The Internet Of Drones AI Applications for Smart Solutions 1st Edition by Arun Solanki, Sandhya Tarar, Simar Preet Singh, Akash Tayal ISBN 9781774639856 1774639858

The Internet Of Drones AI Applications for Smart Solutions 1st Edition by Arun Solanki, Sandhya Tarar, Simar Preet Singh, Akash Tayal – Ebook PDF Instant Download/Delivery: 9781774639856 ,1774639858
Full download The Internet Of Drones AI Applications for Smart Solutions 1st Edition after payment

Product details:
ISBN 10: 1774639858
ISBN 13: 9781774639856
Author: Arun Solanki, Sandhya Tarar, Simar Preet Singh, Akash Tayal

The Internet Of Drones AI Applications for Smart Solutions 1st Edition Table of contents:

Part I Ontroduction to Drones

1. The Internet of Things (IoT) Architectures and Protocols for Drone Communications

1.1 Introduction

1.2 IoD Architectures

1.2.1 Cloud-Based Architecture

1.2.2 Communication Network Architecture

1.2.3 UAV Architecture

1.2.4 Computing Architecture

1.3 IoD Protocols

1.3.1 Key Management

1.3.2 User Authorization

1.3.3 Access Control

1.3.4 Intrusion Detection and Prevention

1.3.5 Identity and Location Privacy

1.4 Recent Applications of IoD

1.4.1 Agriculture

1.4.2 Delivery

1.4.3 Healthcare

1.4.4 Military

1.4.5 Environmental Protection

1.4.6 Search and Rescue

1.5 Conclusion And Future Scope Of IoD

References

2. Approaching Internet Renovation of Imperceptible Computers to Facilitate the Internet of Drones

2.1 Introduction

2.2 Literature Review

2.3 Vital Technological Tools Behind Ubicomp, Iot, and IoD

2.4 Connection of Ubicomp with IoT

2.5 Potentialities of Ubicomp

2.5.1 u-HEALTH

2.5.2 u-Accessibility

2.5.3 u-Learning

2.5.4 u-Commerce

2.5.5 U-Games

2.6 Disputes Associated with Ubicomp

2.7 Future of Ubicomp

2.8 Ubiquitous Computing As A Facilitator For IoD

2.9 Prospects Related To IoD

2.10 Challenges of IoD

2.11 Visualization of Smart Cities With Flying IoT

2.12 Conclusion And Future Directions

References

3. Implementation and Deployment of 5G-Drone Setups

3.1 Introduction

3.2 Mobile Networks-Based Drones Handling Technique

3.2.1 Mobile-Enabled Drones

3.2.2 Wireless Infrastructure Drones

3.3 5G Standardization Advancement and Which is More Relevant in Comparison to the Drones Process

