Computational Intelligence for Unmanned Aerial Vehicles Communication Networks (Studies in Computational Intelligence, 1033) 3030971120, 9783030971120

Table of contents :
Preface
Contents
About the Editors
Machine Learning and AI Approach to Improve UAV Communication and Networking
1 Introduction
2 Literature Discussions
3 UAVs Characteristics
4 Artificial Intelligence and Machine Learning
4.1 Machine Learning Approaches
5 Unsupervised and Supervised ML for UAVs
5.1 Supervised-Based Learning
5.2 Unsupervised Learning Overview
6 Solution for UAVs-Based Issues
6.1 UAVs Coordination and Placement
6.2 Path Calculation
6.3 Virtual Reality in Drones
6.4 Abnormalities in Drone Monitoring
6.5 UAVs Detection
7 Interpretation and Future Practice
8 Conclusion
References
Implementation of Machine Learning Techniques in Unmanned Aerial Vehicle Control and Its Various Applications
1 Introduction
2 Classification of UAV
3 Unmanned Aerial Vehicle (UAV) Market Trends and Values
4 Machine Learning Techniques for UAV Applications
4.1 Linear Regression
4.2 Logistic Regression
4.3 Decision Tree (DT)
4.4 Random Forest (RF)
4.5 Support Vector Machine (SVM)
5 Applications of Machine Learning Techniques in UAV
6 Summary and Discussion
7 Conclusion
References
Machine Learning Techniques for UAV Trajectory Optimization—A Survey
1 Introduction
1.1 What is UAV?
1.2 Machine Learning with Artificial Intelligence
2 Survey Works
2.1 Issues on Physical Layer
2.2 Channel—Modeling
2.3 Interference Management
2.4 Configuration of Transmission Parameters
3 Resource Management and Network Planning
4 Open Issues
4.1 Implementation
4.2 Issues in Physical Layer
4.3 Issues in Security and Privacy
5 Conclusion
References
Metaheuristic Algorithms for Integrated Navigation Systems
1 Introduction
2 Expressing the Navigation Problem
2.1 Inertial Navigation
2.2 Integrated Navigation
3 Optimization by Using Metaheuristic Algorithms
3.1 Genetic Algorithm
3.2 Particle Swarm Optimization
3.3 Inclined Planes System Optimization
3.4 Modified Inclined Planes System Optimization
4 Metaheuristic Algorithms for Designing Integrated Navigation Systems
5 Results
6 Conclusions
References
Security Threats in Flying Ad Hoc Network (FANET)
1 Introduction
2 Overview of FANET
2.1 Network Topology
2.2 Mobility Models
2.3 Node Mobility
2.4 Node Density
2.5 Localization
2.6 Power Consumption
2.7 Radio Propagation Model
3 Literature Review
4 Security in FANET
5 Security Challenges
5.1 Dynamic Network Topology
5.2 High Mobility
5.3 Error Tolerance
5.4 Latency Control
5.5 Key Distribution
5.6 Data Consistency
5.7 Location Awareness
5.8 Need of High Computational Ability
5.9 Privacy
5.10 Routing Protocol
5.11 Network Scalability
6 Security Services
6.1 Availability
6.2 Confidentiality
6.3 Data Integrity
6.4 Authentication
6.5 Non-repudiation
7 Types of Attackers
7.1 Basis of Membership
7.2 Basis of Intention
7.3 Basis of Activity
7.4 Basis of Scope
8 Security Threats
8.1 Attack on Availability
8.2 Attack on Confidentiality
8.3 Attack on Data Integrity
8.4 Attack on Authentication
8.5 Attack on Non-repudiation
9 Solution for Security Threats
9.1 SEAD
9.2 Ariadne
9.3 RobSAD
9.4 ARAN
9.5 SAODV
9.6 A-SAODV
9.7 One Time Cookie
10 Conclusion
References
Secure Communication Routing in FANETs: A Survey
1 Introduction
2 Literature Review
3 Wireless Communication
3.1 Mobility Models
3.2 Time-Dependent Mobility Models
3.3 Routing Protocols in FANETs
4 Conclusion
References
Impact of Routing Techniques and Mobility Models on Flying Ad Hoc Networks
1 Introduction
1.1 Types of Networks
1.2 Traditional Network
2 Background Study
2.1 Mobile Ad Hoc Network (MANET)
2.2 Vehicular Ad Hoc Network (VANET)
2.3 Flying Ad Hoc Network (FANET)
2.4 Single, Multiple and Multiple-group UAVs Application Network
2.5 Classification of UAVs
2.6 Mobility Models
2.7 Routing Techniques
3 Conclusion and Future Direction
References
Analysis of Vulnerabilities in Cybersecurity in Unmanned Air Vehicles
1 Introduction
2 Motivation
3 Cybersecurity Threats
3.1 Spoofing
3.2 Tampering
3.3 Repudiation
3.4 Information Disclosure
3.5 Dos
3.6 Elevation of Privilege
4 Attacks
4.1 Spoofing Attack
4.2 Man in the Middle Attack
4.3 DoS Attack
4.4 Buffer Overflow Attack
4.5 Eaves Dropping Attack
5 Conclusion
References
Silent Listening to Detect False Data Injection Attack and Recognize the Attacker in Smart Car Platooning
1 Introduction
2 Related Works
2.1 Summary of Contributions
2.2 Chapter Organization
3 Research Method
3.1 FDI Attack Detection
3.2 FDI Attacker Recognition
3.3 Smart-Car Based Test Bed Creation for Sample Collection
3.4 Procedure
3.5 Instruments
3.6 Data Analysis Technique
4 Results and Discussion
4.1 Examining Correctness of Algorithms1 and 2
4.2 Brief Answers for RQs
5 Conclusion
References
Taxonomy of UAVs GPS Spoofing and Jamming Attack Detection Methods
1 Introduction
1.1 Motivation Based on the Statical Reports
1.2 Classification of UAVs
1.3 Design Considerations of UAV
2 Taxonomy of UAV Routing Protocols
2.1 Topology Based Routing
2.2 Position-Based Routing
2.3 Hierarchical Routing
2.4 Probabilistic Routing Protocols
2.5 AI-Enabled Routing Protocols
2.6 Deterministic Routing Protocol
2.7 Stochastic Routing Protocols
2.8 Social Network-Based Approach
3 Vulnerabilities in UAV
3.1 System-Related Vulnerabilities
3.2 Propagation Channel Vulnerabilities
3.3 Interference’s Vulnerabilities
4 GPS Spoofing and Jamming Attacks
4.1 GPS Spoofing Attack
4.2 Jamming Attack
5 Literature Survey
6 Conclusion
References
Investigation on Challenges of Big Data Analytics in UAV Surveillance
1 Introduction
1.1 UAV Surveillance
1.2 Application of UAV Surveillance
1.3 Importance of UAV Surveillance
2 Big Data Analytics
2.1 Importance of Big Data Analytics
2.2 Significance of Big Data Analytics in UAV Surveillance
3 Background Study
4 Challenges of Big Data Analytics in UAV Surveillance
4.1 Safety
4.2 Privacy
4.3 Security
5 Conclusion
References
UAV-Based Photogrammetry and Seismic Zonation Approach for Earthquakes Hazard Analysis of Pakistan
1 Introduction
2 UAVs Impacts in Hazard Analysis and Rescue-Based Mission
3 Seismicity of the Area
4 Regional Tectonic Setup
4.1 Main Karakoram Thrust
4.2 Main Mantle Thrust
4.3 Main Boundary Thrust
5 Neighbor Embedding for Seismic Zonation
5.1 Manifold Learning
5.2 Seismicity of Pakistan
6 Bilinear Interpolation for Seismic Zonation
6.1 Directional Bilinear Interpolation Approach
6.2 Results and Interpration from Pakistan Bilinear-Based Interpolation
7 Conclusion
References
Optimizing UAV Path for Disaster Management in Smart Cities Using Metaheuristic Algorithms
1 Introduction
2 Related Study
2.1 Abbreviations
3 Mathematical Modeling and Metaheuristic Algorithm
3.1 Problem Statement
3.2 Path Optimization Using SFOA
4 Case Studies with Discussion
4.1 Scenario 1: General Environment
4.2 Scenario 2: Condense Obstacle Environment
4.3 Scenario 3: Maze Environment
4.4 Scenario 4: Dynamic Environment
4.5 Performance Evaluation
5 Conclusion
References
UAV-Based Rescue System and Seismic Zonation for Hazard Analysis and Disaster Management
1 Introduction
2 Tectonic Setting of Kalabagh Area
2.1 Salt Range and Trans Indus Range Thrust
2.2 Surghar Fault
3 Approaches for Seismic Zonation of Kalabagh
3.1 Kriging Methodology
3.2 Cubic Convolution
4 Conclusion
References
Multi-sensor Fusion Methods for Unmanned Aerial Vehicles to Detect Environment Using Deep Learning Techniques
1 Introduction
1.1 Deep Learning in Object Detection
2 Multiple Sensors Fusion with CNN
2.1 ADAS (Advanced Driver Assistance System)
3 Multi-sensor Fusion Algorithm
3.1 Sensor Fusion Using FusionNet
3.2 Sensor Information Fusion Technology
3.3 CNN and Regression
4 Conclusion
References
General Parametric of Two Micro-Concentrator Photovoltaic Systems for Drone Application
1 Introduction
2 File Basic Concepts for Solar 4 Concentrators
3 Simulation Results
4 Conclusion
References

Citation preview

Studies in Computational Intelligence 1033

Mariya Ouaissa · Inam Ullah Khan · Mariyam Ouaissa · Zakaria Boulouard · Syed Bilal Hussain Shah   Editors

Computational Intelligence for Unmanned Aerial Vehicles Communication Networks

Studies in Computational Intelligence Volume 1033

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at https://link.springer.com/bookseries/7092

Mariya Ouaissa · Inam Ullah Khan · Mariyam Ouaissa · Zakaria Boulouard · Syed Bilal Hussain Shah Editors

Computational Intelligence for Unmanned Aerial Vehicles Communication Networks

Editors Mariya Ouaissa Moulay Ismail University Meknes, Morocco Mariyam Ouaissa Moulay Ismail University Meknes, Morocco Syed Bilal Hussain Shah Dalian University of Technology Dalian, China

Inam Ullah Khan Isra University Islamabad, Pakistan Zakaria Boulouard Faculty of Sciences and Techniques Mohammedia Hassan II University Casablanca, Morocco

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-97112-0 ISBN 978-3-030-97113-7 (eBook) https://doi.org/10.1007/978-3-030-97113-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Unmanned aerial vehicles (UAVs), or aerial drones, have aroused an increasing interest over the last few years. UAVs can be used in different fields, such as emergency communication networks, cartography, aerial photography, video transmission, military applications, modern agriculture, environment, parcel delivery, 3D modeling of soils and buildings, and Wi-Fi broadcasting. The range of UAV use cases and fields of application will be even wider thanks to the potential UAVs can present. Research or design in the field of UAVs is essentially multidisciplinary. Indeed, the design of a UAV requires and calls upon knowledge of several disciplines, in particular, electronics (especially on-board systems), automation, IT, mechanics, aeronautics, robotics, and telecommunications. Regarding communication areas, UAV-aided communication has been recognized as an emerging and promising technique in industry for its superior on flexibility and autonomy. To assist 5G communications, promising research scenarios can be as follows: establishing temporal communication infrastructure during natural disasters, offloading traffic for dense networks, and data collection/processing for supporting Internet of Things (IoT) networks. Recent progress in unmanned aerial vehicles and computational intelligence constitutes a new chance for an autonomous operation and flight. Nowadays, artificial intelligence and deep learning are driving the evolution of UAVs and fuelling their autonomous future. Computer vision achieved a very important progress in image classification, segmentation, and object detection, which makes it a very attractive research field when applied on unmanned aerial vehicles. As much as artificial intelligence can be an important and beneficial asset to improve UAV performances, it can also be dangerous and a serious matter when the UAVs learning is not managed correctly. This book aims to provide a vision that can combine the best of both AI and communication networks for designing the deployment trajectory to establish flexible UAV communication networks. This book is a collection of 16 original contributions that will discuss the major challenges that can face deploying unmanned aerial vehicles in emergent networks. It v

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Preface

will focus on possible applications of UAVs in a Smart City environment where they can be supported by IoT, wireless sensor networks, as well as 5G, and beyond. This book will discuss the possible problems and solutions and the network integration of the UAVs, and compare the communication technologies to be used. In Chapters Machine Learning and AI Approach to Improve UAV Communication and Networking, Implementation of Machine Learning Techniques in Unmanned Aerial Vehicle Control and Its Various Applications, Machine Learning Techniques for UAV Trajectory Optimization—A Survey and Metaheuristic Algorithms for Integrated Navigation Systems, the authors present a survey on different artificial intelligence and machine learning-based approaches to optimize UAV’s potential with better communication, better control, and better trajectory management. In Chapters Security Threats in Flying Ad Hoc Network (FANET) and Secure Communication Routing in FANETs: A Survey, the authors present the concept of flying ad hoc networks (FANETs) and different approaches to secure their communications; while Chapter Impact of Routing Techniques and Mobility Models on Flying Ad Hoc Networks focuses on the impact of these approaches on FANETs. Chapters Analysis of Vulnerabilities in Cybersecurity in Unmanned Air Vehicles–Taxonomy of UAVs GPS Spoofing and Jamming Attack Detection Methods try to analyze vulnerabilities in cybersecurity in UAV networks; while Chapter Investigation on Challenges of Big Data Analytics in UAV Surveillance investigates the role of Big Data in UAV surveillance. The other chapters cover real-world success stories of the role of UAV in different aspects of “Smart Cities”. In Chapters UAV-Based Photogrammetry and Seismic Zonation Approach for Earthquakes Hazard Analysis of Pakistan–UAV-Based Rescue System and Seismic Zonation for Hazard Analysis and Disaster Management, the authors provide concrete examples of the role of UAVs in the protection from natural disasters such as seism. Chapters Multi-sensor Fusion Methods for Unmanned Aerial Vehicles to Detect Environment Using Deep Learning Techniques and General Parametric of Two Micro-Concentrator Photovoltaic Systems for Drone Application try to cover the environmental aspect of UAVs either with their energy management or their interaction with their surroundings. Meknes, Morocco Islamabad, Pakistan Meknes, Morocco Casablanca, Morocco Dalian, China

Mariya Ouaissa Inam Ullah Khan Mariyam Ouaissa Zakaria Boulouard Syed Bilal Hussain Shah

Contents

Machine Learning and AI Approach to Improve UAV Communication and Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bisma Baig and Abdul Qahar Shahzad

1

Implementation of Machine Learning Techniques in Unmanned Aerial Vehicle Control and Its Various Applications . . . . . . . . . . . . . . . . . . . E. Fantin Irudaya Raj

17

Machine Learning Techniques for UAV Trajectory Optimization—A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sasikumar Rajendran, Karthik Kandha Samy, Jeganathan Chinnathevar, and Deva Priya Sethuraj

35

Metaheuristic Algorithms for Integrated Navigation Systems . . . . . . . . . . Ali Mohammadi, Farid Sheikholeslam, and Mehdi Emami

