Romil Rawat, PhD, is an assistant professor at Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore. With over 12 years of teaching experience, he has published numerous papers in scholarly journals and conferences. He has also published book chapters and is a board member of two scientific journals. He has received several research grants and has hosted research events, workshops, and training programs. He also has several patents to his credit. A Mary Sowjanya, PhD, is a faculty member in the Department of Computer Science and Systems Engineering at Andhra University, India. She has three patents to her credit and has more than 70 research publications. She also received the “Young Faculty Research Fellowship Award” under the Viswerayya program from the government of India. Syed Imran Patel, is a lecturer, education program manager, and lead internal verifier at Bahrain Training Institute, HEC, EDUC-Information System Training Programs, Ministry of Education, Bahrain. With his expertise, he contributes to the Quality Assurance Committee, the Grade and Credit Transfer Committee, and the Curriculum Development Committee. Varshali Jaiswal, PhD, is an assistant professor at Vellore Institute of Technology, Bhopal, India. She has over 12 years of experience in the field of academics. She has published more than seven papers in international journals and conferences. Imran Khan, is a faculty member at the Bahrain Training Institute, Higher Education Council, Ministry of Education, Bahrain. Before this, he was a lecturer at Sirt University, Ministry of Education, Libya, and an assistant professor at Osmania University. Allam Balaram, PhD, is a professor in the Department of Information Technology, MLR Institute of Technology, India. A professional with over 16 years of teaching experience and over eight years of research and development experience, he has published 17 papers.

• Preface xiii1 Anomalous Activity Detection Using Deep Learning Techniques in Autonomous Vehicles 1Amit Juyal, Sachin Sharma and Priya Matta1.1 Introduction 21.1.1 Organization of Chapter 21.2 Literature Review 31.3 Artificial Intelligence in Autonomous Vehicles 71.4 Technologies Inside Autonomous Vehicle 91.5 Major Tasks in Autonomous Vehicle Using AI 111.6 Benefits of Autonomous Vehicle 121.7 Applications of Autonomous Vehicle 131.8 Anomalous Activities and Their Categorization 131.9 Deep Learning Methods in Autonomous Vehicle 141.10 Working of Yolo 171.11 Proposed Methodology 181.12 Proposed Algorithms 201.13 Comparative Study and Discussion 211.14 Conclusion 23References 232 Algorithms and Difficulties for Autonomous Cars Based on Artificial Intelligence 27Sumit Dhariwal, Avani Sharma and Avinash Raipuria2.1 Introduction 272.1.1 Algorithms for Machine Learning in Autonomous Driving 302.1.2 Regression Algorithms 302.1.3 Design Identification Systems (Classification) 312.1.4 Grouping Concept 312.1.5 Decision Matrix Algorithms 312.2 In Autonomous Cars, AI Algorithms are Applied 322.2.1 Algorithms for Route Planning and Control 322.2.2 Method for Detecting Items 322.2.3 Algorithmic Decision-Making 332.3 AI’s Challenges with Self-Driving Vehicles 332.3.1 Feedback in Real Time 332.3.2 Complexity of Computation 342.3.3 Black Box Behavior 342.3.4 Precision and Dependability 352.3.5 The Safeguarding 352.3.6 AI and Security 352.3.7 AI and Ethics 362.4 Conclusion 36References 363 Trusted Multipath Routing for Internet of Vehicles against DDoS Assault Using Brink Controller in Road Awareness (tmrbc-iov) 39Piyush Chouhan and Swapnil Jain3.1 Introduction 403.2 Related Work 473.3 VANET Grouping Algorithm (VGA) 503.4 Extension of Trusted Multipath Distance Vector Routing (TMDR-Ext) 513.5 Conclusion 57References 584 Technological Transformation