Intelligent Technologies for Internet of Vehicles

This book gathers recent research works in emerging Artificial Intelligence (AI) methods for the convergence of communication, caching, control, and computing resources in cloud-based Internet of Vehicles (IoV) infrastructures. In this context, the book's major subjects cover the analysis and t...

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Bibliographic Details
Main Authors Magaia, Naercio, Mastorakis, George, Mavromoustakis, Constandinos, Pallis, Evangelos, Markakis, Evangelos K
Format eBook
LanguageEnglish
Published Cham Springer International Publishing AG 2021
Springer International Publishing
Edition1
SeriesInternet of Things
Subjects
Artificial Intelligence
Communications Engineering, Networks
Computer Science
Cyber-physical systems, IoT
Engineering
Innovation/Technology Management
Internet of things
Transportation Technology and Traffic Engineering
Online AccessGet full text
ISBN9783030764920
3030764923
ISSN2199-1073
2199-1081
DOI10.1007/978-3-030-76493-7

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Table of Contents:
  • 5 Blockchain Support for Applications -- 5.1 Accident Detection -- 5.2 Forensics Application -- 5.3 Charging Application for Electronic Vehicles -- 5.4 Carpooling and Platooning -- 6 Open Issues and Future Directions -- 7 Lessons Learned and Conclusion -- References -- Vehicle Guidance System Based on Secure Mobile Communication -- 1 Introduction -- 2 Problem and Contribution -- 2.1 Requirements for a Vehicle Guidance System -- 2.2 State of the Art -- 2.3 Methodological and Statistical Challenges of Vehicular Networks -- 2.4 Research Question -- 3 Introducing SIKAF: The Communications Architecture for Vehicle Guidance Systems -- 3.1 Organization -- 3.2 Implementation -- 3.3 Cryptography -- 4 Results -- 4.1 Hardware for Prototype -- 4.2 Formal Language for Messages -- 4.3 Message Size -- 4.4 Required Amount of Keys -- 4.5 Processing Time -- 5 Conclusion -- 5.1 Classification of the Architecture -- 5.2 Possibilities and Limitations of the Architecture -- 6 Discussion -- 6.1 Possible Extensions -- 6.2 New Concepts for Decentralized Autonomous Systems -- References -- Attack Models and Countermeasures for Autonomous Vehicles -- 1 Introduction -- 2 Attack Surfaces and Potential Threats -- 2.1 Electronic Control Units (ECUs) -- 2.2 Sensors -- 2.3 Intra-vehicular Links -- 2.4 Inter-vehicular Links -- 2.5 Attack Summary -- 3 Existing Countermeasures -- 3.1 Solutions for ECU Attacks -- 3.2 Solutions for Sensor Attacks -- 3.3 Solution for Intra-vehicular Link Attacks -- 3.4 Solutions for Inter-vehicular Link Attacks -- 4 Future Directions -- 4.1 Open Issues -- 4.2 Future Directions -- 5 Conclusion -- References -- Routing Protocols -- VASNET Routing Protocol in Crisis Scenario Based on Carrier Vehicle -- 1 VANET Introduction -- 1.1 Topology Based Routing Protocols -- 1.2 Position Based Routing Protocols or Geographic Routing -- 1.3 Broadcast Routing