45

Security Threats in Flying Ad Hoc Network (FANET) . . . . . . . . . . . . . . . . . Safia Lateef, Muhammad Rizwan, and Muhammad Abul Hassan

73

Secure Communication Routing in FANETs: A Survey . . . . . . . . . . . . . . . . Shaheen Ahmad and Muhammad Abul Hassan

97

Impact of Routing Techniques and Mobility Models on Flying Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Muhammad Abul Hassan, Muhammad Imad, Tayyabah Hassan, Farhat Ullah, and Shaheen Ahmad Analysis of Vulnerabilities in Cybersecurity in Unmanned Air Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mohammad Ammar Mehdi, Syeda Zillay Nain Zukhraf, and Hafsa Maryam

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Contents

Silent Listening to Detect False Data Injection Attack and Recognize the Attacker in Smart Car Platooning . . . . . . . . . . . . . . . . . 145 Sharmistha Majumder, Mrinal Kanti Deb Barma, Ashim Saha, and A. B. Roy Taxonomy of UAVs GPS Spoofing and Jamming Attack Detection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 A. Sabitha Banu and G. Padmavathi Investigation on Challenges of Big Data Analytics in UAV Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 N. Vanitha, G. Padmavathi, P. Nivedha, and K. Bhuvana UAV-Based Photogrammetry and Seismic Zonation Approach for Earthquakes Hazard Analysis of Pakistan . . . . . . . . . . . . . . . . . . . . . . . . 211 Abdul Qahar Shahzad and Mona Lisa Optimizing UAV Path for Disaster Management in Smart Cities Using Metaheuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Zakria Qadir, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Khoa N. Le, and Vivian W. Y. Tam UAV-Based Rescue System and Seismic Zonation for Hazard Analysis and Disaster Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Abdul Qahar Shahzad, Mona Lisa, Mumtaz Ali Khan, and Irum Khan Multi-sensor Fusion Methods for Unmanned Aerial Vehicles to Detect Environment Using Deep Learning Techniques . . . . . . . . . . . . . . 263 Pradeep Duraisamy, Venkatesh Babu Sakthi Narayanan, Ramya Patturajan, and Kumararaja Veerasamy General Parametric of Two Micro-Concentrator Photovoltaic Systems for Drone Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Sarah El Himer, Mariyam Ouaissa, Mariya Ouaissa, and Zakaria Boulouard

About the Editors

Dr. Mariya Ouaissa is a Researcher Associate and Practitioner with industry and academic experience. She is a Ph.D. graduated in 2019 in Computer Science and Networks, at the Laboratory of Modelisation of Mathematics and Computer Science from ENSAM-Moulay Ismail University, Meknes, Morocco. She is a Networks and Telecoms Engineer, graduated in 2013 from National School of Applied Sciences Khouribga, Morocco. She is a Co-founder and IT Consultant at IT Support and Consulting Center. She was working for School of Technology of Meknes Morocco as a Visiting Professor from 2013 to 2021. She is member of the International Association of Engineers and International Association of Online Engineering, and since 2021, she is an “ACM Professional Member”. She is Expert Reviewer with Academic Exchange Information Center (AEIC) and Brand Ambassador with Bentham Science. She has served and continues to serve on technical program and organizer committees of several conferences and events and has organized many symposiums/workshops/conferences as a General Chair also as a reviewer of numerous international journals. Dr. Ouaissa has made contributions in the fields of information security and privacy, Internet of Things security, and wireless and constrained networks security. Her main research topics are IoT, M2M, D2D, WSN, cellular networks, and vehicular networks. She has published over 20 papers (book chapters, international journals, and conferences/workshops), 5 edited books, and 5 special issue as guest editor. Dr. Inam Ullah Khan was a Lecturer at different universities in Pakistan which include Center for Emerging Sciences Engineering and Technology (CESET), Islamabad, Abdul Wali Khan University, Garden and Timergara Campus and University of Swat. Recently, he is selected as a visiting researcher at King’s College London, UK. He did his Ph.D. in Electronics Engineering from Department of Electronic Engineering, Isra University, Islamabad Campus, School of Engineering and Applied Sciences (SEAS). He had completed his M.S. degree in Electronic Engineering at Department of Electronic Engineering, Isra University, Islamabad Campus, School of Engineering and Applied Sciences (SEAS). He had done undergraduate degree in Bachelor of Computer Science from Abdul Wali Khan University Mardan, Pakistan. ix

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About the Editors

Apart from that, his Master’s thesis is published as a book on topic “Route Optimization with Ant Colony Optimization (ACO)” in Germany which is available on Amazon. He is a research scholar; he has published some research papers at international level. More interestingly, he recently introduced a novel routing protocol E-ANTHOCNET in the area of flying ad hoc networks. His research interest includes network system security, intrusion detection, intrusion prevention, cryptography, optimization techniques, WSN, IoT, UAVs, mobile ad hoc networks (MANETS), flying ad hoc networks, and machine learning. He has served international conferences as technical program committee member which include EAI International Conference on Future Intelligent Vehicular Technologies, Islamabad, Pakistan and 2nd International Conference on Future Networks and Distributed Systems, Amman, Jordan, June 26–27, 2018, and now recently working on the same level at International Workshop on Computational Intelligence and Cybersecurity in Emergent Networks (CICEN’21) that will be held in conjunction with the 12th International Conference on Ambient Systems, Networks and Technologies (EUSPN 2021) which is co-organized in November 1–4, 2021, Leuven, Belgium. Dr. Mariyam Ouaissa is a Ph.D. Researcher Associate and Consultant Trainer in Computer Science and Networks from Moulay Ismail University Meknes, Morocco. She received her Ph.D. degree in 2019 from National Graduate School of Arts and Crafts, Meknes, Morocco and her Engineering Degree in 2013 from the National School of Applied Sciences, Khouribga, Morocco. She is a communication and networking researcher and practitioner with industry and academic experience. Dr. Ouaissa’s research is multidisciplinary that focuses on Internet of Things, M2M, WSN, vehicular communications and cellular networks, security networks, congestion overload problem, and the resource allocation management and access control. She is serving as a reviewer for international journals and conferences including as IEEE Access, wireless communications, and mobile computing. Since 2020, she is a member of “International Association of Engineers IAENG” and “International Association of Online Engineering”, and since 2021, she is an “ACM Professional Member”. She has published more than 20 research papers (this includes book chapters, peer-reviewed journal articles, and peer-reviewed conference manuscripts), 5 edited books, and 5 special issue as guest editor. She has served on Program Committees and Organizing Committees of several conferences and events and has organized many symposiums/workshops/conferences as a General Chair. Dr. Zakaria Boulouard is currently a Professor at Department of Computer Sciences at the “Faculty of Sciences and Techniques Mohammedia, Hassan II University, Casablanca, Morocco”. In 2018, he joined the “Advanced Smart Systems” Research Team at the “Computer Sciences Laboratory of Mohammedia”. He received his Ph.D. degree in 2018 from “Ibn Zohr University, Morocco” and his Engineering Degree in 2013 from the “National School of Applied Sciences, Khouribga, Morocco”. His research interests include artificial intelligence, big data visualization and analytics, optimization and competitive intelligence. Since 2017, he is a member of “DraaTafilalet Foundation of Experts and Researchers”, and since 2020, he is an “ACM

About the Editors

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Professional Member”. He has served on Program Committees and Organizing Committees of several conferences and events and has organized many symposiums/workshops/conferences as a General Chair. He has served and continues to serve as a reviewer of numerous international conferences and journals. He has published several research papers. This includes book chapters, peer-reviewed journal articles, peer-reviewed conference manuscripts, edited books, and special issue journals. Dr. Syed Bilal Hussain Shah is currently Adjunct Professor at SKEMA Business School Nanjing Audit University, China. He was Postdoctoral Researcher at the School of Software, Dalian university of Technology, P.R. China. He has got Bachelor degree in Computer Sciences (2007) from University Department of Computer Sciences University of Peshawar, Pakistan. He Joined Bahria University Islamabad, Pakistan, for Masters in Telecommunication and Networking (2009). He completed his Ph.D. from Dalian University of Technology, P.R. China. He has worked as Lecturer at Department of Computer Sciences University of Peshawar (2010–2012) Pakistan. He has authored/co-authored 50+ research papers in reputable journals and conferences such as peer-to-peer networking and applications, future generation computer systems IF, sustainable cities and societies, etc. Furthermore, published papers in ACM, IEEE, and Springer conferences. Also presented his paper in conference, Cambridge, UK July 19, 2017. Main research interests include wireless sensor network, IoT, throughput optimization in WSN, node localization, energy-efficient routing in smart wireless sensor networks, distributed and centralized clustering in WSN, IoT, blockchain, opportunistic networks, and Industry 4.0 technology.

Machine Learning and AI Approach to Improve UAV Communication and Networking Bisma Baig and Abdul Qahar Shahzad

Abstract With the advent of unmanned aerial vehicles (UAVs) several sector of life has been improved. Currently, numerous researches are carried out to enhance UAV capabilities. UAVs are frequently utilized in several life-threatening operations such as rescue, surveillance and transportation. Apart from this, drones-based experiments are conducted in geology, wildlife, safety and ecological protection. Additionally, 5th generation approach which is consists of huge networks, high consistency and transmission rates assist in UAVs. However, to attain such goals is business challenge for rapidly evolving Internet of Things (IoT), particularly in most dynamic and mobile environments. Therefore, utilized in emergency where UAVs ensures rapid recovery and tackling heavy traffic situations. These characteristics have attracted the attention of organizations and academia. Moreover, machine learning (ML) and artificial intelligence (AI) approaches are integrated into network where information is used to solve problems. Thus, combination of ML with AI operates applications. Furthermore, entire operation performance is enhanced. In this chapter, UAVs with machine learning approaches are discussed. Study covers gaps in previous research which influenced existing technique. In different context, machine learning (ML) has recently become a subdomain of artificial intelligence. Keywords IoT · UAV · AI · ML

1 Introduction UAVs become very popular in recent years due to their basic characteristics which include maneuverability, positioning, and capability to communicate with users within line of sight (LOS). This aroused the interest of researchers. UAVs are basically B. Baig COMSATS University Islamabad, Abbottabad Campus, Islamabad 45550, Pakistan A. Q. Shahzad (B) Department of Earth Sciences, Quaid-I-Azam University, Islamabad 45320, Pakistan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Ouaissa et al. (eds.), Computational Intelligence for Unmanned Aerial Vehicles Communication Networks, Studies in Computational Intelligence 1033, https://doi.org/10.1007/978-3-030-97113-7_1

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divided into two categories. One is fixed-wing UAVs and second is helicopter-based UAVs. Both types of UAVs are suitable for a distinct type of application. As fixed wing UAV is suitable for stationery-free missions, for example: military applications like assault and reconnaissance. Nevertheless, the UAVs on the rotor have complicated aerodynamics. You can stay in certain locations as well, but you will not be able to complete long-term missions. Furthermore, rotating-wing is ideal for providing short-term reporting to operator. The engagement of different companies for aircraft production carriers have reduced the market value, and the usage of UAV networks doesn’t look like a dream or as an opinion for the future. Indeed, these are mostly used for radio communications, agriculture, distribution, and reconnaissance. There may be some restrictions on the use of UAVs. For example, visual LOS usage in order to mitigates vulnerabilities in bad weather and dangerous loss of control. More importantly, drone-limited batteries and low computing power are considered the main limitations. In fact, UAVs on market find it difficult to take several hour flights and return to base for batteries recharging. Moreover, restricted simulation power at UAV prevents the execution of complex embedded algorithms that require high CPU and GPU power. So, from scientific aspect complexity can affect its feasibility, so a specific UAV problem is not always a good solution [1]. In addition, the large amount of data available today, multi-performance simulation (MPS) and the availability of better GPUs allowed AI to monitor brightness of day. Consequently, machine learning used in many areas nowadays, even beyond the expectation. Numerous sub-categories of AI are observed such as condensedlearning, deep-learning, and blended-learning for certain issues. For instance, artificial intelligence branch of DL utilize covers to mimic individual brain. Furthermore, RL which is usually utilize in simulation, speech, and language recognition. This part of AI introduced in 1979. Here, agents learn to do good deeds in order to receive the greatest reward. The learning process can be accomplished by exploring and exploring the various states available. Machine learning branch RL is considered to evolve spontaneously as compared to other branches. Dissimilar to other, RL utilize in field of robotics to learn how to plan routes and complete difficult jobs. Such contribution of RL did not restrict the robotics field. Considerably, RL procedure assists in main functions of making decision where target agents take into account when intermingling with new prospective. However, FL is latest field in machine learning primarily offered in 2016 by Google to sustenance distributed data. Machine learning FL setup is consider for training highly centralized models on devices that share distributed data without sending data to locally shared blocks. That is, utilize to operate machine learning approaches in distributed information design. Therefore, this technique considered as safe to do and using FL is a very hot topic for UAV-based networks [2].

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2 Literature Discussions Neutrally, disparage some previous research, this portion mainly focus on the machine learning as how it improve the general performance of UAV in issues regarding wireless network. Firstly, machine learning tool is utilize to solve different sort of complex issues and provide direction to tackle upcoming targeted problems. Such techniques emphasis on the ML prospective in dealing with certain difficulties where some literature, it is mixed with automatic learning [3]. Furthermore, the concept of solving machine learning is crystal clear as its finding is more reliable and optimal when compared with other empirical approaches. Here it advances some criticism: are ML techniques in dealing with problems better than convectional approaches? Although previous successful experiments illustrates that ML approach in solving issues is always superior than others, still there are many gaps which needed to be fully addressed. While assessing the accuracy of ML technique it is essential that data set is fully developed and having all the prospective which needed to be solved. For instance, when working on the image to determine whether it contain UAV or the number of nodes it contain with accurate positioning. This sort of work is considered as visual detection of image through ML approach. Machine learning investigates the image behavior through several methods to determine what the image originally has? Here the proposed concept is utilized to find out the target using the machine learning tool. In case the provided images have not numerous drones. Definitely, CNN offers best way to find out through AI and ML approaches [4]. Other shortcomings of ML can be found in several articles comparing methods from a performance perspective. For example, when comparing the CNN architecture, when comparing two machine learning models, the calculation is to find out why one model is better than another model and why the NN architecture is better than the other. You may not see that there is no explanation. This point is typical for ML black boxes. That is, it includes the parameters for adjusting and evaluating the results and cannot be described elsewhere. As a result, it may not be possible to predict which models will be used and which will not be promising for a particular problem [8]. ML is another interesting option, however, especially for UAV problems. So I’m sure I can work on other ideas in the future. In fact, more complex ML models can be tested with some UAV problems. For example, when predicting path authorization, you can test several regression tools for this problem. I also know that the UAV detection problem is solved by sound or image detection and this is an automatic vision problem. On the other hand, with ML, if the NNs match (for example, provide CNN type for images, RNN type for audio, radio), the last NN is used to rank the output to estimate each input type. In addition, I noticed that I tend to use supervised learning algorithms when troubleshooting UAV.