of Middleware and Heuristic Approaches for Intelligent Transport System 61Rajender Kumar, Ravinder Khanna and Surender Kumar4.1 Introduction 614.2 Evolution of VANET 624.3 Middleware Approach 644.4 Heuristic Search 654.5 Reviews of Middleware Approaches 724.6 Reviews of Heuristic Approaches 754.7 Conclusion and Future Scope 78References 795 Recent Advancements and Research Challenges in Design and Implementation of Autonomous Vehicles 83Mohit Kumar and V. M. Manikandan5.1 Introduction 845.1.1 History and Motivation 855.1.2 Present Scenario and Need for Autonomous Vehicles 855.1.3 Features of Autonomous Vehicles 865.1.4 Challenges Faced by Autonomous Vehicles 865.2 Modules/Major Components of Autonomous Vehicles 875.2.1 Levels of Autonomous Vehicles 875.2.2 Functional Components of An Autonomous Vehicle 895.2.3 Traffic Control System of Autonomous Vehicles 915.2.4 Safety Features Followed by Autonomous Vehicles 915.3 Testing and Analysis of An Autonomous Vehicle in a Virtual Prototyping Environment 945.4 Application Areas of Autonomous Vehicles 955.5 Artificial Intelligence (AI) Approaches for Autonomous Vehicles 975.5.1 Pedestrian Detection Algorithm (PDA) 975.5.2 Road Signs and Traffic Signal Detection 995.5.3 Lane Detection System 1025.6 Challenges to Design Autonomous Vehicles 1045.7 Conclusion 110References 1106 Review on Security Vulnerabilities and Defense Mechanism in Drone Technology 113Chaitanya Singh and Deepika Chauhan6.1 Introduction 1136.2 Background 1146.3 Security Threats in Drones 1156.3.1 Electronics Attacks 1156.3.1.1 GPS and Communication Jamming Attacks 1166.3.1.2 GPS and Communication Spoofing Attacks 1176.3.1.3 Eavesdropping 1176.3.1.4 Electromagnetic Interference 1206.3.1.5 Laser Attacks 1206.3.2 Cyber-Attacks 1206.3.2.1 Man-in-Middle Attacks 1216.3.2.2 Black Hole and Grey Hole 1216.3.2.3 False Node Injection 1216.3.2.4 False Communication Data Injection 1216.3.2.5 Firmware’s Manipulations 1216.3.2.6 Sleep Deprivation 1226.3.2.7 Malware Infection 1226.3.2.8 Packet Sniffing 1226.3.2.9 False Database Injection 1226.3.2.10 Replay Attack 1236.3.2.11 Network Isolations 1236.3.2.12 Code Injection 1236.3.3 Physical Attacks 1236.3.3.1 Key Logger Attacks 1236.3.3.2 Camera Spoofing 1246.4 Defense Mechanism and Countermeasure Against Attacks 1246.4.1 Defense Techniques for GPS Spoofing 1246.4.2 Defense Technique for Man-in-Middle Attacks 1246.4.3 Defense against Keylogger Attacks 1276.4.4 Defense against Camera Spoofing Attacks 1276.4.5 Defense against Buffer Overflow Attacks 1286.4.6 Defense against Jamming Attack 1286.5 Conclusion 128References 1287 Review of IoT-Based Smart City and Smart Homes Security Standards in Smart Cities and Home Automation 133Dnyaneshwar Vitthal Kudande, Chaitanya Singh and Deepika Chauhan7.1 Introduction 1337.2 Overview and Motivation 1347.3 Existing Research Work 1367.4 Different Security Threats Identified in IoT-Used Smart Cities and Smart Homes 1367.4.1 Security Threats at Sensor Layer 1367.4.1.1 Eavesdropping Attacks 1377.4.1.2 Node Capturing Attacks 1387.4.1.3 Sleep Deprivation Attacks 1387.4.1.4 Malicious Code Injection Attacks 1387.4.2 Security Threats at Network Layer 1387.4.2.1 Distributed Denial of Service (DDOS) Attack 1397.4.2.2 Sniffing Attack 1397.4.2.3 Routing Attack 1397.4.2.4 Traffic Examination Attacks 1407.4.3 Security Threats at Platform Layer 1407.4.3.1 SQL Injection 1407.4.3.2 Cloud Malware Injection 1417.4.3.3 Storage Attacks 1417.4.3.4 Side Channel Attacks 1417.4.4 Security Threats at Application Layer 1417.4.4.1 Sniffing Attack 1417.4.4.2 Reprogram Attack 1427.4.4.3 Data Theft 1427.4.4.4 Malicious Script Attack 1427.5 Security Solutions For IoT-Based Environment in Smart Cities and Smart Homes 1427.5.1 Blockchain 1427.5.2 Lightweight