  • 2.2 Standard Traditional Vehicular Router Design and Networking -- 2.3 Software Defined Networking (SDN) -- 3 Benefits of SDN -- 4 Towards SDN Architecture for IoV -- 4.1 Present Architecture for SDN -- 4.2 SDN Architecture Mapping to IoV -- 4.3 Modified SDN Architecture Mapping to IoV -- 5 The State of the Art for SDN Enabled IoV -- 6 Limitations and Possible Solutions -- 7 Automatic Configuration Framework for SDN Enabled IoV -- 7.1 Assumptions -- 7.2 Requirements -- 7.3 Deployed Solutions -- 7.4 Proposed Automatic Configuration Method -- 8 AI Assisted SDN Enabled IoV -- 8.1 Data Collection -- 8.2 AI Management in IoV -- 8.3 AI Assisted Framework for SDN Enabled IoV -- 9 Experimental Scenario and Results -- 9.1 Emulation Scenarios -- 9.2 Emulation Results -- 10 Open Challenges for SDN in IoV -- 10.1 Transition from Non-SDN IoV to SDN IoV -- 10.2 Performance -- 10.3 Mobility -- 10.4 Security -- 11 Conclusions -- References -- IoV with ML/DL Technologies -- Machine Learning Technologies in Internet of Vehicles -- 1 Introduction -- 2 Background and Motivation -- 3 AI in IoV Network -- 3.1 AI for IoV Multimedia Communication -- 3.2 Intelligent IoV Edge Based Algorithm -- 3.3 AI for Vehicle to Everything -- 4 AI enabled QoE/QoS Optimization -- 4.1 Buffer Aware QoE/QoS Optimization -- 4.2 Energy Aware QoE/QoS Optimization -- 5 ML Algorithms in IoV Network -- 5.1 ML Based Edge Caching Mechanisms for IoV -- 5.2 Deep Reinforcement Learning Based Offloading Algorithm -- 5.3 ML for Dynamic and High Mobility IoV -- 5.4 ML Based Decision Making in IoV -- 6 Machine Learning Applications in IoV -- 6.1 Intelligent Autonomous Driving -- 6.2 Deep Learning for Driver Safety and Assistance -- 6.3 ML in Smart Transportation -- 7 Future Directions and Tentative Solutions -- 8 Conclusion -- References -- Deep Learning Approaches for IoV Applications and Services
  • 1 Introduction -- 2 The Evolution of Deep Learning in IoT -- 3 Distributed Machine Learning Systems -- 3.1 Distributed Machine Learning Architecture -- 3.2 Parallelization Algorithms -- 3.3 Fast Optimization Algorithms -- 4 Internet of Vehicles DL Applications and Services -- 5 Convolutional Artificial Neural Networks (CANN) -- 5.1 The IoV Multilayer Perceptron's (MLPs) -- 5.2 CANN IoV Architectures -- 5.3 CANN IoV Algorithms -- 6 Recurrent Neural Networks (RNNs) -- 7 Deep Reinforcement Learning for IoV Network -- 7.1 Energy Efficiency and Operation of Scheduling -- 7.2 Resources Allocation and Management -- 7.3 Optimization Performance -- 8 Deep Learning Driven IoV Networks -- 8.1 DL Driven Network-Level Internet of Vehicle Data Analysis -- 8.2 DL Driven App-Level Internet of Vehicle Data Analysis -- 8.3 DL IoV Systems Clustering Procedures and Protocols -- 8.4 IoV Network Control Based DL -- 8.5 Predictive Analytics "Regression" Issues in DL -- 9 ML Future Directions and Challenges in IoV Applications -- 10 Conclusion -- References -- Intelligently Reduce Transportation's Energy Consumption -- 1 Introduction -- 1.1 Internet of Vehicles (IoV) -- 1.2 Literature Review -- 2 Fluid Dynamics -- 2.1 Physical Approach -- 2.2 Fluid Flow in the Grid -- 3 Methodology -- 3.1 Implementation -- 3.2 Visual Presentation -- 3.3 Mathematical Approach -- 4 Summary -- 5 Conclusion and Future Work -- References -- Security and Privacy -- Blockchain-Based Internet-of-Vehicle -- 1 Introduction -- 2 Background -- 2.1 Overview on Blockchains -- 2.2 Blockchain-Based IoV -- 2.3 Blockchain-Based Supporting AI in IoV -- 3 Blockchain Support for Data Sharing -- 3.1 Incentive Announcement Protocol -- 3.2 Smart Contracts in Incentives and Data Sharing -- 4 Blockchain Support for IoV Trust Management -- 4.1 Lightweight Authentication Mechanism -- 4.2 Traffic Events Verifications