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3 UAVs Characteristics As broadband request increases, universal reportage, access, local networks actively sustenance existing terrestrial loopback-systems. A key component of NTN, the Low Altitude Platform (LAP), aims to facilitate a variety of civilian, commercial and governmental IoT missions and applications, from security operations and military services to amusement and broadcastings. Unmanned aerial vehicles, which are the typical main type of CPU, are usually utilized for a short period of time (numerous hours). This allows backbone of several link communications to be quickly implemented in complex applications without public security personnel, and rescue missions after natural disasters or unforeseen events, photo exploration. Market research predicts UAV sales may approximately exceed $14 billion annually by 2022. However, Federal Aviation Administration predicts number of UAVs may reach to 2.4 million by 2022. The size and dynamics of the market are clearly the motivating strength behind the development of UAVs. Recently, regulators, industry, and academia have made a strong commitment to use drones as base stations, mobile repeaters, or autonomous communications hubs to provide reliable, lowlatency communications in urban and suburban areas. The 3rd Generation Partnership Project (3GPP) [1] proposed aircraft utilization for long-term evolution and IEEE 802.16 Relay Task Force introduced the concept of roaming relays. In 2013, the Special Committee (SC-228) established a viable UAV framework established by Radio Aeronautical Commission to formulate the technical characteristics of UAV operations. In addition, in 2016, RTCA established the Unmanned Aerial Vehicle Advisory Board to safely introduce unmanned aerial vehicles into the national aerospace system. FAA and NASA launched combined initiative to assimilate UAVs into US warfare systems [2]. From manufacturing perspective, main sellers such as Microsoft, Google, Facebook, and YouTube tested 4G and 5G antenna platforms for LTE uses. Depending on flight system, drones are divided in RPVs multi-rotor drones (helicopter based drones), winged drones, front-wing/hybrid drones, robots, and unmanned aerial vehicles. They range tiny toys to huge aircraft. In addition, UAVs charge for communication equipment, cameras, radar, sensors, etc. from thousands to milligrams in order to control the size and trip time. Due to exclusive features, drone can achieve high coverage at high altitudes and high altitudes, and can provide economical airflow in all areas using a large linear connection space (LoS) that moves quickly in deployment and the movement is on demand [5]. In addition to using a small number of UAVs, UAV samples can work together to perform complex tasks over very large areas, especially for monitoring and surveillance applications, but fly in special networks. (FANET) When most UAVs communicate ad hoc, connectivity and coverage can be achieved in situations of constraints of the terrestrial network: remote sites, highly mobile and distributed sites. They can be expanded effectively. However, while UAV interference must be effectively mitigated for successful UAVs operation built systems, UAV motion, supply supervision, and governor are primarily based on UAV concentration. There is a problem with the variety of types. The same is true for interoperability and positioning between

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different wireless networks. In addition, not only is the UAV’s capacity limited in terms of network load and onboard processing, but the need for engine power and flying device is crucial real-world aspects limiting large-scale use of UAV. In this regard, it is of great concern to extend the life of the drone, which is highly dependent on flight performance and operation constraints. However, associated to global wireless systems, UAV based network has unique and special structures which include: topology, orbital paths and poorly attached nodes. Due to control constraints, the energy-efficient model of onboard schemes requires route planning and battery programming to extend range. In addition, mobility and Doppler forwarding can be improved accordingly, and the quality of service (QoS) for information transmission is distorted. In general, communications must be tailored to speed and QoS transfers to attain preferred goals. Furthermore, current traditional communication methods have fundamental limitations, especially in multipart situations where unpredicted non-linear phenomena overcome. Consequently, mission-based, vibrant and acute communications which lead to intricacy, ambiguity, and higher levels of unpredictability, the ML/AI has radically different decision-making abilities to find the correct UAV location and trajectory. It is an important technology that it provides [6].

4 Artificial Intelligence and Machine Learning Artificial intelligence is considered to be scientific machines learning to execute human-based responsibility. Artificial intelligence uses a variety of applications such as speech recognition, robotic vehicles, machine-based interpretation, and communication. Furthermore, the techniques used to teach machines how to learn are a special subset of artificial knowledge, latest structure called Machine Learning. ML offers situations-based solution where numerous devices require simultaneous entrance to system properties, for example during Internet of things data exchange. Intellectual control across the network is required to meet the different needs of this new type of service. The goal is to manage network resources as best as possible. Therefore, machine learning procedures projected as effective method to solving conflicting problems that arise from IoT bionetwork. Generally, machine learning based on design background, the principal concept of which used in correlations between previous datasets and a set of good deeds to adapt to changes in the environment without human intervention. Obviously, the advantage of wireless network machine learning frameworks is that network elements can track, study, and forecast numerous communiqué factors which include traffic patterns, wireless behavior, and tool locations. DL is special machine learning class. DL uses multiple layers to create artificial neural networks, allowing smart decisions to be made without humanoid interference. The deep learning algorithms are used in restricted intervention needed but require higher computing requirements. However, ML, DL and AI techniques are extensively used in numerous wireless situations to improve many network parameters.

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4.1 Machine Learning Approaches Machine learning falls into several categories, including controlled, semi-controlled, and unendorsed learning with reinforcement [7].

4.1.1

Supervised Learning

Controlled based learning, algorithm uses a set of data. Both the required inputs and outputs are available in this dataset. Therefore, this type of algorithm can only be used when there is a large amount of tagged data.

4.1.2

Un-supervised Learning

Unconfirmed algorithm requires learning information, but lacks a labeled result. Such sort of training performs pattern or collecting detection in the available data.

4.1.3

Semi-supervised Learning

The semi-supervised algorithm takes transitional method to type of data presented. This type of training uses tagged and untouched data for training.

4.1.4

Reinforcement Learning

In RL, a series of actions using trial and error rules solves the problem. Thus, basic concepts of training are very dissimilar from former idea of using historical data. Instead, RL algorithm specializes in previous solutions on how to solve the problem. The RL algorithm is used in a variety of situations in arena wireless network optimization.

5 Unsupervised and Supervised ML for UAVs Recent ML phrase associated to artificial intelligence. This subcategory of artificial intelligence allows computers perform accurately which is based on knowledge expanded by studying few of the above cases. Machine learning experimentation is conducted fruitful in previous period, thanks to amount of data accessible and simulation of today. Research is focused on relating machine learning to solve UAV-related problems.

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Fig. 1 Machine learning operational strategy types

ML areas are classified in dissimilar problem classes. For example, ML is distributed into three categories such as supervised, unsupervised, and RL-based issues as shown Fig. 1. Therefore, we will distinguish between knowledge areas to avoid further misperception.

5.1 Supervised-Based Learning Through this approach provided information is flagged. That is, it provides a true baseline value for each data item so that the algorithm can learn to use those values to determine new unlabeled items. For example, predict the price of a UAV based on features. Here, algorithm is needed to offer along with dataset which include the characteristics of individually drone with related. Datasets are generally divided into training and testing sets. Supervised activities are often divided into graduate activities or activities. Step-by-step activity remedies for the right to comment (such as price forecasts). However, the problem of classification provides cut-off values that indicate which class an entry belongs to (e.g. classification of benign or malignant tumors). Below is the most common machine learning statistic for controlled and unsubstantiated learning. Also, it focuses on procedures utilize to solve UAV-related difficulties stated in study.

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5.1.1

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Related Division and Algorithms

Numerous algorithms are utilized for division and recession. For example, Support Vector Machine performs dual approaches. You can also generate a decision tree to resolve regressions or classifications depending on your use case.

5.1.2

Recession Algorithm

This approach utilizes algorithm which performs reversion. However, expecting uninterrupted output of values is assigned.

5.1.3

Classifier Algorithm

Explain the basic concept of pure classifier Machine Learning. Some sources claim that a naive Bayesian classifier with “some modifications” can be used for regression, but since it was originally derived for Bayesian theorem classification.

5.1.4

Multilayer Perceptron (MLP)

RNA is mathematically formulated for machine learning to simulate human biological neural networks. ANN-based built-in on series of partly linked drones called perceptron are gathered at dissimilar levels. However, detectors are accountable for handling inbound and outbound delivery information.

5.1.5

Convolution Neural Networks (CNN)

Generally, CNN is broadly utilizes for the purpose of image detection. However, method for understanding Natural Language Processing (NLP) and speech recognition is carried out. Where, the output and input layers are artificial neurons.

5.1.6

Recurrent Neural Networks (RNN)

If information is consistent, RNN is performed to correct the problem. For example, you can browse text speeches, videos, or audio recordings. This approach is broadly utilized for speech recognition, language interpretation, and language processing.

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5.2 Unsupervised Learning Overview Recent ML phrase associated to artificial intelligence. Below are the unsupervised algorithms.

5.2.1

Clustering Algorithm

ML has some common aggregation algorithms. These are Gaussian Combustion Module and Agglomeration. Algorithms based on density like DBSCAN, while others act as strong associations like combustion. However, GMM is utilized to modify association rules.

5.2.2

Dimension Reduction

Dimension reduction is classic machine learning method performed by altering information from multidimensional 3D illustration to low dimension. Here, we will discuss spectrum methods which include Auto encoder (AE), a type of neural network used to explore and encode the representation of data. Surprisingly, the EC architecture is very simple. Another common spectrum-based algorithm, principal component analysis (PCA), is also referred to general procedure.

5.2.3

Generative Adversarial Networks

This approaches architecture utilized two basic system to create examples of information transmitted as real-time. It is typically used to create images, videos and audio [8].

6 Solution for UAVs-Based Issues Main issues in UAVs are related to its communications, coordination and interaction with each other.

6.1 UAVs Coordination and Placement The researchers monitor antenna stations to reduce the load on earth base stations while minimizing drone power consumption. MAL assistance is considered a given solution because the UAV does not have to keep moving forward, but is used

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temporarily to wait for the wireless network congestion. Expected congestion is due GMMS. This could be a previously identified and unsupervised ML series model. For example information utilizes the Gaussian scattering. Firstly, the averaging procedure distributes consumers in K-means groups, where maximum weight prediction approach for group in order to achieve optimum factors of GMMM model. Furthermore, reduction of distribution is carried out through fixing UAV energy issue. Mathematical approach illustrate the finding of machine learning is more important than the classical solution at the cost of reducing the mobility and energy required for the links. Combining machine learning and optimization techniques is critical, but when you use the K-mean algorithm to categorize users, you can manually select the number of K groups and initiate a cluster center location. But the question is that, “How to do it?” Several authors and researchers have explored the optimal placement of UAVs as base stations by constructing structured radio maps. Due to multifaceted behavior of arena with complication in radio map usage, the author proposes grouping and joint regression problems using a maximum probability approach of tracking design. Also, machine learning approach utilize channel forecast for radio map reconstruction [9]. Use machine learning technology to predict loads with the maximum weight expected. This machine learning technology is compared to the maximum algorithm for optimizing expectations and the k-capacity algorithm for performance. In addition, contract theory is used to ensure that load demand is met by choosing the right UAV for each access point. Researcher explored the creation of routes for UAV flight routes based on previous flight records. The problem is that the mistakes made by pilots in the past will affect future UAV flight routes. To this end, the authors applied an EU-based unsupervised learning method to eliminate pilot errors and restore images generated from flight recordings. The proposed method was compared with path generation using the K resource algorithm and proved to be efficient. Efficiency amongst the base station and the UAV enhanced through forecasting drone position in consideration of the previous position. In fact, during the unloading of base stations on the ground, UAVs can be exposed to wind turbulence, which causes delays and loss of capability. In order to tackle such issues, researcher suggests (RNN) structure in which subsequent parallel angle and height of drone respect to ground-station is projected through postangle. Such technique generates location-specific predictions in fast-moving UAVs. The author constantly changes factors which include concealed number and level to investigate predictions correctness. Mathematical approach illustrate correctness is achieved in sixteen secret drones. Drone route plan are proposed in the published literature [10].

6.2 Path Calculation You may be wondering how ML can evaluate and model complex UAVs and established empirical models used to complicate UAV communication links. In this regard,

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forecast of drone to drone route failure. Forecasts obtained through KNN (next neighborhood) and the Forest Random algorithm is compared with the statistical analysis. Expected route failure is based on many factors which include length, height, and altitude. Comparing results obtained with the ray tracing software, it can be seen that ML did a good job with this planned activity. Because millimeter-wave bandwidth is used in next-generation mobile systems to increase bandwidth, they used neural networks to predict ATS channel conditions between two basic stations. Level Base Station (ii) Roof antenna base station. The first neural network classifies the connection type (LOS/NLOS/off) sends this information to the second neural network to generate different channel parameters. Some authors used a GAN to characterize the air-ground link for millimeter-wave communication in a wireless UAV network. The free distributed architecture aims to provide training for distributed UAV networks. The training process is based on a distributed data set to measure the channel. The machine-supervised study was used in another study to predict the quality of the relationship between UAVs and ground glands. For example, ANN is used to predict road losses for. ANN is used for predicting UAV signal strength and speculates propagation in the channel. The ANN scale is designed to analyze the effects of several common factors on signals, such as contrast, reflection, and diffusion. The right side includes restrictions like distance to the UAV, altitude, time, lost route, and more. This intriguing task could disrupt the extensive data processing time at ANN. Meanwhile, signal power amongst drones with base node is formulated through this approach. Author examines built-up setting where signal power information is utilized in order to provide ANN flow. While consuming information, factor is precisely predicted. Additionally, this approach is utilized with machine learning strategy to forecast strength of the signal receive through the aircraft from the mobile base station [11–13]. In short, an unsupervised study has been used to simulate 3D channels amongst drone with cellphone operators. This problem uses Gaussian methodology to classify LOS and NLOS references. This work uses means procedure to categorize LOS and NLOS references.

6.3 Virtual Reality in Drones This approach explains the real environments for various purposes which include amusement and learnings. Machinery commenced to attract considerable attention in recent times and recently observed an interesting line of research linking to merge the virtual reality with drone system. Artificial intelligence utilized solves numerous problems of implementing virtual reality. This approach acquires optimal transmission which includes small invisibility and great information proportions. Whereas, some cases, researchers recommend a VRUAV evaluate efficiency of numerous DL solutions. Some are investigating whether UAV-IoT networks could be used to immerse themselves in virtual reality remotely. Unmanned aircraft are placed

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Fig. 2 Machine learning operational strategy

in areas of particular interest, assuming different perspectives and sending them to specific aggregation points. Accumulation is carried out to include virtual reality at operator geo-positioning. Main objective of this VR is to enhance accuracy in specific circumstances. In this task, we used the RL method to find the best UAV location for maximum immersion accuracy. The DL method is transmitted and submitted, and RL approach associated to the latest existing mathematical based method. Furthermore, obtained results were compared where the former statistical approach show high efficiency [14].

6.4 Abnormalities in Drone Monitoring UAVs often suffer from the sensitivity to any anomalies that might occur when a drone is in operation. To avoid this situation, offered information through UAV sensor to monitor flight safety level. However, irregularity finding is considered as common procedure where involves identifying irregular information systems in the operating system. In Fig. 2 some researchers devises an uncontrolled algorithm for detecting adverse events that occur in drone testing [8].

