehensive Review and Future Directions
12.1 Introduction
12.1.1 the Development of Iot and Its Security Consequences
12.1.2 Limitations of Conventional Security Mechanisms
12.1.3 the Potential of Deep Learning in Internet of Things Security
12.2 Objectives and Scope
12.2.1 the Chapter’s Framework
12.3 Literature Survey
12.4 Methodology
12.5 Significance of the Study
12.6 Future Directions and Research Opportunities
12.7 Conclusion
References
13 an Empirical Study of the Concept Drift Driven Machine Learning for Iot Network Anomaly Detection
13.1 Introduction
13.2 Literature Review
13.3 Methodology
13.3.1 Proposed Algorithm: Optimal Sliding and Adaptive Windowing
13.4 Results and Discussion
13.4.1 Results and Analysis
13.4.2 the Used Datasets
13.4.3 Osaw Performance over Cidds (2017) and Nsl- Kdd Datasets
13.5 Conclusion
References
14 Internet of Things (iot) Security Using Blockchain
14.1 Introduction
14.2 Literature Survey
14.3 Iot Security Concerns and Solutions
14.3.1 Security Concerns
14.3.2 Solutions for Securing Iot
14.4 Blockchain
14.4.1 Overview
14.4.2 Characteristics
14.4.3 Protocols and Technologies
14.4.3.1 Consensus Protocol
14.4.3.2 Practical Byzantine Fault Tolerance
14.4.3.3 Smart Contract
14.5 Requirement of Blockchain in Iot
14.5.1 Decentralization
14.5.2 Smart Contracts
14.5.3 Interoperability
14.6 Blockchain Deployment in Iot
14.6.1 Agriculture
14.6.2 Industry 4.0
14.6.3 Smart V2v Communications
14.6.4 Uav Communications
14.7 Securing Iot Using Blockchain
14.8 a Comparative Analysis: Authentication Techniques of Iot
14.9 Conclusion
References
15 Secure Communication and Authentication in Iot-based Uav Networks
15.1 Introduction
15.1.1 Motivation
15.1.2 Contribution
15.1.3 Outline of the Chapter
15.2 Overview of Unmanned Aerial Vehicles
15.2.1 Uav
15.2.2 Category of Uavs
15.2.2.1 Weight-based [6]
15.2.2.2 Altitude-based [7]
15.2.2.3 Wings-based [7]
15.2.2.4 Range-based [7]
15.2.3 Connections in Iot-based Uav Networks
15.2.4 Features of Iot-based Uav Networks
15.3 Pertinent Work
15.4 Security Requirements for Secure Communication and Authentication
15.4.1 Mutual Authentication
15.4.2 Man-in-the-middle Attack
15.4.3 Replay Attack
15.4.4 Brute-force Attack
15.4.5 Physical Capture Attack
15.4.6 Session Key Attack
15.4.7 Un-traceability
15.4.8 Anonymity
15.5 Secure Communication and Authentication Mechanisms
15.5.1 Mutual Authentication Scheme
15.5.2 Sentinel Mechanism
15.5.3 Secure Identity Verification Mechanism
15.5.4 Discernible Yet Privacy-enforcing Authentication
15.5.5 Authentication-secured Encryption
15.5.6 Fortified Framework for Safeguarding Privacy During Authentication
15.5.7 Stealthy Pathways Empowered Lightweight Authentication
15.5.8 Cipher Concordance and Streamlined Verification Paradigm
15.5.9 Streamlined Key Accord in Remote User Authentication
15.5.10 Dual-phase, Agile Mutual Authentication Paradigm
15.5.11 Authentication Framework for Aggregated Data
15.5.12 Encryption Paradigm with Homomorphic Properties
15.5.13 Security Protocol Anchored in Mutual Authentication for Uav to Uav and Uav to Gcs Communication
15.5.14 Secure Hashing Scheme
15.5.15 Protocol for Authentication and Key Accord with Three Factors
15.5.16 Strong Key Management Protocol for Authentication
15.5.17 Scheme for Lightweight Verification with Two Factors
15.5.18 Encryption Approach Utilising Symmetric and Ecc Techniques
15.5.19 Mac Protocol (message Authentication Code Protocol)
15.5.20 Temporal Credentials-based Authentication
15.5.21 Puf-based Drone-2-drone Mutual Authentication Protocol
15.5.22 Access and Handover Authentication Schemes
15.6 Conclusion and Future Scope
References
16 a Comprehensive Iot Security Framework Using Edge Computing: Approaches and Challenges
16.1 Introduction
16.1.1 Motivation and Contribution
16.1.2 Fog/edge Computing (fegc)
16.1.3 Cyber-physical System (cphs/cps)
16.2 Edge Computing-based Secured Iot Framework (egciot)
16.2.1 Previous Literature
16.2.2 Architecture at Edge Layer
16.2.3 Firewall at Egc
16.2.4 Edge Computing-based Intrusion Detection System
16.2.5 Edge-based Authentication and Authorization Framework
16.3 Three-layer Security Framework Using Edge Computing
16.4 Four-layer Architecture Using Edge Computing
16.5 Five-layer Framework
16.6 Case Study on Iot Security Framework Using Edge Technology in Healthcare Sector
16.7 Conclusion
16.8 Future Research Direction
References
17 Edge Computing-based Design for Iot Security
17.1 Introduction
17.1.1 Motivation
17.1.2 Contribution
17.2 Computing Paradigms Associated with Edge Computing
17.2.1 Cloud Computing
17.2.2 Mobile Computing
17.2.3 Mobile Cloud Computing
17.2.4 Edge Computing
17.2.4.1 Cloudlet Computing
17.2.4.2 Mobile Ad Hoc Cloud Computing (macc)
17.2.4.3 Fog Computing
17.3 Threats and Attacks on Security and Privacy in Iot Based on Edge Computing (ec)
17.3.1 Authentication and Authorisation Attacks
17.4 Various Solutions and Countermeasures to Address the Security and Privacy Issues in Ec-based Iot
17.5 the Difficulties and Obstacles That Come with Implementing Edge Computing in Iot Applications
17.6 Case Studies
Ec Can Be Used in Various Fields, Which Are as Follows, Focusing on Security
17.7 Conclusion
References
18 Wi-fi Jammer, a Threat to Iot Networks: Experimental Perspective
18.1 Introduction
18.2 Literature Review
18.3 Jamming Techniques
18.3.1 Types of Attacks
18.3.1.1 Deauthentication Attack (deauth)
18.3.1.2 Clone Beacon
18.3.1.3 Beacon
18.3.2 Mounting an Attack
18.4 System Requirement
18.5 Hardware Requirement
18.5.1 Nodemcu
18.5.2 Booster Module
18.5.3 Charging Module
18.6 Software Requirement
18.7 Configuration
18.8 Algorithms to Mount Various Attacks
18.8.1 Deauth Attack
18.8.2 Beacon Attack
18.8.3 Clonebeacon
18.9 Result and Analysis
18.9.1 Deauthentication Attack
18.9.2 Beacon Attack
18.9.3 Clonebeacon Attack
18.10 Conclusion and Future Work
References
Index