  • Intro -- Introduction -- Contents -- Emerging Trends of AI and IoV -- The Fundamentals and Potential of the Internet of Vehicles (IoV) in Today's Society -- 1 Introduction -- 2 Methodology -- 3 Internet of Vehicles (IoV) Concept -- 3.1 Types of Vehicle Networks -- 3.2 IoV Applications -- 3.3 Information Technology Integrated with Automation Technology for IoV -- 4 IoV Communication Networks -- 4.1 V2V (Vehicles with Vehicles) -- 4.2 V2R (Vehicles with Infrastructure) -- 4.3 V2X (Vehicles with Internet (Cloud)) -- 4.4 VANETs (Vehicular Ad Hoc Networks) -- 5 IoV Architecture -- 5.1 Advantages and Benefits of IoV -- 5.2 Challenges for Implantation of IoV Structure -- 6 Discussion -- 7 Conclusions -- 8 Trends -- References -- AI-Enabled IoV Applications and Systems -- Intelligent Approaches for Fault-Tolerance in Radio Communication of Autonomous Vehicles -- 1 Introduction -- 2 Problem Overview -- 3 Radio Technology Usage in Autonomous Vehicles -- 3.1 Radio Technologies in AV -- 3.2 The General Fault-Tolerance Process -- 3.3 Existing Fault-Tolerant Radio Technologies in AV -- 4 Artificial Intelligence as a Tool for Fault-Tolerance -- 4.1 Artificial Reasoning -- 4.2 Artificial Learning -- 5 Case Study -- 5.1 Propagated-Fault Detection -- 5.2 Fault Diagnosis and Recovery -- 6 Thoughts About AI Application to Wireless Propagated Fault Management -- 7 Future Challenges and AI Opportunities Toward Reliable Autonomous Vehicles -- 8 Conclusion -- References -- AI-Based Traffic Queue Detection for IoV Safety Services in 5G Networks -- 1 Motivation -- 2 From the Biological Neurons to the Artificial Neural Networks -- 2.1 Convolutional Neural Network: A Model of the Visual Cortex -- 3 Object Detection -- 3.1 Two-Stage Object Detection -- 3.2 One-Stage Object Detection -- 3.3 The Final Choice -- 4 Realization of the Traffic Queue Detector -- 4.1 YOLOv3
  • 4.2 SORT -- 4.3 Direction of Movement Detector -- 4.4 Traffic Queue Detector -- 4.5 Comparison Between the Two Solutions -- 4.6 Considerations on the Choice of Parameters -- 5 Conclusion and Future Perspective -- References -- Internet of Vehicles - System of Systems Distributed Intelligence for Mobility Applications -- 1 Introduction -- 2 Concept and Related Work -- 3 ECAS Vehicle Capabilities, Technologies, and Architectures -- 3.1 Vehicles Architecture Evolution -- 4 IoV 3D Multi-layered Architecture -- 4.1 IoV Functional Layers -- 4.2 IoV Trustworthiness Properties -- 4.3 IoV System Characteristics -- 5 Design Issues in Implementing the IoV Architecture -- 5.1 AI-Based Design and Technologies -- 5.2 Fail-Operational Design -- 5.3 End-to-End Security -- 5.4 Intelligent Connectivity for IoV -- 6 Future Research Challenges -- 7 Conclusions -- References -- Software-Defined Networking/Network Function Virtualization -- Cross Network Slicing in Vehicular Networks -- 1 Introduction to Network Slicing and SDN -- 1.1 What Is Network Slicing? -- 1.2 Network Slicing Enabling Technologies -- 1.3 Network Slicing as a Solution -- 1.4 Network Slicing from Business Perspective -- 1.5 Network Slicing Architecture -- 1.6 Software Defined Networking (SDN) -- 2 Network Slicing in Vehicular Networks -- 2.1 In-Vehicle Network Slicing -- 2.2 VANETs Slicing -- 3 Applications of Vehicular Network Slicing -- 3.1 Vehicle-to-Everything (V2X) Slices -- 3.2 Real-World Scenario with V2X Network Slicing -- 4 Intelligent Network Slicing -- 4.1 Machine Learning in Network Slicing -- 4.2 Smart Vehicular Network Slicing -- 5 Challenges and Open Issues -- 6 Conclusion -- References -- Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles -- 1 Introduction -- 2 Background -- 2.1 Internet of Vehicles
  • 1.4 Geocast Routing