6.5 UAVs Detection As UAVs are used by both civilians and the military, authorities control certain applications in which UAVs can be used for espionage or as deadly weapons. Therefore, UAV detection and monitoring is essential to prevent these threats. Many research services in this field develop different methods, sharing them in image and audio based solutions. One way to solve UAV detection problems is to use visual representation such as object displayed-images. Machine learning architecture of various

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depths was compared with the visual detection task of a UAV through specific image capturing tool [15]. Additionally, some articles explore some of the machine vision techniques used to detect UAVs. Finally, emphasize on creating cross scheme which utilize images, audio signals, and radio together would be a very good idea in the future.

6.5.1

Drone Photogrammetry

Visual-based surveillance considered being outside this specific field, but there are several studies in the literature related to UAV imaging. For example, detection of mandatory (emergency) safely landing on sites. Discovery turns into cataloguing issue in which dual well-known classifiers such as SVM and GMM are tested. These turns into 3D view to secure/insecure lattice record. Furthermore, filtration is carried out in order to eliminate the hazard of ground base station. Specifically, main objective of this type problem was not measured in such interrogation that it can only be considered visual-based surveillance but only image is not capture from specific elevation, it could be some sort of task to apply to. In other words, techniques which include feature-based extraction, CNN, and boundary indicators are used to capture images from UAVs [16].

7 Interpretation and Future Practice The use of machine learning technology in UAVs systems are problematic due to restricted on-board computing capabilities. In fact, most off -the -shelf UAVs do not require complex processors to execute complex machine learning procedures. However, proposed UAV design with authoritative processor, simulator, and GPU, you still need to consider their complete cost with weight. Consequently, UAV power limitations continue to cause similar problems. By using the cloud to train models and drawing conclusions at the UAV level, we can find a solution to the problem. Although this specific elucidation enhances communiqué costs, and the UAV must alternately communicate with the cloud, returning to the problem of energy constraints. So using ML on board is another good solution, but this time set the ML algorithm to UAV power limit. This approach takes us to a new area, commonly known as device learning, especially for disabled devices. In addition to the limitations of the UAV hardware and software cited overhead where actual utilization of machine learning in Adhoc system poses numerous serious obstacles to the current principals. Whereas, this study focused on the use of partial or stand-alone UAVs, in general the existing regulatory requirements do not allow for such real operations. However, this is crucial to note that there are areas which need improvement. Dissimilar to FAA, latest regulation of the European Aviation Safety Agency (EASA), published in December 2020, operates autonomous UAVs, including classification according to level of application threat. Therefore, it ensures

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novel prospects for advancement in Adhoc network system elucidations based on machine learning and artificial intelligence in general term [17]. Finally, this is significant to synchronize and integrate regulations for specific application of UAVs around the world to stimulate future research [18].

8 Conclusion To conclude, a machine learning framework whether it is supervised or unsupervised has tackles effectively several obstacles by providing intelligent-solutions to a multitude of UAV-related challenges. Therefore, in the future, we believe that we can continue to explore more supervised and unsupervised learning methods. AI is one of the most popular parts where it informs cars and enables them to perform tasks better than humans. Combining the benefits of using artificial intelligence in a UAV network is considered an interesting and inspiring idea. Although traditional methods have been successful in solving many problems in this area, it is still interesting to investigate whether ML and RL can provide more powerful and accurate solutions. The transition from traditional learning to smart learning may require sacrificing readability and management, but AI solutions, especially given the unprecedented success of machine learning and smart learning in decisionmaking tasks. It is worth choosing. However, while we believe that smart solutions do not always outperform traditional solutions, traditional types of approaches can sometimes provide simple and effective solutions. Undoubtedly, this ambiguity is one of the reasons for investigating the use of AI to solve some special problems in UAV networks. Originally, UAVs were designed for full manual control of humans, but with the recent advent of artificial intelligence, the market has offered intelligent UAVs. In this context, artificial intelligence can use the information gathered by the drone’s sensor to perform a multitude of tasks. Artificial intelligence can play an important role in UAV resource management to maximize energy efficiency. Orbit and UAV implementation projects are also subject to artificial intelligence improvements due to the ability of UAVs to avoid obstacles and automatically calculate their orbit. For example, “Follow Me” drones have been very successful in the market these days. This type of drone provides excellent video capture, owner surveillance, and capture, and features powerful and intelligent algorithms to avoid obstacles. In addition, this context allows you to extend a wide range of applications such as surveillance, motion control, and landing detection. UAV visualization can also be improved by using existing modern computerized visions for UAV imaging. In a nut shell, the performance of a UAV-based network can be significantly improved by integrating information algorithms to automate complex tasks and increase the level of information in the system.

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References 1. 3GPP TR 36.777 V1.1.0: study on enhanced LTE support for aerial vehicles. Tech. Specification Group Radio Access Network (2018) 2. National Aeronautics and Space Administration (NASA), Unmanned Aircraft Systems Integration in the National Airspace system Project, (2016) 3. B. Li, Z. Fei, Y. Zhang, UAV communications for 5G and beyond: recent advances and future trends. IEEE Internet Things J. (2019) 4. M. Mozaffari, W. Saad, M. Bennis, Y. Nam, M. Debbah, A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun. Surv. Tutor. (2019) 5. A. Fotouhi, H. Qiang, M. Ding, M. Hassan, L.G. Giordano, A. Garcia-Rodriguez, J. Yuan, Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges. IEEE Commun. Surv. Tutor. (2019) 6. P. S. Bithas, mosthenes Vouyioukas, nasios G. Kanatas, A survey on machine-learning techniques for UAV-based communications. MDPI (2019) 7. E. Alpaydin, Introduction to Machine Learning, (MIT Press, Cambridge, MA, USA, 2014) 8. M.-A. Lahmeri, M.A. Kishk, M.-S. Alouini, Artificial intelligence for UAV-enabled wireless networks: a survey. IEEE Open J. Commun. Soc. 2, 1015–1040 (2021) 9. Q. Zhang, W. Saad, M. Bennis, X. Lu, M. Debbah, W. Zuo, Predictive deployment of UAV base stations in wireless networks: machine learning meets contract theory. IEEE Trans. Wireless Commun. 20(1), 637–652 (2021) 10. J. Kwak, Y. Sung, Autoencoder-based candidate waypoint generation method for autonomous flight of multi-unmanned aerial vehicles. Adv. Mech. Eng. 11(6), 1–16 (2019) 11. S. Alsamhi, O. Ma, S. Ansari, Predictive estimation of the optimal signal strength from unmanned aerial vehicle over internet of things using ANN 12. P. Ladosz, H. Oh, G. Zheng, W.-H. Chen, A hybrid approach of learning and model-based channel prediction for communication relay UAVs in dynamic urban environments. IEEE Robot. Autom. Lett. 4(3), 2370–2377 (2019) 13. S.K. Goudos, G. Athanasiadou, Application of an ensemble method to UAV power modeling for cellular communications. IEEE Antennas Wireless Propag. Lett. 18(11), 2340–2344 (2019) 14. M. Chen, W. Saad, C. Yin, Deep learning for 360 content transmission in UAV-enabled virtual reality, in Proceeding of IEEE International Conference on Communications (ICC), (2019), pp. 1–6 15. J. Park, D.H. Kim, Y.S. Shin and S.-H. Lee, A comparison of convolutional object detectors for real-time drone tracking using a PTZ camera, in Proceeding of IEEE International Conference on Control, Automation and Systems (ICCAS), (2017), pp. 696–699 16. K. Mukadam, A. Sinh, R. Karani, Detection of landing areas for unmanned aerial vehicles, in Proceeding of IEEE International Conference on Computing Communication Control and automation (ICCUBEA), (2020), pp. 1–5 17. S. Dhar, J. Guo, J. Liu, S. Tripathi, U. Kurup, M. Shah, On-device machine learning: an algorithms and learning theory perspective 18. V. Sharma, R. Kumar, K. Srinivasan, D.N.K. Jayakody, Coagulation attacks over networked uavs: concept, challenges, and research aspects. Int. J. Eng. Technol. 7, 183–187 (2018)

Implementation of Machine Learning Techniques in Unmanned Aerial Vehicle Control and Its Various Applications E. Fantin Irudaya Raj

Abstract An unmanned aerial vehicle (UAV), sometimes known as a drone, is an aircraft or airborne system that is controlled remotely by an onboard computer or a human operator. The ground control station, aircraft components, and various types of sensors make up the UAV system. UAVs are categorized depending on their endurance, weight and altitude range. They can be used for multiple commercial and military applications. UAV intelligence and performance entirely depend on their ability to sense and comprehend new and unfamiliar environments and conditions. Numerous Machine Learning (ML) algorithms have recently been developed and implemented in the UAV system for this purpose. The integration of machine learning and unmanned aerial vehicles has resulted in outputs that are both fast and reliable. It will also lessen the number of real-time obstacles that UAVs confront while simultaneously boosting their capabilities. Additionally, it will pave the way for the application of UAVs in a number of different fields. The current chapter discusses in detail machine learning approaches and their integration with unmanned aerial vehicles. Additionally, it discusses the application of UAVs in various domains and their effectiveness. Keywords Unmanned aerial vehicle · Machine learning · Drone · Classification of UAV · Recent trends and values · UAV applications

1 Introduction Unmanned Aerial Vehicles (UAVs), or drones as they are commonly called, have only been in use for around 60 years. Many countries’ air defences now include unmanned aerial vehicles (UAVs). Since the US Air Force deployed unmanned drones in the 1940s, unmanned aerial vehicles have made great strides. Those drones were designed for observation and espionage; however, their operational systems contained defects that rendered them useless [1]. After the continuous research and E. Fantin Irudaya Raj (B) Department of Electrical and Electronics Engineering, Dr Sivanthi Aditanar College of Engineering, Tiruchendur, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Ouaissa et al. (eds.), Computational Intelligence for Unmanned Aerial Vehicles Communication Networks, Studies in Computational Intelligence 1033, https://doi.org/10.1007/978-3-030-97113-7_2

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revolution, UAVs have evolved into the very sophisticated devices they are today. In the present world, UAVs can be used in various fields it varies from agriculture to military applications [2]. The innovation in the UAV is started at the beginning of the 1900s. In the year, 1922 the first launch of UAV, namely RAE 1921 Target is getting launched from the British aircraft carrier HMS Argus. The first successful flight of a radio-controlled UAV was in the year 1924, which flew around 39 min. Starting from this, many countries all around the world have started to show a high level of interest in unmanned aerial vehicles. They started to invest huge amounts of money in research and development to develop modernized and more reliable UAVs. In 1998, the first trans-Atlantic crossing of UAV was performed. Trans-Pacific crossing of a UAV was performed in the year 2001. These are a few important developments in the evolution of UAVs [3]. Figure 1 depicts the recent types of simple UAVs used in recent times. The recent developments in the UAV make it viable for various applications. Because UAVs can be controlled remotely and flown at varying distances and heights, they are great candidates for tackling some of the world’s most demanding jobs. Drones can be spotted aiding in the looking for survivors following a cyclone, supporting police departments and the army with an eye in the sky amid terrorist occurrences, and assisting scientists conducting research in some of the world’s harshest climates [4]. They’ve even made their way inside our residences, where they operate as both a source of entertainment and an indispensable tool for photographers. Many civil engineering applications like monitoring bridge safety, land surveying, crack detection can also be carried out using UAVs [5].

Fig. 1 Different types of UAVs developed in recent times

Implementation of Machine Learning Techniques …

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Machine learning (ML) is an artificial intelligence subset that splits data into testing and training sets in order to construct prediction and forecasting models. Kneural networks, support vector machines, and Random forests are some of the machine learning methods used for modelling and prediction [6]. By means of adopting these ML algorithms in UAVs, optimal trajectories of motion become possible. It allows the UAV can be implemented in different terrain and can be used in different weather conditions [7]. The present work provides a comprehensive review of the scope, future prospects of Machine Learning algorithms from the UAV applications. It discusses in detail the UAV classification, its market trends and market values in the present times. The various machine learning algorithms and their classification is also briefed. In addition, the adoption of these ML algorithms in UAVs and their different applications are also explained.

2 Classification of UAV Classification of UAVs can be based on a variety of performance parameters [8]. Wind loading, speed, range, endurance, and weight are all critical factors that distinguish various types of UAVs and serve as the foundation for useful categorization. It can be a subsection depending upon the Strategic, Tactical, Size of the UAV, and Special task [9]. Table 1 provides the detailed classification of UAV and their endurance, flight altitude, range, and mass. In addition to the above categorization, UAVs are categorized according to measurements or parameters that include price, maximum take-off weight, engine type, and pricing. Drones can be classified by to their range (short or long), pricing (expensive or affordable), payloads (high or low), model complexity (complex or noncomplicated), number of blades (quadcopter, octocopter, multi-copter), and other factors. Further, the UAVs are classified into multi-copter, unmanned helicopter, tiltwing, and fixed-wing [10]. These types of classifications provided by the researchers are listed in Table 2.

3 Unmanned Aerial Vehicle (UAV) Market Trends and Values In the last six years, the market for unmanned aerial vehicles (UAVs) has exploded. Figure 2 depicts commercial UAV revenue from 2015 to 2021, as well as predicted revenue and forecasted up to 2030 [11], while Fig. 3 depicts UAV market values in various sectors. The worth of enterprises and labour in various industrial sectors that can benefit from drone utilization demonstrates the significance of various UAV applications. In the presented industrial sectors, the overall value of drone-powered solutions

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Table 1 Detailed classification of UAV Depend upon the strategic (TBDa ) Types

Mass (kg)

Range (km)

Flight altitude (m)

Endurance (h)

High altitude long endurance

2500–5000

>2000

20,000

22–44

Stratospheric

>3000

>2200

>25,000

>44

Exo-stratospheric

TBDa

TBDa

>35,000

TBDa

Medium altitude long endurance

1000–1500

>600

4000

24–48

Long altitude long endurance

15–25

>600

3000

>24

Low altitude deep penetration

250–2000

>250

100–9000

0.5–1

Medium range endurance

500–1500

500

8000

10–18

Medium range

150–500

70–200

4000

6–10

Short range

50–250

30–70

3000

3–6

Close range

25–150

10–30

2500

2–4

Mini

δ P . So, our alternative hypothesis is established and we hence may proceed with silent listening approach.  

3.2 FDI Attacker Recognition Here we shall get our answer for the second part of RQ 2. From Tables 3 and 4, we consider input matrix A[m][n] where, m is the number of trial or sent data and n be the features like p, q, p ⊕ q, FDI, Non-FDI etc. Let b be the vector with values 1, and 0 for separating the trails in two groups FDI attacker and non-attacker, respectively. To select actual attacker profile, we can use the following equation: a × b = c , ||A · b|| = |c|

(2)

c[i] > 0 will store the corresponding attacker’s MAC address. This procedure was accomplished with the help of Algorithm 2. Proof 2 The proof is given in Sect. 4. From the definition of FDI attack derived in this paper and the value of a, we can generate normal distribution curve of an FDI attack as following Fig. 2. Proof 2 finds the optimal value of a. The above graph shown in Fig. 2 may be illustrated with the help of both Proof 2 and Table 2. Table 2 has mixed data of FDI attack and non-FDI attack. We may calculate the probability density function (PDF) with the formula given below: f (x) = √

1 2π σ 2

e−

1 (x − μ)2 2σ 2

Here, σ , μ represents mean and standard deviation, respectively.