Cryptography 1437.5.3 Biometrics 1437.5.4 Machine Learning 1437.6 Conclusion 144References 1448 Traffic Management for Smart City Using Deep Learning 149Puja Gupta and Upendra Singh8.1 Introduction 1508.2 Literature Review 1518.3 Proposed Method 1548.4 Experimental Evaluation 1558.4.1 Hardware and Software Configuration 1558.4.2 About Dataset 1568.4.3 Implementation 1568.4.4 Result 1578.5 Conclusion 158References 1589 Cyber Security and Threat Analysis in Autonomous Vehicles 161Siddhant Dash and Chandrashekhar Azad9.1 Introduction 1629.2 Autonomous Vehicles 1629.2.1 Autonomous vs. Automated 1639.2.2 Significance of Autonomous Vehicles 1639.2.3 Challenges in Autonomous Vehicles 1649.2.4 Future Aspects 1659.3 Related Works 1659.4 Security Problems in Autonomous Vehicles 1679.4.1 Different Attack Surfaces and Resulting Attacks 1689.5 Possible Attacks in Autonomous Vehicles 1709.5.1 Internal Network Attacks 1709.5.2 External Attacks 1739.6 Defence Strategies against Autonomous Vehicle Attacks 1759.6.1 Against Internal Network Attacks 1759.6.2 Against External Attack 1769.7 Cyber Threat Analysis 1779.8 Security and Safety Standards in AVs 1789.9 Conclusion 179References 17910 Big Data Technologies in UAV’s Traffic Management System: Importance, Benefits, Challenges and Applications 181Piyush Agarwal, Sachin Sharma and Priya Matta10.1 Introduction 18210.2 Literature Review 18310.3 Overview of UAV’s Traffic Management System 18510.4 Importance of Big Data Technologies and Algorithm 18610.5 Benefits of Big Data Techniques in UTM 18910.6 Challenges of Big Data Techniques in UTM 19010.7 Applications of Big Data Techniques in UTM 19210.8 Case Study and Future Aspects 19810.9 Conclusion 199References 19911 Reliable Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles 203Ambika N.11.1 Introduction 20411.2 Literature Survey 20711.3 Proposed Architecture 21011.4 Experimental Results 21111.5 Analysis of the Proposal 21111.6 Conclusion 213References 21412 Multitask Learning for Security and Privacy in IoV (Internet of Vehicles) 217Malik Mustafa, Ahmed Mateen Buttar, Guna Sekhar Sajja, Sanjeev Gour, Mohd Naved and P. William12.1 Introduction 21812.2 IoT Architecture 22012.3 Taxonomy of Various Security Attacks in Internet of Things 22112.3.1 Perception Layer Attacks 22112.3.2 Network Layer Attacks 22312.3.3 Application Layer Attacks 22412.4 Machine Learning Algorithms for Security and Privacy in IoV 22512.5 A Machine Learning-Based Learning Analytics Methodology for Security and Privacy in Internet of Vehicles 22712.5.1 Methodology 22712.5.2 Result Analysis 22912.6 Conclusion 230References 23013 ML Techniques for Attack and Anomaly Detection in Internet of Things Networks 235Vinod Mahor, Sadhna Bijrothiya, Rina Mishra and Romil Rawat13.1 Introduction 23613.2 Internet of Things 23613.3 Cyber-Attack in IoT 23913.4 IoT Attack Detection in ML Technics 24413.5 Conclusion 249References 24914 Applying Nature-Inspired Algorithms for Threat Modeling in Autonomous Vehicles 253Manas Kumar Yogi, Siva Satya Prasad Pennada, Sreeja Devisetti and Sri Siva Lakshmana Reddy Dwarampudi14.1 Introduction 25414.2 Related Work 26314.3 Proposed Mechanism 26514.4 Performance Results 26814.5 Future Directions 27014.6 Conclusion 273References 27315 The Smart City Based on AI and Infrastructure: A New Mobility Concepts and Realities 277Vinod Mahor, Sadhna Bijrothiya, Rina Mishra, Romil Rawat and Alpesh Soni15.1 Introduction 27815.2 Research Method 28015.3 Vehicles that are Both Networked and Autonomous 28215.4 Personal Aerial Automobile Vehicles and Unmanned Aerial Automobile Vehicles 28715.5 Mobile Connectivity as a Service 28815.6 Major Role for Smart City Development with IoT and Industry 4.0 28915.7 Conclusion 291References 292Index 297