3.3.1 Simplicity And Versatility Wireless Networks

3.3.2 5G Ran

3.3.3 5G Core

3.3.4 5G Network Management

3.4 Standardization Prospect And Digitization

3.4.1 Network Configuration and Slice Design for Wids

3.4.2 Support for New Business Models

3.4.3 New Qos And Kpi Parameters

3.5 Conclusions

References

Part II Drone Automation Solutions for Security and Surveillance

4. Security Issues in the Internet of Drones (IoDs)

4.1 Introduction

4.1.1 Architecture

4.1.2 Problem Description

4.2 Confidentiality, Integrity, Authentication, And Availability Attack

4.2.1 Data Interception

4.2.2 Malicious Data Injection

4.2.3 Digital Signatures

4.2.4 Digital Signature Algorithm (Dsa)

4.2.5 Cryptographic

4.2.6 Memory Protection Unit

4.2.7 Man-In-The Middle Attacks/Eavesdropping/Replay Attacks

4.2.8 Spoofing

4.2.9 Traffic Analysis/Drone Detection

4.2.10 Unauthorized Access

4.2.11 Channel Jamming

4.2.12 Mavlink

4.2.13 Denial Of Service (Dos) Attacks/Distributed Denial Of Service (Ddos)

4.2.14 Anomaly Detection

4.2.15 Message Forgery

4.2.16 Privacy

4.2.17 Software Defined Network (Sdn)

4.2.18 Identity-Based Attack

4.2.19 False Information Dissemination/Fabrication Attack

4.2.20 Spoofing Attacks/Sybil Attack

4.2.21 Three-Way Handshake Attack

4.2.22 Wi-Fi Air Crack Attack

4.2.23 Forensic-By-Design

4.2.24 Secure Data Aggregation

4.2.25 Tree-Based Aggregation Technique

4.2.26 Cluster-Based Data Aggregation Protocols

4.2.27 Dynamic Topologies And Adaptive Routing Mechanisms

4.2.28 Network Topology Attack

4.2.29 Dns Amplification Attack

4.2.30 Attack On Security Log

4.2.31 Malicious Insider

4.3 Conclusion And Future Scope

References

5. Real-Time Monitoring and Analysis of Troposphere Pollutants Using a Multipurpose Surveillance Drone

5.1 Introduction

5.2 Structure Of A Basic Drone

5.2.1 Main Components

5.3 Proposed Multipurpose Surveillance Drone

5.3.1 Sensors Used In Drone

5.3.2 Interfacing Of Sensors With Raspberry Pi

5.3.3 Structural Features Of The Presented Drone

5.4 Troposphere Parameters Measurement

5.5 Comparison Of Drone Data And Weather Department Data

5.6 Conclusion

References

6. Advanced Object Detection Methods for Drone Vision

6.1 Introduction

6.2 Literature Review

6.3 Types of Annotations Used in Computer Vision

6.4 Navigational Structure of the IoD Architecture

6.5 Deep Learning (DL) Fundamentals

6.5.1 Artificial Neural Network (Ann)

6.5.2 Convolutional Neural Network (Cnn)

6.6 You Only Look Once (YOLO)

6.6.1 Architecture

6.6.2 Working

6.6.3 Advantages of YOLO

6.6.4 Disadvantages of YOLO

6.7 Faster R-CNN

6.7.1 R-CNN

6.7.2 Fast R-CNN

6.7.3 Working of Faster R-CNN

6.7.4 Advantages of Faster R-CNN

6.7.5 Disadvantages of Faster R-CNN

6.7.6 Architecture

6.8 Single Shot Detection (SSD)

6.8.1 Architecture

6.8.2 Working

6.8.3 Advantages of SSD

6.8.4 Disadvantages of SSD

6.9 Conclusion and Future Scope

References

7. Security Analysis of UAV Communication Protocols: Solutions, Prospects, and Encounters

7.1 Introduction

7.2 Architecture Of Drone

7.3 Routing Protocol Challenges In Uaanet

7.3.1 Security Challenges In Uaanet

7.3.2 Uas Certification Challenges

7.4 Model Driven Development Of Secure Uaanet Routing Protocol

7.4.1 Secure Routing Protocol Design Architecture

7.5 Drone Communication And Security

7.5.1 Security In Aerial Surveillance And Tracking

7.5.2 Security In Collision And Obstacle Avoidance

7.6 Communication Architectures

7.6.1 Centralized Communications

7.6.2 Decentralized Communications

7.7 Promising Communication Modes

7.7.1 Security Attacks In Uav

7.7.2 Multi-Layer Security Framework

7.8 Futuristic Research Areas

7.8.1 Medicinal Drones

7.8.2 Agricultural Drones

7.8.3 Rescue Scenarios

7.9 Conclusion And Future Scope

References

Part III Drones in the Machine Learning Environment

8. Challenges and Opportunities of Machine Learning and Deep Learning Techniques for the Internet of Drones

8.1 Introduction: Internet of Drones (IoD)

8.1.1 IoD Networks

8.2 Advantages Of Drones Using Mobile Technologies

8.3 ADVANTAGES OF INTERNET OF DRONES (IoD)

8.4 DISADVANTAGES OF INTERNET OF DRONES (IoD)

8.5 Introduction: Machine Learning (Ml) Algorithms

8.5.1 History Of Machine Learning (Ml)

8.5.2 Probability Theory

8.5.3 Statistical Theory

8.5.4 Machine Learning (Ml)

8.5.5 Essential Keywords

8.5.6 Mechanism Of Machine Learning (Ml)

8.5.7 Traditional Programming Vs. Machine Learning (Ml)

8.5.8 How Machine Learning (Ml) Works?

8.5.9 Key Elements Of Machine Learning (Ml) (Figure 8.15)

8.5.10 Machine Learning (Ml) Approaches

8.5.11 Simple Linear Regression Algorithm

8.5.12 Random Forest Algorithm

8.5.13 Logistic Regression Algorithm

8.5.14 Knn (K-Nearest Neighbors)

8.5.15 Decision Tree Algorithm

8.5.16 Support Vector Machine (Svm)

8.5.17 Naïve Bayes