(3)

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3.3 Smart-Car Based Test Bed Creation for Sample Collection 3.3.1

FDI Attack Detection Model

While setting up the test bed, we assume that the network allows all the non-https contents to be run. We are going to use [25]. We are also going to build the scenario as stated in [11]. Three machines in a simple Wireless network under same router are connected as shown in Fig. 3. Let us assume Mc/1 be the sender, Mc/3 be the receiver and Mc/3 is acting as passive listener (alternatively, sometimes we may address it as ‘monitor’). Their corresponding IP Addresses are shown in Fig. 3, for example. Assume that both the sender and receiver are running on Mac operating system and the monitor is running on Ubuntu Linux. We simply converted the monitor into a router and issued ARP spoofing on the monitor for sender and receiver. Now, since the monitor becomes silent listener, it is capable of getting all the data exchanged between Mc/1 and Mc/2. And being a router, it is able to get the correct sensor data associated to every machine. Sender and receiver are nothing but the simulated selfdriving cars which are exchanging sensor data. Detailed Simulation of self- driving car environment is actually not under the scope of the detection algorithm. Interested readers may follow the process of simulating self-driving car environment as shown in [26]. In M/c-1 and M/c-2, we created a deep learning based simulated environment with unity as game engine. We trained, validated our cars with sensor data. Simulated cars (M/c-1 and M/c-2) are sending and receiving sensor data and the monitor (M/c-3) is capturing the ICMP packets. ICMP packets are being analyzed in

Fig. 2 Computation of probabilities and percentiles for normal random variables: X ∼ (μ, σ ) [24]

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Fig. 3 Monitor sniffs data exchanged between two smart-car nodes under lab set-up

real time with actual sensor data. This comparison is very important for understanding if there exists any difference in the actual sensor data and the obtained ICMP packet or not. Difference between these two values confirms False Data Injection (FDI) attack in our experimental set-up.

Table 2 Sample table to compute probabilities and percentiles for normal random variables X ∼ (μ, σ ) FDI? No. of FDI? Non-FDI? No. of Non-FDI FDI 1 No-FDI 0 FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI FDI Mean

1 1 1 1 1 1 1 1 1 1 1 1 0.5

No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI No-FDI St. dev

0 0 0 0 0 0 0 0 0 0 0 0 0.516397779

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Understanding FDI Attack Snippet for comparing UDP, ICMP packets with actual sensor data: FOR i = 0, 1, 2, 3, . . . , n − 1 do IF Sensor _data[i] ⊕ I C M P_ packet[i] == 1 Alert: FDI Attack ELSE Default ENDIF ENDFOR

Mathematically, the above snippet can also be represented as in Eq. (4): n−1  i=0

p[i] ⊕ q[i]

== 1, F D I Attack == 0, N o F D I Attack

 (4)

Here, p and q represent actual sensor data and sent ICMP packet, respectively. As shown in Fig. 4, from the above mathematical derivation of Eq. (4), we can define FDI attack with the help of state diagram also. We are collecting data from such five sets in run time. As it is clear that from our experimental model, we have created post-test only model to study our sample. This post-test only setting gives us the detailed data set to test our algorithm and give a valid conclusion over the work.

3.4 Procedure 3.4.1

Comparing Sensor Data and ICMP Packet

Algorithm 1: FDI Detection algorithm (For each Experimental Group)

Fig. 4 State diagram to understand definition of an FDI attack

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STEP 1. INITIALIZATION: Get Actual Sensor data and ICMP packets in two arrays p, and q, respectively, to solve p[i] ⊕ q[i]. Assuming that initially the system has zero alert, set variable aler t = 0. STEP 2. FOR i = 0, 1, 2, 3, . . . , n − 1 do STEP 3. IF p[i] ⊕ q[i] == 1 Generate Alert: FDI Attack aler t = 1 ELSE Generate Message: System Scanning Progressing alert = 0 ENDIF ENDFOR STEP 3. STOP

Suppose, there are two finite strings p and q. We have to prove the equality of these strings. To understand the equality of these two strings, we have to assume that Pr ((q[i] == p[i])| p[i]) should be 1. We are making this calculation in raw level. So, we converted our sensor data and ICMP data in binary number system. The state diagram shown in Fig. 4 helps us to understand how XOR operation is used here to compare these two values.

3.4.2

Recognizing FDI Attacker

To become an FDI attacker, Pr (M AC[i]|aler t = 1) should be 1. We are using ARP snooping to understand the ICMP packets exchanged over wireless network. So, we have advantage to correctly recognize the machines by their MAC addresses. Algorithm 2: FDI Attacker Recognition Algorithm (For Each Experimental Group) STEP 1. INITIALIZATION: Get MAC Addresses of each nodes in an array MAC. Assuming the system is not under threat, set variable attack = 0 STEP 2. FOR i = 0, 1 IF alert == 1 Declare MAC[i] as FDI attacker ELSE: Default

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ENDIF ENDFOR STEP 3. STOP

Remark 1 Note that collaborative security detection for large network is not scope of the paper. If someone wishes to make a centralized security detection using our algorithm, first they need to setup the experiment accordingly. Then only she should use the centralized security detection version of it. Theoretically as well as practically we found that paralleling the computation is appropriate for it.

3.5 Instruments As per instruction given in [19], we run our simulator in training mode to collect 3 images per frame corresponding to left, right, and center-mounted cameras, steering angle and speed for each frame. Further based on the model, we generated our test dataset. From the continuous plot of steering angle, one can easily identify the existing curve on a path, from the speed and distance covered; one can calculate the exact length of that curve. So, if M/c-2 wants to get any such sensor information, M/c-1 may send the information (unit less). Our Algorithms 1 and 2 demand binary values. So, we are converting the sensor data in binary. We validated the self-driving model used in both M/c-1 and 2, in total five experimental sets and got the following results as shown in Fig. 5. We wanted a smooth driving. We trained our model using an ADAM optimizer with a learning rate of 0.0001. We changed this learning rate after each 50 epoch. We applied T-test [27] to assure the statistical significance between the model Mean Squared Error loss for each experimental group and observed that ∀x ∈ (1, 2, 3, 4, 5), T _value(x) < critical_value. In set of five experimental groups, we found that there is no significance difference in our self-driving car model. We also analyzed the relationship between the self-driving car model given in [19] and the model simulated in our experimental setup. We analyzed the T-value of the sample means of the steering angle obtained in both our model and the former. We found that the two models are statistically significant, since ∀x ∈ (1, 2, 3, 4, 5), T _value(x) < critical_value, given model in [19].

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3.6 Data Analysis Technique The basic difference between an FDI attacker and a non-FDI attacker were made on the basis of the mean and standard deviations as shown in Table 3. Definition 1 We are defining an attack (in our case, FDI) with mean value as one and zero standard deviation, and a non-attack is defined as both zero mean and zero standard deviation. For our dataset, typically we can compute, by the law of Binomial Distribution [28], for 100 numbers of trials, Pr (Success f ul_Attack) with mean value, μ = np = 50.

Fig. 5 Validation of our simulated smart car models

Table 3 Sample data collected for FDI attack and non-FDIs Result FDI

Mean St. dev

1 1 1 1 1 1 1 1 1 0

Non-FDI 0 0 0 0 0 0 0 0 0 0

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Fig. 6 Performance of Algorithms 1 and 2

4 Results and Discussion From Fig. 6, we find that both Algorithms 1 and 2 are capable of successfully detecting 100% FDI attacks, as well as recognizing the attacker in run time. We apply T-Test to check the statistical significance of the test at initial level. We also apply Chebyshev’s inequality. As shown in Table 4, we can detect the attack with 100% success rate. Successful detection of attack or no-attack = 100%. Successfully recognizing the data value sent over network = 100%. Successfully recognizing the attacker node = 100%. From the literature study, it is quite obvious to understand that game-based attack detection does not guarantee prevention from accidents or damage in vehicle platooning. But, it is clear from Lemmas 1 and 2, that since the attack detection takes place before successful attack on the target machine, the present approach may save property damage in FDI attacks compared to the game-based one.

Table 4 Data for understanding FDI Trial p = Sensor q = Sent Data Data i=1 i=2 :

100 101 :

101 101 :

p⊕ q

Comparison

Meaning

110 111 :

q = p ⊕ q q= p⊕q :

FDI No FDI :

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4.1 Examining Correctness of Algorithms 1 and 2 Let us check here the correctness of our algorithm (FDI attack detection algorithm). Assume, Z takes two values 0 and 1 as follows Eq. (5): 1, i f p[i] ⊕ q[i] = 1 Z= 0, i f p[i] ⊕ q[i] = 0

 (5)

Assume, Y denotes the attack occurs. Since, we know in our case, the expected value of Y is Eq. (6), E[E[Y |Z ]] = E[Y ] (6) We can claim that the working principle of our algorithm for comparing actual sensor value and the sent values makes the algorithm correct. Proof 3 L .H.S = E[E[Y |Z ]]   = Pr (Z = z) y · Pr (Y = y|Z = z) z

= = =

 y

z

y

z

 

y

y · Pr (Y = y|Z = z) · Pr (Z = z) y · Pr (Y = y ∩ Z = z)

y · Pr (Y = y)

y

= E[Y ] = R H S(H ence Pr oved). Since, as we are using binomial distribution, we will now use the Chebyshev’s inequality. For any random variable, its variance to be given as Eq. (7): V ar [X ] = E[X 2 ] − (E[X ])2

(7)

Assume X denotes a machine to be an attacker i.e. it is associated to the attacker recognition algorithm (Algorithm 2). Therefore, if we want to know what is the expectation that an FDI attack launched and the selected machine is an attacker, can be given by Eq. (8): V ar [X + Y ] = V ar [X ] + V ar [Y ] + 2Cov(X, Y )

(8)

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Clearly, we understand that, this relationship between X and Y is actually helping us to identify the importance of M/c-3 i.e. the monitor. Let us understand first the dependency of X and Y. If we further expand the covariance, we will get Eq. (9): Cov(X, Y ) = E[X · Y ] − E[X ] · E[Y ]

(9)

If X and Y are completely independent then Cov(X, Y ) should be 0. Or, we can say, as in Eq. (10) that: E[X · Y ] = E[X ] · E[Y ] (10) Assume the situation where we have to recognize an attacker particularly when an attack takes place. In that case, if X and Y are not dependent, we might get Eq. (11), E[X · Y ] − E[X ] · E[Y ] = 0

(11)

We may argue if, X and Y are dependent, Cov(X, Y ) becomes 0 · 75. We assume that the expectation is ranging between 0 and 1. So, it is clear that to establish the need of knowing the MAC address of an attacker will only be applicable iff X and Y are dependent. In our case, the role of monitor is backed with the working principle of ARP Spoofing where it constantly monitors ICMP packets and sensor value with respect to particular MAC address. Thus we can defend the usage of ARP spoofing in Algorithm 2 for attacker recognition. Proof 4 E[X · Y ] =

 x

=



x y · Pr (X = x) · Pr (Y = y)

y

x · Pr (X = x) ·



x

y · Pr (Y = y)

y

= E[X ] · E[Y ]  

When X and Y are independent. Proof 5 In our case,

= 1, i f Pr (X ) = 1 X = 0, i f Pr (X ) = 1 − p

 (12)

Assume, E[X ] = p

(13)

E[X 2 ] = 1 − p

(14)

and

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Therefore, for successful attacker recognition with probability 1 and unsuccessful attacker recognition with probability 0 lead the value of Var[X] to be Eq. (15): = 1, f or Pr (X ) = 1 V ar [X ] = 1, i f Pr (X ) = 0

 (15)

We derive Eq. (15) by applying the formula Eq. (16), V ar [X ] = p − p 2

(16)

We shall consider the situation with the help of another example. Out of two given ranges of MAC addresses, identifying a particular machine is denoted with a >0. Therefore, applying two-tailed test using Chebyshev’s inequality, we get Eq. (17): Pr (|X − E[X ]| ≥ 0) ≤

V ar [X ] a2

(17)

From the rule of Binomial distribution, X ∼ B(n, p). For each successful attack, in every single trial, n will be 1, for knowing the MAC address correctly of a particular machine, p will be 0.5. Therefore, E[X ] = np = 0 · 5 (18) Therefore, V ar [X ] = E[X 2 ] − (E[X ]2 ) = 1 − p 2 = 1 − 0 · 25 = 0 · 75

(19)

Finally, we can solve Eq. (17) by Eq. (18) V ar [X ] 0 · 75 = 0 · 75 = 2 a 1

(20)

Proof 6 From Table 5, we can write: 

11 A= 00

Table 5 Data for recognizing attacker Trial Comparison i=1 i=2 :

q = p ⊕ q q = p⊕q :

 (21)

Meaning

Sender’s MAC address

FDI No FDI :

8:0:27:ba:fd:31 8:0:20:af:bd:35 :

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From Eq. (21), we get: 

     1 1   1 · 1 + 1 · 0 + 1 · 1 + 1 · 0 2 = · 10 = 0 · 1 + 0 · 0 + 0 · 1 + 0 · 0 0 00

(22)

Clearly, from the output matrix c stated in Eq. (22), we find, for cases c[i] = 0, there was no attack. From Table 5, we took two features denoting comparison and meaning. Based on c[i] > 0 or more precisely, c[i] = 2, we can choose the corresponding MAC address of the attacker, i.e. the sender for a particular trial. To minimize approximation error, we apply Chernoff Bounds. Theorem 1 Let X 1 , X 2 , . . . , X n be independent random variables with Pr (X i = = 0) = 21 . 1) = Pr (X i  n Xi . Let, X = i=1 −a 2

Then Pr (X ≥ a) ≤ e 2n . In our case, we are not interested in mean = 0. Neither we are interested in the events those lead to c[i] ≤ 0. So, we shall use one tail test for a > 0 values only. Since, we √ are interested in the non-zero terms of A[m][n], we need to calculate Pr (|c1 | > 4mlnn). Pr (|c1 | >

√

4mlnn) ≤ e−

4mlnn replacing a 2 with 4 mlnn 2k

(23)

where, k is the term corresponds to 1 in A[m][]. √ From the example of Proof 6, we find, Pr (|c1 | > 4mlnn) ≤ e− 4mlnn 2∗k −2ln2 ≤ e− 4∗1ln2 ≤ e ≤ 0.25. If we tail bound both the sides, then we shall get 2∗1 Pr (|c1 | > a) = 2 ∗ 0.25 = 0.50.

4.2 Brief Answers for RQs From the experiment, data collection and analysis of the results we derive the following answers of our RQs: 1. In Fig. 5, we already proved the equivalence of our test-bed with existing deep learning based self-driving cars. Hence, we conclude that yes, we can simulate real time, deep-learning based smart car network as our test-bed. 2. In Sects. 3 and 4, we showed the importance of ARP spoofing, the analysis of Algorithms 1 and 2; also we analyzed the dependencies of detecting attack and recognizing attacker in present work. Hence, we may argue that ARP spoofing based algorithm could be an alternative of game based algorithms. In addition, Lemmas 1 and 2 prove the importance of our algorithm over game-based solutions.