Romil Rawat, PhD, is an assistant professor at Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore. With over 12 years of teaching experience, he has published numerous papers in scholarly journals and conferences. He has also published book chapters and is a board member of two scientific journals. He has received several research grants and has hosted research events, workshops, and training programs. He also has several patents to his credit. A Mary Sowjanya, PhD, is a faculty member in the Department of Computer Science and Systems Engineering at Andhra University, India. She has three patents to her credit and has more than 70 research publications. She also received the “Young Faculty Research Fellowship Award” under the Viswerayya program from the government of India. Syed Imran Patel, is a lecturer, education program manager, and lead internal verifier at Bahrain Training Institute, HEC, EDUC-Information System Training Programs, Ministry of Education, Bahrain. With his expertise, he contributes to the Quality Assurance Committee, the Grade and Credit Transfer Committee, and the Curriculum Development Committee. Varshali Jaiswal, PhD, is an assistant professor at Vellore Institute of Technology, Bhopal, India. She has over 12 years of experience in the field of academics. She has published more than seven papers in international journals and conferences. Imran Khan, is a faculty member at the Bahrain Training Institute, Higher Education Council, Ministry of Education, Bahrain. Before this, he was a lecturer at Sirt University, Ministry of Education, Libya, and an assistant professor at Osmania University. Allam Balaram, PhD, is a professor in the Department of Information Technology, MLR Institute of Technology, India. A professional with over 16 years of teaching experience and over eight years of research and development experience, he has published 17 papers.

• Preface xiii1 Anomalous Activity Detection Using Deep Learning Techniques in Autonomous Vehicles 1Amit Juyal, Sachin Sharma and Priya Matta1.1 Introduction 21.1.1 Organization of Chapter 21.2 Literature Review 31.3 Artificial Intelligence in Autonomous Vehicles 71.4 Technologies Inside Autonomous Vehicle 91.5 Major Tasks in Autonomous Vehicle Using AI 111.6 Benefits of Autonomous Vehicle 121.7 Applications of Autonomous Vehicle 131.8 Anomalous Activities and Their Categorization 131.9 Deep Learning Methods in Autonomous Vehicle 141.10 Working of Yolo 171.11 Proposed Methodology 181.12 Proposed Algorithms 201.13 Comparative Study and Discussion 211.14 Conclusion 23References 232 Algorithms and Difficulties for Autonomous Cars Based on Artificial Intelligence 27Sumit Dhariwal, Avani Sharma and Avinash Raipuria2.1 Introduction 272.1.1 Algorithms for Machine Learning in Autonomous Driving 302.1.2 Regression Algorithms 302.1.3 Design Identification Systems (Classification) 312.1.4 Grouping Concept 312.1.5 Decision Matrix Algorithms 312.2 In Autonomous Cars, AI Algorithms are Applied 322.2.1 Algorithms for Route Planning and Control 322.2.2 Method for Detecting Items 322.2.3 Algorithmic Decision-Making 332.3 AI’s Challenges with Self-Driving Vehicles 332.3.1 Feedback in Real Time 332.3.2 Complexity of Computation 342.3.3 Black Box Behavior 342.3.4 Precision and Dependability 352.3.5 The Safeguarding 352.3.6 AI and Security 352.3.7 AI and Ethics 362.4 Conclusion 36References 363 Trusted Multipath Routing for Internet of Vehicles against DDoS Assault Using Brink Controller in Road Awareness (tmrbc-iov) 39Piyush Chouhan and Swapnil Jain3.1 Introduction 403.2 Related Work 473.3 VANET Grouping Algorithm (VGA) 503.4 Extension of Trusted Multipath Distance Vector Routing (TMDR-Ext) 513.5 Conclusion 57References 584 Technological Transformation