8.5.18 Unsupervised Learning Algorithm

8.5.19 K-Means Algorithm

8.5.20 Apriori Algorithm

8.5.21 Clustering Algorithm

8.5.22 Semi-Supervised Learning Algorithm

8.5.23 Reinforcement Learning Algorithm

8.5.24 Characteristics Of Reinforcement LearningAlgorithm

8.5.25 Reinforcement Learning Algorithm Applications

8.6 Introduction to Neural Networks

8.6.1 Deep Neural Networks

8.6.2 Convolutional Neural Network (Cnn)

8.6.3 Recurrent Neural Network

8.6.4 Generative Adversarial Network (Gan)

8.7 Introduction To Deep Fake

8.7.1 How Does Deepfake Work?

8.7.2 Deepfake Future Prospective

8.7.3 Discussions And Conclusion

References

9. Machine Learning and Deep Learning Algorithms for IoD

9.1 Introduction

9.2 Related Work

9.3 Machine Learning (ML) Algorithm (Figure 9.1)

9.3.1 Supervised Learning (Sl)

9.3.2 Unsupervised Learning

9.4 Deep Learning (Dl) Algorithm

9.4.1 Classification Of Neural Network

9.4.2 Dnn Architectures

9.4.3 Neural Network Of Autoencoder

9.4.4 Restricted Boltzmann Machine (Rbm)

9.4.5 Long Short-Term Memory (Lstm)

9.4.6 Comparison Of Dnn Networks

9.4.7 Training Algorithms

9.4.8 Comparison Of Deep Learning (Dl) Algorithms

9.5 Drone as the New “Flying IoT”

9.5.1 ML AND DL BASED APPLICATION OF IoD

9.6 Machine Learning (Ml) In Drone

9.6.1 Radio Resource Allocation

9.6.2 Relays Design Of Collectors

9.6.3 Type Of Uav Choosing

9.6.4 Selected Several Uavs Acting As Bss

9.6.5 Structure of a Mobile Cloud

9.7 Principle Working Of Machine Learning (Ml) In Drone

9.7.1 Drone Data And Cloud Computing

9.7.2 Method Of Data Handling As A Group (Mdhg) Algorithm

9.7.3 Drone Operating Steps

9.8 Deep Learning (Dl) Use In Drone

9.9 Principle Working of Deep Learning (Dl) in Drone

9.9.1 Structure Of Path Planner

9.10 Drone Controller

9.11 Drone Navigation

9.12 Deep Learning (Dl) Drone Surveillance System

9.12.1 Identification And Understanding Of Human Acts

9.12.2 Single Shot Multi-Box Detector Used by Human Detection

9.13 ML and DL Based IoD Algorithm

9.13.1 Algorithm of ML and DL Based IoD Algorithm

9.14 Paradigm of MLDL Based IoD

9.14.1 Motion Planning

9.15 Conclusion And Future Work

References

Part IV Drones in Smart Cities

10. Smart Cities and the Internet of Drones

10.1 Introduction

10.2 Smart Cities

10.2.1 Smart City Real-Time Implementations

10.3 Internet of Drones (IoD)

10.3.1 Drones

10.3.2 Relevant Networks

10.4 Security Issues

10.4.1 Attacks on IoD

10.5 IoD Realtime Implementations

10.5.1 Construction And Infrastructure

10.5.2 Emergency/Disaster Relief

10.5.3 Smart City Monitoring

10.5.4 Smart Agriculture

10.5.5 Wildlife Conservation

10.5.6 Healthcare

10.5.7 Weather Forecasting

10.5.8 Energy

10.5.9 Mining

10.5.10 Insurance

10.6 Challenges of IoD

10.7 Conclusion And Future Scope

References

11. The Internet of Drones for Enhancing Service Quality in Smart Cities

11.1 Introduction

11.2 Professional Drone

11.3 Drone-Api

11.4 IoD Application Into Smart City Solutions

11.4.1 Intelligent Traffic Control In Smart Cities

11.4.2 Control Of Crowd In Smart Cities

11.4.3 Control Of Natural Disasters

11.4.4 First Aid Drones In Smart Cities

11.4.5 Surveillance In Smart Cities

11.4.6 Intelligent Transportation in Smart Cities

11.4.7 Intelligent Control Of Capital And Properties

11.4.8 Navigation In Smart Cities

11.4.9 PARCEL, DELIVERY, OR CARRIAGE IoD SYSTEM IN SMART CITIES

11.4.10 AGRICULTURAL APPLICATIONS OF IoD UNITS

11.4.11 Data Collection In Smart Cities

11.4.12 GAMES AND ENTERTAINMENT IoD UNITS

11.4.13 IoD SYSTEM FOG COMPUTING

11.4.14 IoD DEVICES IMPROVING LIFE QUALITY IN SMART CITIES

11.5 Types of Drones

11.6 Conclusion And Future Scope

References

Part V Applications of Drones in Business and Disaster Relief Management

12. Internet of Drones Applications in Aviation MRO Business Services

12.1 Introduction

12.2 Literature Review

12.2.1 Analysis

12.2.2 Identification of Critical Factors in Mro Services

12.3 Problem Definition

12.4 Methodology And Analysis

12.4.1 Analysis

12.5 Results And Discussion

12.5.1 Inspection Of The Aircraft

12.5.2 Safety

12.5.3 Cost Reduction

12.5.4 Reduce Delivery Time

12.5.5 Data Security

12.6 Conclusion And Future Scope

References

13. Deploying Unmanned Aerial Vehicle (UAV) for Disaster Relief Management

13.1 Introduction

13.2 Drones in Nuclear Disaster: Case Study

13.3 Drones In Flood Disaster: Case Study

13.4 Drones In Wildfire Disaster: Case Study

13.5 Conclusion And Future Scope

References

Index

People also search for The Internet Of Drones AI Applications for Smart Solutions 1st Edition:

    
google drone internet
    
internet by drone
    
internet using drones
    
drones ai
    
are drones considered ai

Tags: Arun Solanki, Sandhya Tarar, Simar Preet Singh, Akash Tayal, AI Applications, Smart Solutions