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3. We carry out our experiment under real time set-up. We launch and detect FDI attacks in real time. Thus, we can argue that RQ3 is true.

5 Conclusion The chapter shows how ARP spoof based algorithms can detect False Data Injection attacks and recognize the attacker successfully in a simulated smart-car test bed. It gives idea to beginners of test bed creation with deep learning algorithm for selfdriving cars of automation level 5. It discusses how to launch an FDI attack in order to find security breach in the system. It also shows how to recognize the FDI attacker using ARP spoofing which is a hacker’s tool. Finally, it argues the applicability of ARP spoofing, over game theory based solution. We analyzed 100% FDI attack detection and attacker recognition using Algorithms 1 and 2, presented in this paper. In spite of all of them, we have some limitations, and we want to work on them as future work. We selected less number of computers in real life setup. Initially, we prepared five different experimental groups with two MAC machines and one LINUX machine. Then we gathered data and tested Algorithms 1 and 2, under both wireless and hybrid combination of wired and wireless) network. Since, our motive is also to deal with FDI attacks in smart car platooning, then we prepare next set of experiment and find some interesting observations like: • applying ARP spoofing from monitor allows to track other suspicious node to apply ARP spoof based attacks. • IP forwarding can immediately stop serving any suspicious or malicious node from the network. Even after carrying out the experiment with high configuration machines, we observed that the analyzed and produced data are huge. As a solution we decide to paralleling the task and use high performance computers to monitor more than two nodes, in future. Since detailed study regarding smart cars is out of the scope of the chapter, we focus more on the behavioral components of the modern smart cars. We shall work more on our present test bed as part of future work. ARP spoof is a strong tool used by hackers. On the other hand, cyber security is such a subject that to know the attacks we have to know the hackers’ techniques. In real time situation, different vulnerability analysis or threat analysis tools are there for system testing, but instead applying them, we directly used it to compare sensor data. In reality, no theoretical books tell us about direct approach to launch such attacks. Aim of this chapter is basically to encourage the students of cyber security to identify security breaches, come up with solution with the help of hands-on knowledge of hacking like ARP-Spoofing based tool, under the supervision of experts and

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security analysts. Finally, we conclude that from security analyst’s point of view, and sensor network point of view, we may apply such readily available tools to defend security breaches. Acknowledgements We are thankful to Information Security lab, NIT Agartala. The fellowship of the first author (research scholar) working under the research work is funded by the MHRD, Govt. of India.

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Taxonomy of UAVs GPS Spoofing and Jamming Attack Detection Methods A. Sabitha Banu

and G. Padmavathi

Abstract The fast evolution of the Unmanned Aerial Vehicle (UAV) has added a great deal of ease to our lives. Specific characteristics of UAV networks, such as node mobility and network design, differ. Security is a significant issue with UAVs since they are used for such delicate tasks. Both the business and academia are interested in the security of unmanned aerial vehicle networks, and as a result, to keep data safe from hackers and fictional actions by unauthorized users, some effective methods must be employed. Numerous threats may attack such networks with the intent of jamming communication, interfering with network functioning, or injecting incorrect data. Since unmanned aerial vehicles (UAVs) depend on the Global Location System (GPS) for positioning and navigation, they are susceptible to GPS spoofing and jamming attacks. Numerous studies are being conducted to improve the robustness of UAV routing protocols and detection mechanisms through the use of Machine Learning (ML), Deep Learning (DL), and Computational Intelligence (CI) techniques. These studies also aim to improve battery life, network performance, and security against attackers. This chapter discusses the different classification of UAVs and their applications, a Statistics report of the current UAV market, some design considerations to build a UAV, taxonomy of UAV routing protocols, and a study of ML, DL, and CI methods used to detect the most predominant attack called GPS Spoofing and Jamming attacks. Keywords UAV · GPS spoofing attack · Jamming attack · Taxonomy · ML/DL detection methods

1 Introduction An unmanned aerial vehicle (UAV) is a plane that can fly without a human pilot. Instead, it is remotely piloted or autonomously flown by on-board computer systems. UAVs are also known as drones [1]. A wide variety of applications and industries A. S. Banu (B) · G. Padmavathi Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Ouaissa et al. (eds.), Computational Intelligence for Unmanned Aerial Vehicles Communication Networks, Studies in Computational Intelligence 1033, https://doi.org/10.1007/978-3-030-97113-7_10

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Fig. 1 UAV applications

are possible with UAVs. Drone applications [2] will spread into many areas of life, which are illustrated in the below system map Fig. 1. Furthermore, they also assisted in covid-19 pandemic situations without any human contact. Sometimes drones are also used in locations that were not able to reachable. Earlier, UAVs were mainly employed for military purposes. They carry out operations that put human pilots in danger. Recently, however, UAVs have been found increasing for civilian uses. Some civilian operations are search and rescue, policing, and inspecting military movements, strategic activities, or environmental monitoring [3].

1.1 Motivation Based on the Statical Reports According to a CNBC report [4], the UAV industry grew fast, from $100 billion in 2020 to $1.5 trillion by 2040, shown in Fig. 2.

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Fig. 2 UAV market forecast

1.2 Classification of UAVs There are different types of UAVs are available in the market. They have differed in terms of Size, Payload, Efficiency, Complexity, and Usage. UAVs vary in size, weight, payload, speed, range, endurance, and electrical and mechanical design. Some of them are [5] • Single Rotor drones • Multirotor drones – – – – • • • • • • • • • • • •

Tricopter (3 rotors) Quadcopter (4 rotors) Hexacopter (6 rotors) Octocopter (8 rotors)

Fixed-wing drones Fixed-wing hybrid drones Small drones Microdrones Tactical drones Reconnaissance drones Large combat drones Non-combat large drones Target and decoy drones GPS drones Photography drones Racing drones.

There are several numbers of UAVs used for both military and commercial purposes based on their usage. Multiple UAVs, sensors, base stations, and data transmission connections comprise UAV networks. The majority of UAV missions need three-dimensional interaction with the operator, necessitating many on-board control sensors. The transmission and management of real-time data by a UAV need reliable and coordinated communication connections between sensors. The primary control

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components are physical infrastructure (external hardware), computer systems (internal hardware), and non-physical software. The payload, operator, data connections, and support components are all similar to UAVs. Several data connections may be established during flight, even in autonomous mode, with one or more Ground Control Stations (GCS), ground-based antennas, Mobile Ground Units, or other UAV (e.g., in the case of swarming). A communication connection may be either continuous and dedicated (like Wi-Fi or Bluetooth) or discrete (like a cable or satellite link). UAV networks rely on data flow via sensors, connections, avionics, and hardware infrastructures like any other IT network or operating system [5].

1.3 Design Considerations of UAV Due to their unique features, UAV networks need many critical design considerations. Several factors to consider before building UAV networks include the following [6]: • • • • • • • •

Topology mobility latency frequent link disconnection prediction, flight formation collision avoidance combat with external disturbances and scalability.

UAV networks need a high degree of scalability, flexibility, and robust routing protocols because of the fast changes in topology and activities during network operation. Routing protocols of UAV networks are examined in the coming section. In most cases, UAV networks use three distinct modes of data transmission. They are unicasting, broadcasting, and multicasting. In most routing systems, packets are delivered via which to get to the desired location via series of one-to-one connections from the source node. Broadcast data transmission entails transmitting information in the form of packets from a specific source node to all destinations nodes in the network region. Probabilistic routing, network coding, and other methods are built on multicast, which is a node that distributes data packets to a group of target nodes in advance. Earlier, UAV networks were tested using the routing technologies employed in MANET and VANET. However, it failed due to the inability of the system to adapt the unmanned aerial vehicles’ high level of mobility, rapidly changing network architecture, and poor communication links, routing overhead, bandwidth, scalability. It may be addressed by either improving existing routing protocols, hybridizing different routing protocols, or replacing them with new ones [7]. Figure 3 illustrates the taxonomy of UAV routing protocols, from conventional to AI-enabled routing protocols.

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Fig. 3 Taxonomy of UAV routing protocols

2 Taxonomy of UAV Routing Protocols Routing protocols for unmanned aerial vehicles (UAVs) are divided into two distinct groups: (i) network architecture-based and (ii) data-forwarding-based, and again, in turn, network architecture-based is subdivided into “topology-based routing, position-based routing, hierarchical routing, SI based routing, probabilistic routing, and AI-enabled routing”. Data forwarding-based is subdivided into “deterministic

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routing, stochastic routing, and social networking-based routing” [6, 8, 9]. Subdivisions of the routing as mentioned earlier and their protocols are explained in detail below.

2.1 Topology Based Routing Network packets are routed using topology-based routing techniques. Topologybased routing systems use IP addresses to define nodes. The subdivisions of topologybased routing protocols are: • • • •

Static Routing Protocol Proactive Routing protocol Reactive Routing protocol Hybrid routing protocol.

2.1.1

Static Routing Protocol

Static routing techniques have fixed routing. The UAVs can’t update or change their routing database while in flight. It is also a fault-tolerant routing protocol.

Data Centric Routing (DCR) Data-centric routing uses data to work. For one-to-many communications, these routing methods may be utilized. This protocol performs better in clustered environments.

Loar Carry and Deliver Routing (LCAD) Data transfer from the source ground station to the destination is safe using this method while maintaining high throughput. However, because of vast distances, the method’s primary flaw is a significant transmission latency. A multi-UAV system may very well be utilized to minimize data transmission latency.

Multi-level Hierarchical Routing (MLHR) Several clusters in a hierarchical network may carry out distinct functions. MLHR may have a flat basis. The hierarchical design expands the network’s operating area. UAV networks are clustered, with just the cluster head (CH) connecting to other cluster heads and the ground node.

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Proactive Routing Protocol

Proactive Routing protocol utilizes routing information to store all network routing. The nodes regularly update and exchange their routing tables. As a result of changes in topology, it must be updated in the tables. The main benefit of PRP is that it is constantly up to date. All communication nodes must exchange routing messages to keep the routing tables updated. However, it uses an excessive amount of bandwidth which ruins the network. Changes in the topology of a network affect how quickly it reacts, resulting in latency.

Optimized Link State Routing (OLSR) OSLR is a mobile ad hoc routing protocol. Due to its proactive character, a linkstate algorithm’s stability is inherited by the protocol. OLSR is a link-state protocol optimization for mobile ad hoc networks. OLSR is intended to operate fully decentralized, with no central authority. Because each node transmits loss of control signals regularly, the protocol allows for tolerable levels of damage. When nodes exchange control messages, overhead is generated. Numerous additional routing protocols have been suggested based on the OLSR technique, including D-OLSR, M-OSLR, and CE-OSLR.

Directional Optimized Link State Routing (D-OLSR) This method reduces the number of multipoint relays by utilizing directional antennas. If the distance from the origin to the destination is more than Dmax /2, the node uses the DOLSR technique. The OLSR with an omnidirectional antenna is utilized for distances less than Dmax /2. In this way, we reduce the latency and network finding overhead. However, the overhead in UAV networks is still too high.

Multidimensional Perception and Energy Awareness OLSR (MPEAOLSR) OLSR has been enhanced to take node connection time, link-layer congestion level, and node residual energy into account when determining the best route to take. Data transmission success rates are improved, packet loss is reduced, and end-to-end latency is minimized as a result.

Dynamic Dual Reinforcement Learning Routing (DDRLR) For multi-service wireless mesh networks, this research explores an independent Quality of Experience (QoE) methodology. Asymmetric forward/backward reinforcement learning techniques are used for each mesh node to dynamically change

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their routing strategies to optimize the perceived QoE for each network flow by the user. A new source rate adaptation technique is used in conjunction with the routing methods to match the transmitting rate to the available network capacity.

Destination Sequence Distance Vector Routing (DSDV) The Bellman-Ford method is modified somewhat in DSDV routing. DSDV is a proactive technique for routing data transmission. DSDV uses incremental and fulldump update packets. Network topology changes are signaled by sending incremental packets, reducing network overhead, and not eliminating it. To prevent network loops, DSDV routes are numbered sequentially. However, updating the route takes much bandwidth.

BABEL A distance vector technique that avoids loops, BABEL. It is better suitable for version 4 and version 6 of Internet protocol networks. The BABEL can improve loop-free concurrence rapidly. BABEL utilizes a metric to determine the shortest route. Because BABEL updates the periodic routing database, it produces additional traffic when the network topology changes. BABEL fails in UAV networks based on datagram loss rate and average outing time.

Better Approach to Mobile Ad Hoc Network (BATMAN) Ad hoc networks’ BATMAN is a relatively recent proactive routing system. BATMAN actively maintains the presence of all communication nodes via single-hop and multi-hop. Next-hop neighbors may be utilized to establish communication with the target node. The BATMAN algorithm is advantageous for finding the optimum subsequent hop for each position. Because BATMAN does not compute the whole path, it is rapid. BATMAN has excellent data rate and packet loss characteristics. The BATMAN packets are small because they can only hold so much data. Because the packets lack route information, BATMAN cannot spread.

Optimized Link Routing with Expected Transmission Count (OLSR-ETX) The OLSR-ETX outperforms the conventional OLSR packet transfer, delay, and overhead.

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Link-Quality and Traffic-Load Aware Optimized Link-State Routing (LTA-OLSR) LT-OSLR uses statistics on the received signal strength to find the link quality.

Multidimensional Perception and Energy Awareness OLSR (MPEAOLSR) Node connection time, residual energy, and link-layer congestion are all factors in improved OLSR route selection. Reduces packet drop and improves data transmission success rates while reducing overall delay.

Cluster Head Gateway Switch Routing (CGSR) This is a table-based algorithm for clustered networks. In this approach, each node keeps two tables: one for cluster membership and one for routing. It is quick to find the route. Choosing the cluster head does add to the routing protocol’s complexity. It also does not apply to flat mesh networks.

Wireless Routing Protocol (WRP) It includes the routing table network view. The nodes maintain several tables to improve performance: Distance Table (DT), Route Table (RT), Link Cost Table (LCT), and Message Retransmission List (MRL). It finds the shortest route and converges faster than DSDV. A new connection break technique is enabled by monitoring update messages. Cost is significant table update overhead.

Topology Broadcast Based on Reverse Path Forwarding (TBRPF) It uses reverse path forwarding. It utilizes minimum-hop trees instead of shortest-path trees to save on overhead. To minimize routing message cost, it compares the past and current network states instead of the entire network status. However, it seldom finds the shortest route.

2.1.3

Reactive Routing Protocol

They generate demand-based routing data. It implies that finding a path can be performed when a transmission session is required. This method reduces overhead, particularly in low-traffic areas. If a route fails, constructing another route takes a while. Listed below are part of prominent reactive routing protocols.

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Dynamic Source Routing (DSR) This routing system saves a complete route from one node to the next. It has a lower overhead than AODV, OSLR, and (TORA). The major disadvantage is the route-finding time.

Ad Hoc on Demand Distance Vector (AODV) It is a standard routing method for storing the next node in a routing table’s next hop, maximizing network bandwidth efficiency and reducing routing overhead. However, route determination takes too lengthy, causing network congestion.

Radio Metric Ad Hoc On-Demand Distance Vector (RM-AODV) IEEE 802.11s suggested this protocol. RM-AODV eliminates the route determination complexity from the top layers, allowing them to view all UAVs in one hop. The protocol’s route cost measure indicates connection quality and resource consumption when a frame is transferred across a link.