of Middleware and Heuristic Approaches for Intelligent Transport System 61Rajender Kumar, Ravinder Khanna and Surender Kumar4.1 Introduction 614.2 Evolution of VANET 624.3 Middleware Approach 644.4 Heuristic Search 654.5 Reviews of Middleware Approaches 724.6 Reviews of Heuristic Approaches 754.7 Conclusion and Future Scope 78References 795 Recent Advancements and Research Challenges in Design and Implementation of Autonomous Vehicles 83Mohit Kumar and V. M. Manikandan5.1 Introduction 845.1.1 History and Motivation 855.1.2 Present Scenario and Need for Autonomous Vehicles 855.1.3 Features of Autonomous Vehicles 865.1.4 Challenges Faced by Autonomous Vehicles 865.2 Modules/Major Components of Autonomous Vehicles 875.2.1 Levels of Autonomous Vehicles 875.2.2 Functional Components of An Autonomous Vehicle 895.2.3 Traffic Control System of Autonomous Vehicles 915.2.4 Safety Features Followed by Autonomous Vehicles 915.3 Testing and Analysis of An Autonomous Vehicle in a Virtual Prototyping Environment 945.4 Application Areas of Autonomous Vehicles 955.5 Artificial Intelligence (AI) Approaches for Autonomous Vehicles 975.5.1 Pedestrian Detection Algorithm (PDA) 975.5.2 Road Signs and Traffic Signal Detection 995.5.3 Lane Detection System 1025.6 Challenges to Design Autonomous Vehicles 1045.7 Conclusion 110References 1106 Review on Security Vulnerabilities and Defense Mechanism in Drone Technology 113Chaitanya Singh and Deepika Chauhan6.1 Introduction 1136.2 Background 1146.3 Security Threats in Drones 1156.3.1 Electronics Attacks 1156.3.1.1 GPS and Communication Jamming Attacks 1166.3.1.2 GPS and Communication Spoofing Attacks 1176.3.1.3 Eavesdropping 1176.3.1.4 Electromagnetic Interference 1206.3.1.5 Laser Attacks 1206.3.2 Cyber-Attacks 1206.3.2.1 Man-in-Middle Attacks 1216.3.2.2 Black Hole and Grey Hole 1216.3.2.3 False Node Injection 1216.3.2.4 False Communication Data Injection 1216.3.2.5 Firmware’s Manipulations 1216.3.2.6 Sleep Deprivation 1226.3.2.7 Malware Infection 1226.3.2.8 Packet Sniffing 1226.3.2.9 False Database Injection 1226.3.2.10 Replay Attack 1236.3.2.11 Network Isolations 1236.3.2.12 Code Injection 1236.3.3 Physical Attacks 1236.3.3.1 Key Logger Attacks 1236.3.3.2 Camera Spoofing 1246.4 Defense Mechanism and Countermeasure Against Attacks 1246.4.1 Defense Techniques for GPS Spoofing 1246.4.2 Defense Technique for Man-in-Middle Attacks 1246.4.3 Defense against Keylogger Attacks 1276.4.4 Defense against Camera Spoofing Attacks 1276.4.5 Defense against Buffer Overflow Attacks 1286.4.6 Defense against Jamming Attack 1286.5 Conclusion 128References 1287 Review of IoT-Based Smart City and Smart Homes Security Standards in Smart Cities and Home Automation 133Dnyaneshwar Vitthal Kudande, Chaitanya Singh and Deepika Chauhan7.1 Introduction 1337.2 Overview and Motivation 1347.3 Existing Research Work 1367.4 Different Security Threats Identified in IoT-Used Smart Cities and Smart Homes 1367.4.1 Security Threats at Sensor Layer 1367.4.1.1 Eavesdropping Attacks 1377.4.1.2 Node Capturing Attacks 1387.4.1.3 Sleep Deprivation Attacks 1387.4.1.4 Malicious Code Injection Attacks 1387.4.2 Security Threats at Network Layer 1387.4.2.1 Distributed Denial of Service (DDOS) Attack 1397.4.2.2 Sniffing Attack 1397.4.2.3 Routing Attack 1397.4.2.4 Traffic Examination Attacks 1407.4.3 Security Threats at Platform Layer 1407.4.3.1 SQL Injection 1407.4.3.2 Cloud Malware Injection 1417.4.3.3 Storage Attacks 1417.4.3.4 Side Channel Attacks 1417.4.4 Security Threats at Application Layer 1417.4.4.1 Sniffing Attack 1417.4.4.2 Reprogram Attack 1427.4.4.3 Data Theft 1427.4.4.4 Malicious Script Attack 1427.5 Security Solutions For IoT-Based Environment in Smart Cities and Smart Homes 1427.5.1 Blockchain 1427.5.2 Lightweight