Dynamic Topology-Multipath AODV (DT-MAODV) To reduce the frequency of route rebuilding, this routing protocol utilizes a route that is both linked and disjointed to create a large number of alternative routes. When compared to AODV, it reduces packet loss and reduces end-to-end latency.

Associativity-Based Routing (ABR) The present routing method utilizes a connectivity database to maintain track of network connections. Other reactive routing methods need more route reconstructions. This risk rises with increased mobility and traffic.

Signal Stability-Based Adaptive Routing (SSA) When a steady connection is not available, this method chooses unstable connections. It prioritizes signal strength and connection reliability. This method’s route setup time is lengthy.

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Message Priority and Fast Routing (MPFR) In order to expedite data transmission, this protocol utilizes priority bits in the decision-making process. Using this method, the essential packets are processed first, which reduces the processing time for lower-priority ones.

Dynamic Backup Routes Routing Protocol (DBR2P) When a link fails, this routing method includes a backup node mechanism to quickly reconnect, allowing backup routes to be discovered when the original one is lost.

Dynamic MANET On-Demand (DYMO) The Route Request (RREQ) packets are broadcast along with the location data using this method. Compared to AODV, it has a reasonably low overhead, but it takes longer to set up routes.

Time Slotted On-Demand Routing (TSOR) Solves network congestion using this routing method. Each UAV has its time slot, reducing the communication overhead between UAV pairs.

Reactive-Greedy-Reactive (RGR) For UAVs, a version of AODV with fewer hops was suggested. However, the shortest route is not always the best.

Modified-RGR (M-RGR) Routes in RGR that are more reliable and stable. Packet forwarding UAVs seek high link dependability. This method uses GPS data to determine the distance between two points. Data transmission between two nodes traveling in opposing directions is lost. Otherwise, the dedicated route uses the AODV protocol.

AODV Security Extension (AODVSEC) A lack of security in the AODV routing protocol does not render it unreliable on an open network. For security, AODVSEC proposes to extend the scope of AODV with less computation time.

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Trusted AODV (TS-AODV) A protocol is described that allows nodes to participate in routing depending on their trust rating. If a node’s trust value exceeds a threshold, it is recognized as trustworthy and permitted to participate in routing.

2.1.4

Hybrid Routing Protocols

Hybrid routing methods are developed to reduce overhead issues in proactive and reactive protocols. In contrast, Proactive Routing Protocol has a high overhead of control messages and requires more time to find routes. Intra-zone routing utilizes Proactive Routing Protocol, whereas inter-zone routing uses Reactive Routing Protocol. Some of the hybrid routing protocols for UAV networks are listed below.

Zone Routing Protocol (ZRP) Make use of the concept of clustering. For inter clustering, it uses proactive, and intraclustering uses a reactive approach. This technique reduces the processing time and overhead of the route finding. Maintaining information is nevertheless tricky for dynamic nodes and connection behavior.

Temporarily Ordered Routing Algorithm (TORA) Networks with many hops utilize this routing technique where routers only save information about nearby nodes. However, it restricts control message transmission in highly dynamic networks, reducing UAV network efficiency.

Hybrid Wireless Mesh Routing Protocol (HWMP) It aids in route selection. In multi-hop networks, HWMP is utilized for video surveillance.

On-Demand Routing with Boids of Reynolds Protocol (BR-AODV) Routing based on demand and the Boids of Reynolds technique is used in BR-AODV to guarantee routing and connectivity during data transfer. Boids of Reynolds are used in this protocol to preserve connection when the UAV’s dynamic topology changes. The AODV protocol is used to transmit messages between unmanned aerial vehicles (UAVs) when needed because it enables the UAV nodes to acquire routes if required. The number of route-finding launches may be reduced by using BR-AODV instead of

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a conventional satellite. Packet delivery ratio, delay, and packet drop are all improved over AODV.

Link Estimation-Based Preemptive Routing (LEPR) The AODV routing protocol is the foundation for this protocol. In developing LRPR, the link stability measure is used, with GPS providing node position information. Additionally, the quality of the connection, the degree of safety, and the likelihood of node mobility are considered. Multiple link-disjoint routes are explored throughout the route discovery process. LEPR minimizes broken pathways and end-to-end latency.

Reactive Flooding Routing (RFR) RFR is suggested for scenarios involving the use of technology in farming in which abrupt climatic changes significantly impact the quality of the crops and the farming methods. UAVs are equipped with a specialized sensor that transmits data to farmers. The reactive method outperforms the proactive approach in terms of packet delivery ratio.

Sharp Hybrid Adaptive Routing Protocol (SHARP) SHARP is a hybrid routing system that adapts to changing network and traffic characteristics. SHARP allows applications with varying needs to adjust routing layer performance. SHARP allows applications to explore this area in networks with dynamic traffic patterns, node degrees, and mobility rates. SHARP outperforms in terms of packet overhead, loss rate, and jitter.

2.2 Position-Based Routing With GPS, the present algorithms determine the best route depending on the user’s location. For instance, the following node may be chosen as a result of proximity to the destination node. These techniques’ primary drawback is their reliance on precise positioning and tracking devices. This routing is ideal for networks of highly dynamic UAVs. The following section contains a summary of the protocols.

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UAV-Assisted VANET Routing Protocol (UVART)

Algorithms that take connection and traffic density into account while routing traffic. UVAR is controlled by the following four factors: traffic density, node distance, connection, and vehicle dispersion. The Dijkstra algorithm is used to locate the source and destination. When using UVAR to predict the number of automobiles in a certain area. When the network is on the ground, UVAR may serve as a relay. It also offers road signs and assists traffic management.

2.2.2

Connected-Based Traffic Density Aware Routing Protocol (CRUV)

The cars exchange periodic HELLO packets. These exchanges are used to identify the most linked segments. UAVs share this data with other nodes to optimize route choices. If a segment is linked, the source vehicle chooses the UAV to send data to. Its main benefit is that UAVs discover it when the present vehicle cannot locate the linked section.

2.2.3

UAV-Aided Vehicular Networks (UAV-VN)

A network’s route availability is dependent on vehicle density and collaboration. It improves route connectivity. SCF allows UAVs to help ground vehicles in data transmission to roadside units (RSU).

2.2.4

UAV Relayed Tactical Mobile Ad Hoc Networks (UAVRT-MANET)

The UAV-aided relay node creates a temporary route in MANET. The relay node temporarily joins partitioned networks and offers backup routes.

2.2.5

Predictive-Optimized Link State Routing Protocol (P-OLSR)

P-OLSR used in UAV networks to predict connection quality using GPS. Geographical locations are shared between the nodes by exchanging HELLO packets. Using this method, each node knows its neighbors’ positions. The protocol can be utilized in high-speed UAV networks.

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Predictive Routing for Dynamic UAV Networks (PR-DUAV)

The PR-DUAV routing system uses predicted intermediate node positions to choose paths. The route selection criteria of Dijkstra’s shortest path method are updated to include anticipated pairwise distances. It lowers calculation time and improves the delivery route. The PR-DUAV routing method extends network connection by eliminating links that are likely to be severed owing to communication range exceeding.

2.2.7

Location-Aware Routing for Opportunistic Delay-Tolerant Networks (LAROD)

LAROD is a delay-tolerant geographical routing system based on greedy forwarding and store carry and forward. It has a good network delivery ratio. LAROD is appropriate for mini-drones but consumes too much energy.

2.2.8

Deadline Triggered Pigeon with Travelling Salesman Problem (DTP-TSP-D)

The node on the ground communicates with UAVs in the air to relay information. A UAV serves as a transporting node in this routing system. The message is delivered on time using a genetic algorithm. In terms of packet delivery ratio and average latency, it beats the other conventional routing method.

2.2.9

Mobility Predication-Based Geographic Routing (MPGR)

Inter-UAV networks based on geographic location. MPGR detects the mobility of UAVs using the Gaussian distribution function.

2.2.10

Geographic Position Mobility-Oriented Routing Protocol (GPMOR)

Gauss–Markov mobility model was utilized to predict mobility routing. UAVs are equipped with GPS and communicate their geographical location to nearby UAVs. Additionally, GPMOR predicts and deduces the movements of neighbors.

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Optimized Link State Routing with Mobility and Delay Prediction (OLSR-PMD)

OLSR-PMD uses the Kalman filter technique to find an inter relay of constant neighbor nodes based on node movement and latency predictions.

2.2.12

Distance-Based Greedy Routing (DSGR)

DSGR seeks to speed up UAV route setting. It depends on local forwarding and avoids route configuration. The network is grid-based. Each node’s location is measured. DSGR outperforms Dijkstra’s shortest route.

2.2.13

Robust and Reliable Predictive Routing (RRPR)

RRPR merges both direct and indirect transmission by adjusting the angle. The position and trajectory data are obtained using unicast and geocast routing methods.

2.2.14

Topology-Aware Routing Choosing Scheme (TARCS)

Topology modification is critical in FANETs. Moving nodes in TARCS detect periodic changes in network architecture. TARCS can adapt to FANET topology changes. The new topology determines the routing route. Topology Change Degree is a mobility measure used in FANETs to indicate topology change.

2.2.15

Aeronautical Mobile Ad Hoc Networks (ARPAM)

ARPAM packets include geographical information utilized in the routing process to make the best choices depending on the nodes’ locations.

2.2.16

Reactive Greedy Reactive Protocol (RGR)

If there is no existing route to the desired location, the sender must create an ondemand route to maintain communication with the destination.

2.2.17

Geolocation-Based Multi-Hop Routing Protocol (GLMHRP)

After a while, UAV transmits route data. This data includes the UAV’s position, speed, and direction. The data sending choice is based on greed. Navigation nodes can receive the latest information about nodes, affecting routing performance.

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Position Aware, Secure, and Efficient Mesh Routing (PASER)

A complete safe routing route with valid nodes. It may rapidly identify malicious nodes. PASER is better for dynamic UAV networks.

2.2.19

Location-Aided Delay Tolerant Routing (LADTR)

LADTR uses the Store-Carry-Forward (SCF) method to improve routing protocol performance in UAV networks.

2.2.20

Jamming-Resilient Multipath Routing (JARMROUT)

Failures in limited areas or deliberate interferences and interruptions affect FANET’s overall functionality. The JARMROUT is based on a mix of three central systems: quality of the connection, the volume of traffic, and geographical distance.

2.2.21

Greedy Perimeter Stateless Routing (GPSR)

This technique selects a coordinator based on location. The GPSR protocol works by passing data packets to the nearest neighbor node. Portion forwarding is used when greedy forwarding fails.

2.2.22

Greedy-Hull-Greedy (GHG)

To restrict local recovery, this protocol splits the network into closed sub-spaces.

2.2.23

Greedy-Random-Greedy (GRG)

To find the local minimum, the message is forwarded using the greedy method. However, weak networks do not fit this approach.

2.2.24

Greedy Forwarding (GF)

In contrast to the GRG, this routing algorithm only verifies the node position at every step without making any indicators. After that, it sends the packet to the nearest node.

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Energy-Balanced Greedy Forwarding Routing (EBGR)

Partitioning the forwarding area into four sections. After this one in each potential sub-region, the node is chosen based on its excess energy and the remaining hops to the destination node.

2.2.26

Greedy Distributed Spanning Tree Routing (GDSTR)

Greedy forwarding maintains track of 2-hop neighbor information to avoid local minima.

2.3 Hierarchical Routing The lowest levels of hierarchical routing protocols may create clusters of nodes. Each node has a database of information about its neighbors that is updated by hello packets. To choose the optimal route, each cluster head interacts with the others. Here is a collection of hierarchical routing protocols.

2.3.1

Cluster-Based Routing Protocol (CBRP)

Cluster-based networks may arrange UAVs. The CBRP is split into square grids depending on geographic location. One UAV will serve as a Cluster Head, responsible for data routing. Members of every UAV cluster send data to the cluster head for base station transfer. It preserves routing tables and does not need to find routes, which reduces overhead.

2.3.2

Modularity-Based Dynamic Clustering Relay Routing Protocol (MDCR)

After creating clusters that change throughout time, The UAVs are relocated to vertically projected positions from the cluster centroids. Modularity is assessed and compared inside and across clusters of a network graph. Modularity is assessed and compared inside and across clusters of a network graph. Some of the advantages are transmission power and energy efficiency.

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Bio-inspired Clustering Scheme for Fanets (BICSF)

The BICSF method utilizes energy-aware cluster creation and cluster head selection. BICSF has performed better than others with cluster construction time, energy consumption, cluster lifespan, and delivery success probability.

2.3.4

Hybrid Self-organized Clustering Scheme (HSCS)

It’s a hybrid clustering algorithm of dragonfly algorithm and glowworm swarm optimization used mainly in cognitive IoT-based UAV networks. The HSCS system uses GSO to create clusters and choose cluster heads and DA for efficient cluster management. A method to detect deceased cluster members is used to improve network stability. The route selection function in HSCS selects the next-hop neighbor for data transfer.

2.3.5

Swarm Intelligence-Based Localization and Clustering (SIL-SIC)

The SIL algorithm leverages particle fitness function for intercluster distance, intracluster distance, residual energy, and geographic location with PSO. SIC saves energy, and better particle optimization selects cluster heads. The SIL method improves convergence time and accuracy while reducing computing costs.

2.3.6

Cluster-Based Routing Protocol (CBRP)

To arrange the nodes into overlapping or disjoint clusters, this routing technique uses each cluster head and membership information to identify inter-cluster routes. In order to speed up the route discovery process, the protocol clusters nodes into groups. Inter-cluster and intra-cluster routing use unidirectional connections.

2.3.7

Enhanced Cluster Head Gateway Switch Routing (ECGSR)

Here AODV-based routing method has a congestion management mechanism. The cluster head observes traffic jams by decreasing transmissions and establishes pathways as needed. This method minimizes packet drops, routing overhead, and delay while decreasing network congestion.

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Fisheye State Routing (FSR)

Updates a topological map used to calculate the shortest route. FSR is a protocol that utilizes link-state information to route traffic that performs three functions: Neighbor find Route computation, information distribution.

2.4 Probabilistic Routing Protocols For network congestion and security, probabilistic routing systems discover numerous paths from source to destination. Here are some examples of UAV network algorithms.

2.4.1

Random Walk Routing (RWR)

A “random walk” method forwards packets to the next available neighbor. Attackers cannot anticipate the route, which offers excellent security. However, forwarding is inefficient.

2.4.2

MIMO-Based Random Walk Routing (MRWR)

This routing system adapts communication modes at each step to save energy over random walk routing. The greater complexity costs more.

2.5 AI-Enabled Routing Protocols AI-powered routing protocols utilize ML algorithms to understand the architecture of the network, status of the channel, user behavior, the movement of traffic, and other factors. Using these methods, current networking may be created, especially for dynamic UAV networks. The following is a comprehensive classification of artificial intelligence protocols.

2.5.1

Topology Predictive Routing Protocols

Machine Learning algorithms are used to predict node motion trajectories in order to choose routes. The edge measurements may include distance, energy consumption, latency, bitrate, etc. Topology-based routing protocols include.

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Learning-Based Adaptive Position MAC Protocol (LAP-MAC) Here protocol combines the concept of PPMAC and RLSRP and performs better in terms of directional deafness.

Predictive Dijkstra’s It implies that the intermediary nodes’ positions are predicted using ML techniques. The shortest route based on Dijkstra’s is found by combining predicted information with path specifications.

Predictive Greedy Routing (PGR) To get to the target node, each node guesses its neighbors’ whereabouts.

Predictive Optimized Link State Routing (P-OLSR) This method uses GPS data to compute an Expected Transmission (ETX) count measure to assess the nature of the connection while determining the optimum route.

Geographic Position Mobility Oriented Routing (GP-MOR) To reduce the effect of highly dynamic UAV motions, it adopts the Gauss Markov mobility model. This method uses the mobility connection as well as the Euclidean distance for better choices.

Mobility Prediction Clustering Algorithm (MPCA) Helps find cluster heads in clustered environments based on node reliability and predicts the network topology. This method also guarantees cluster stability.

Robust and Reliable Predictive (RARP) This technique uses a combination of unicasting and geocasting routing technologies to route traffic. Directional transmission is used to monitor topology changes and guess intermediate node positions using a 3-D estimate.

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Scoped Flooding and Mobility Prediction-Based RGR (SFMPRGR) A distance between two nodes is computed by combining data packets’ mobility prediction (velocity, direction, timestamp). Since GGF saves lost data packets if the following hop is out of range, this method works well for dynamic networks.

Q-Learning-Based Geographic Ad Hoc Routing Protocol (QGeo) Nodes make geographic routing choices dynamically with no prior knowledge of the network’s architecture. It has a neighbor table and a Q-learning element. The GPS’s position estimate module continually updates the GPS’s stated location or other localization techniques’ current location.

Predictive Ad Hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge (PARRoT) As with the previous routing protocol, this one-use mobility control information predicts future agent movement, predicts its future location based on its present position, and propagates it to neighboring nodes.

Fuzzy Logic Reinforcement Learning-Based Routing Algorithm (FLRLR) Fuzzy logic is applied to find a live view of the closest nodes. It then uses reinforcement learning to decrease the typical hops produced via training and receive future benefits.

2.5.2

Self-adaptive Learning-Based Routing Protocols

Most learning-based routing systems utilize Reinforcement Learning (RL) to learn how to route. In addition to being independent of topologies modeling and channel estimation, RL-based algorithms provide several other advantages. These learningbased routing algorithms have been developed to support dynamic UAV networks better, as seen below.

Q-Routing It learns based on experience. They are stored in a Q-table by each node. Each node chooses the following node that reduces trip time. The preceding node’s Q-values are updated after receiving a packet.

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Predictive Q-Routing (PQ-Routing) By predicting traffic trends, they learned and stored new optimum policies under decreasing load circumstances. To re-examine the routes that have been abandoned owing to traffic delays. To modify the probing frequency, use the route recovery rate estimate.

Dual Reinforcement Q-Routing (DRQ-Routing) Each communication hop is based on the transmitter and recipient of each communication hop adding data to the packet they get from their neighbors.

Credence-Based Q-Routing (CrQ-Routing) and Probabilistic Credence-Based Q-Routing (PCrQ-Routing) With these two techniques, congestion traffic is captured to enhance learning process.

Full-Echo Q-Routing Each node gets the expected journey time of each neighbor, which is used to update each neighbor’s Q-values.

Full-Echo Q-Routing with Adaptive Learning Rate Adaptive learning is combined with full-echo Q-Routing to boost exploration efficiency.

Adaptive Q-Routing with Random Echo and Route Memory (AQRERM) the baseline method’s overshoot and settling time, and learning stability have been improved.

Poisson’s Probability-Based Q-Routing (PBQ-Routing) For intermittently linked networks, this method utilizes transmission probability and Poisson’s probability to manage transmission energy.

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Delayed Q-Routing (DQ-Routing) The value function is not overestimated, so the routing protocol is employed with two sets of value functions.

QoS-Aware Q-Routing (Q2-Routing) It guarantees traffic Quality of Service by varying the learning rate depending on Q-value changes.

Q-Network Enhanced Geographic Ad Hoc Routing Protocol Based on GPSR (QNGPSR) Q-network is used to assess the quality of various route pathways. Q values decide the forwarding. This data is used to evaluate the environment and node categories.

Adaptive and Reliable Routing Protocol with Deep Reinforcement Learning (ARdeep) Automatically characterizes network changes using a Markov Decision Process model for formulating routing decisions considering node’s remaining energy, the distance between the nodes, link status, the link’s expected connection time, the packet error ratio.

Traffic-Aware Q-Network Enhanced Routing Protocol Based on GPSR (TQNGPSR) As a traffic balancing technique, this algorithm utilizes neighbor congestion information to assess the quality of a wireless connection. The protocol then decides on routing depending on each wireless link’s assessment.

Q-learning Based Multi-objective Optimization Routing Protocol (QMR) This new method adapts the learning parameters to the network’s dynamics. Unknown optimum routes are investigated while re-estimating adjacent connections to choose the most trustworthy next hop.

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Q-Learning-Based Fuzzy Logic for Multi-objective Routing Algorithm in Flying Ad Hoc Networks (QLFLMOR) This method aids in selecting routing routes based on per-link and total path performance. Each UAV determines the best route to the target using a fuzzy system with the link- and path-level characteristics.

Adaptive UAV-Assisted Geographic Routing with Q-Learning (QAGR) When a routing request comes in, the ground vehicle looks up the Q-table filtered according to the global routing route to see which node is the best fit.

Fully-Echoed Q-Routing with Simulated Annealing Inference for Flying Ad Hoc Networks (FESAIQ-Routing) The temperature parameter quantifies the effect of node mobility on Q-value update rates. The gradual change in the exploration step improves exploration and accommodates sudden changes in network dynamicity.

2.6 Deterministic Routing Protocol A node’s subsequent motion is known to its neighbors. Because UAVs fly in regulated formations, this protocol may be used in networks. A tree method might be used to choose routes if all nodes are aware of the node movement, accessibility, and behavior of other nodes. Nodes are classified as child or root nodes in a tree. The shortest path is chosen from the tree.

2.7 Stochastic Routing Protocols The networks that exhibit unexpected behavior make use of this protocol. In this case, packet delivery choice is critical. This may be done by re-sending the data to the next node. Some of the stochastic routing protocols include. • • • •

Epidemic Routing-based Approach Estimation-based Routing Node Movement and Control-based Routing, and Coding-based Routing.

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Epidemic Routing-Based Approach

Moving nodes are not linked with each other. The other nodes are flooded with the same messages several times. Nodes estimate the likelihood of each connection instead of sending messages to the following nodes. The node needs big buffers, bandwidth, and power for this approach.

2.7.2

Estimation-Based Approach

Each node stores the packet and forwards it depending on the estimate. Small networks benefit from random nodes, while extensive networks suffer from estimated overheads.

2.7.3

Node Movement and Control-Based Approach

When detached from adjacent nodes, and nodes determine whether to wait for reconnection. In a reactive situation, waiting for re-connection may cause unacceptable transmission delays.

2.7.4

Coding-Based Approach

This protocol utilizes network coding to minimize duplicating data and retransmission. This technique may be used in UAV networks when retransmission requires finding a new route due to interruption.

2.8 Social Network-Based Approach Using, a wide variety of networking protocols is unrealistic when nodes’ mobility is fixed. When nodes visit a location, they may save the location’s data in a database. Using this information, the node may rapidly pick routes for future tries. This protocol is helpful for UAV node information storage. SN-based routing demands more buffer space and bandwidth.

3 Vulnerabilities in UAV Due to the high usage of UAV networks in smart cities, Security, Safety, and Privacy have become the primary concern. The main task of the UAVs is to gather information, analyze, and transmit sensitive data. Attackers try to impersonate or poison the

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Fig. 4 Different types of vulnerabilities based on GNSS

routing table in the routing protocols to perform the attack. So secured communication becomes a significant concern because UAVs are vulnerable to cyber-attacks, data manipulation, and data theft [1]. The effects vary as a result of the different tactics and targets of attacks. Modern UAVs depend largely on Global Navigation Satellite System (GNSS) for guidance, navigation, and control (GNC). The Global Positioning System (GPS) is the most commonly used GNSS. GPS-dependent UAVs need precise, reliable, and continuous location data to operate safely. However, research has revealed that GPS signals may be jammed or spoofed due to intrinsic flaws. The civil GPS systems lack encryption and authentication, making the satellite signals readily replicable or fabricated to launch GPS spoofing attacks. Numerous vulnerabilities exist with GNSS, which is a synonym of GPS given in Fig. 4. According to the Royal Academy of Engineering [10], GNSS vulnerabilities fall across three main categories:

3.1 System-Related Vulnerabilities The satellites themselves (for example, a lack of available satellites or the transmission of poor signals); receiver failure and outage; or issues with the augmentation systems that boost the GNSS signal (e.g. EGNOS). This covers both failures induced by assaults on GNSS systems and outages caused by the orbital environment, such as geomagnetic storms, among other things.

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3.2 Propagation Channel Vulnerabilities It includes signal interruptions caused by environmental variations caused by space weather phenomena and mistakes caused by signals reflected off buildings or other objects (multipath).

3.3 Interference’s Vulnerabilities They are the disrupted signals from other sources. Commercial power transmitters or radars may create unintentional interference; however, it is also possible that receivers will be jammed on purpose, sending false GPS signals (GPS Spoofing) and delayed or rebroadcasted signals (meaconing). Particular vulnerabilities based on interferences attempt to steal data through security flaws in communication connections, while others attempt to spoof sensors, such as GPS spoofing and jamming. As with conventional network security, the Availability, Confidentiality, Integrity of UAV communications are all security objectives. GPS Spoofing and Jamming attacks compromise the network, which challenges the CIA rule Fig. 5 and tries to hijack the UAV, intercept the communication, and act as MTM attacks. Additional characteristics like privacy, authenticity, accountability, non-repudiation, and reliability may be the goal of UAV security, depending on the specific prerequisites. GPS Spoofing and Jamming attacks are discussed in detail in the coming sections.

Fig. 5 Objectives of security

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Fig. 6 GPS spoofing attack

4 GPS Spoofing and Jamming Attacks 4.1 GPS Spoofing Attack GPS Spoofing is duplicating or falsifying GPS signals to mislead a targeted GPS device or recipient, altering its characteristics such as position, velocity, and time. If a GPS spoofing attack is successful, it might cause a drone to crash or alter the flying path. GPS Spoofing is explained in Fig. 6. Various studies indicate that an attacker may push a GPS-guided drone off course or even hijack it if they know its present location and planned flight route. A drone may be made to fly across no-fly zones by spoofing the “Geo-fencing” safety function. This weakness allows drug traffickers and others to cross prison boundaries for drug trafficking and monitoring illegally. Spoofing is more subtle: a fake signal sent by a ground station that merely confuses a satellite receiver [11].

4.2 Jamming Attack Jamming is typically caused by GNSS signal interference. However, accidental jamming may occur due to space weather or defective equipment that emits signals on the L1 frequency, interfering with GNSS reception. Intentional jamming is used to overwhelm weak GNSS receivers [12]. Personal Protection Devices (PPD) are often employed in addition to military jammers. These are easily accessible and cheap, yet most nations ban them. GPS has an inherent vulnerability that has existed since

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Fig. 7 Jamming attack

the system’s inception in the 1980s. This is an unavoidable problem since GPS is intended to be shared and utilized by all civilian devices, exposing the signal shown in Fig. 7. Table 1 details many real-world instances of GNS spoofing and jamming attacks against UAVs [13]. Cyber security designs are classified into two broad categories: cyber defense and cyber detection. On the one hand, the former method focuses on data privacy and confidentiality and ways to mitigate outsider attacks (i.e., Attackers located outside the local network’s borders) that are most inclined to compromise network integrity. In contrast to that, later methods are mostly employed to identify network intrusions. As a result, they are capable of detecting internal and external attacks with incredible accuracy. These latter methods depend on Intrusion Prevention Systems (IPS) and Intrusion Detection Systems (IDS) to anticipate and identify an attacker’s misbehavior, respectively. With the continuous growth in research of UAVs and their security and privacy issues, this chapter comprises various types of Machine Learning, Deep Learning, Intrusion Detection System, and Intrusion Prevention System models for detection and prevention of GPS Spoofing and Jamming attacks are covered in Sect. 5. As mentioned above, some countermeasures to the attacks are covered in Sect. 3 and conclude with the future prediction of GPS Spoofing and Jamming attacks.

5 Literature Survey

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Table 1 GPS spoofing and jamming attacks real-time incidents Month, year

Actual incidents of GPS spoofing and jamming UAV attacks

Dec 2019

This periodic GPS signal loss has been linked to a jammer that was set up at a pig farm near Harbin Airport in northeast China

Jan 2020

After discovering that GPS jamming devices are utilized in 85% of the country’s cargo truck thefts, Mexico adopts anti-jammer legislation

Feb 2020

A light aircraft pilot’s worrisome report to NASA’s Aviation Safety Reporting System indicates that a US Department of Defense (DoD) drone may be faking the signals A GPS and Galileo signal interruption has been reported at a French GNSS equipment manufacturer’s facility regularly

Mar 2020

‘Circle-style’ In Iran’s capital, Tehran, GPS spoofing has been recorded. An unidentified GPS user alerts the US government that his or her (unspecified) gadget seems to be moving in a circle around the Iranian Army training institution while it is parked

Jun 2020

As in the past, GPS jamming is causing havoc in the extreme north of Norway, not far from the Russian border

Aug 2020

After a drone accident in the UK, the dangers of jamming and spoofing interference to unmanned aerial vehicles have been brought to light (UAVs) Many Chinese fishing boats have been accused of lying about their whereabouts to conceal illegal fishing operations

Sep 2020

The Maritime Administration of the United States urges the maritime sector to be alert to GPS interruptions anywhere in the world

Nov 2020

According to Fortune, GPS failures are now frequent on commercial aircraft routes between the United States, Europe, and the Middle East, echoing the MARAD warning from September

Aug 2021

Iran-Israel maritime tensions rise as a deadly drone strike targets a ship

Sep 2021

GPS Spoofing in Pokemon Go

Author

Techniques used

Enhancements

Zouhri et al. [14]

Security Communication Protocol Between UAV and GCS (SPUAV)

Confidentiality, integrity, authentication based on sufficient energy, network connectivity, mobility, and network security

Chen et al. [15]

Lightweight active GPS spoofing detection method

Real-time detection without reducing the detection rate

Haque et al. [16]

Identity-based encryption and Security and confidentiality selective encryption methods flexibility, efficiency, communication, storage, overhead reduction, and data hiding mechanism

Wang et al. [17]

LSTM model

Detects in a short time and quickly, enhances computing efficiency, no upgradations needed, tested in various platforms (continued)

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(continued) Author

Techniques used

Enhancements

Dasgupta et al. [18]

LSTM model for spoofing detection

The maximized attack detection rate

Jansen et al. [19]

Crowd-GPS-Sec

Detects the attack in