Cryptography 1437.5.3 Biometrics 1437.5.4 Machine Learning 1437.6 Conclusion 144References 1448 Traffic Management for Smart City Using Deep Learning 149Puja Gupta and Upendra Singh8.1 Introduction 1508.2 Literature Review 1518.3 Proposed Method 1548.4 Experimental Evaluation 1558.4.1 Hardware and Software Configuration 1558.4.2 About Dataset 1568.4.3 Implementation 1568.4.4 Result 1578.5 Conclusion 158References 1589 Cyber Security and Threat Analysis in Autonomous Vehicles 161Siddhant Dash and Chandrashekhar Azad9.1 Introduction 1629.2 Autonomous Vehicles 1629.2.1 Autonomous vs. Automated 1639.2.2 Significance of Autonomous Vehicles 1639.2.3 Challenges in Autonomous Vehicles 1649.2.4 Future Aspects 1659.3 Related Works 1659.4 Security Problems in Autonomous Vehicles 1679.4.1 Different Attack Surfaces and Resulting Attacks 1689.5 Possible Attacks in Autonomous Vehicles 1709.5.1 Internal Network Attacks 1709.5.2 External Attacks 1739.6 Defence Strategies against Autonomous Vehicle Attacks 1759.6.1 Against Internal Network Attacks 1759.6.2 Against External Attack 1769.7 Cyber Threat Analysis 1779.8 Security and Safety Standards in AVs 1789.9 Conclusion 179References 17910 Big Data Technologies in UAV’s Traffic Management System: Importance, Benefits, Challenges and Applications 181Piyush Agarwal, Sachin Sharma and Priya Matta10.1 Introduction 18210.2 Literature Review 18310.3 Overview of UAV’s Traffic Management System 18510.4 Importance of Big Data Technologies and Algorithm 18610.5 Benefits of Big Data Techniques in UTM 18910.6 Challenges of Big Data Techniques in UTM 19010.7 Applications of Big Data Techniques in UTM 19210.8 Case Study and Future Aspects 19810.9 Conclusion 199References 19911 Reliable Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles 203Ambika N.11.1 Introduction 20411.2 Literature Survey 20711.3 Proposed Architecture 21011.4 Experimental Results 21111.5 Analysis of the Proposal 21111.6 Conclusion 213References 21412 Multitask Learning for Security and Privacy in IoV (Internet of Vehicles) 217Malik Mustafa, Ahmed Mateen Buttar, Guna Sekhar Sajja, Sanjeev Gour, Mohd Naved and P. William12.1 Introduction 21812.2 IoT Architecture 22012.3 Taxonomy of Various Security Attacks in Internet of Things 22112.3.1 Perception Layer Attacks 22112.3.2 Network Layer Attacks 22312.3.3 Application Layer Attacks 22412.4 Machine Learning Algorithms for Security and Privacy in IoV 22512.5 A Machine Learning-Based Learning Analytics Methodology for Security and Privacy in Internet of Vehicles 22712.5.1 Methodology 22712.5.2 Result Analysis 22912.6 Conclusion 230References 23013 ML Techniques for Attack and Anomaly Detection in Internet of Things Networks 235Vinod Mahor, Sadhna Bijrothiya, Rina Mishra and Romil Rawat13.1 Introduction 23613.2 Internet of Things 23613.3 Cyber-Attack in IoT 23913.4 IoT Attack Detection in ML Technics 24413.5 Conclusion 249References 24914 Applying Nature-Inspired Algorithms for Threat Modeling in Autonomous Vehicles 253Manas Kumar Yogi, Siva Satya Prasad Pennada, Sreeja Devisetti and Sri Siva Lakshmana Reddy Dwarampudi14.1 Introduction 25414.2 Related Work 26314.3 Proposed Mechanism 26514.4 Performance Results 26814.5 Future Directions 27014.6 Conclusion 273References 27315 The Smart City Based on AI and Infrastructure: A New Mobility Concepts and Realities 277Vinod Mahor, Sadhna Bijrothiya, Rina Mishra, Romil Rawat and Alpesh Soni15.1 Introduction 27815.2 Research Method 28015.3 Vehicles that are Both Networked and Autonomous 28215.4 Personal Aerial Automobile Vehicles and Unmanned Aerial Automobile Vehicles 28715.5 Mobile Connectivity as a Service 28815.6 Major Role for Smart City Development with IoT and Industry 4.0 28915.7 Conclusion 291References 292Index 297