Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Communication, Computation and Perception Technologies for Internet of Vehicles / / Yongdong Zhu [and three others], editors
| Communication, Computation and Perception Technologies for Internet of Vehicles / / Yongdong Zhu [and three others], editors |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hangzhou, China : , : Springer Nature Singapore Pte Ltd., , [2023] |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 004.678 |
| Soggetto topico |
Internet of things
Vehicular ad hoc networks (Computer networks) |
| ISBN | 981-9954-39-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Acknowledgement -- Contents -- 1 Modeling Microscopic Traffic Behaviors for Connected and Autonomous Vehicles -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Car-Following Models -- 2.2 Modeling Traffic Instabilities -- 3 The Proposed Model -- 3.1 The Congestion Term -- 3.2 The Free-Flow Term -- 4 Car-Following Behaviors in Mixed Traffic Flow -- 4.1 Car-Following Behaviors of AVs -- 4.2 Car-Following Behaviors of HVs -- 5 Vehicle Platoon Simulation for Mixed Traffic Flow -- 6 Conclusion -- References -- 2 ITS Traffic Management with Connected Vehicles: An Overview -- 1 Introduction -- 2 Traffic Sensing with Connected Vehicles -- 2.1 The Evolution of ITS Data Collection Techniques -- 2.2 Traffic State Estimation -- 3 Traffic Management with Connected Vehicles -- 3.1 Adaptive Traffic Control and Guidance -- 3.2 Bottleneck Capacity Optimization -- 4 Open Issues and Future Research Directions -- 4.1 The Impact of CV Penetration Rate -- 4.2 Robustness of ITS Solutions Under Connected Environment -- 5 Conclusions -- References -- 3 Evolution of Wireless Communication Technology for V2X Assisted Autonomous Driving -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 A Brief History of Wireless Communication Technology for V2X -- 3 Use Case and Requirements of V2X Communications -- 3.1 3GPP -- 3.2 5GAA -- 4 Wireless Communication Technology for V2X -- 4.1 Bluetooth -- 4.2 ZigBee -- 4.3 Wi-Fi -- 4.4 DSRC -- 4.5 C-V2X -- 5 Standardization Activities for V2X Communications -- 6 Conclusion -- References -- 4 5G Meets V2X: Integration, Application, Standard and Industrialization -- 1 Introduction -- 2 5G and Its Integration with V2X -- 2.1 5G Introduction -- 2.2 5G Three Scenarios and Standardization.
2.3 5G URLLC Scenario, Characteristics, Challenges -- 2.4 Principle of 5G uRLLC Integration with V2X -- 2.5 5G Applications for V2X -- 2.6 V2X Security Applications -- 2.7 Transportation Efficiency Applications -- 2.8 V2X Entertainment Applications -- 2.9 Future V2X Applications -- 3 Standardization and Industrialization of C-V2X -- 3.1 C-V2X Technology and Standardization -- 3.2 Global Spectrum Allocation for C-V2X -- 3.3 C-V2X Technology Application Trends and Industrialization -- 4 Conclusion -- References -- 5 Enabling Reconfigurable Intelligent Surface for V2X Communication Systems -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 V2X Technology and Standard Evolution -- 2.2 Fundamentals and Introduction of RIS -- 3 Modeling and Application of RIS-Assisted Communication System -- 3.1 Received Signal Modeling of RIS-Assisted Communication System -- 3.2 Potential Application Scenarios of RIS-Assisted Communication System -- 4 RIS-Assisted V2X Communication System -- 4.1 Challenge of RIS-Assisted V2X Communication -- 4.2 RIS-Assisted Vehicular Communication Network -- 4.3 RIS-Assisted Vehicular Edge Computing -- 5 Future Directions -- 6 Conclusion -- References -- 6 Real-Time Object Detection for ITS Applications -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Object Detection Algorithm -- 2.1 Overview of Object Detection Algorithm -- 2.2 YOLOv5 Algorithm -- 3 Lightweight Method -- 3.1 Lightweight Backbone -- 3.2 Model Compression -- 4 Knowledge Distillation Strategy -- 4.1 Knowledge Distillation -- 4.2 Knowledge Distillation Based on YOLOv5 -- 5 Experiments and Discussions -- 5.1 Datasets -- 5.2 Experiment Environment -- 5.3 Evaluation Metrics -- 5.4 Model Training -- 5.5 Experimental Results -- 6 Conclusion -- References. 7 3D Scene Perception for Autonomous Driving -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Existing LiDAR Sensor Based Scene Perception Methods -- 2.2 Existing Vision Based Scene Perception Methods -- 2.3 Existing LiDAR Sensor and Vision Fusion Methods -- 3 Vision Based 3D Scene Perception -- 3.1 Overview -- 3.2 Depth Estimation with a Single Image -- 3.3 Depth Estimation with a Stereo Pair -- 3.4 Depth Estimation with a Monocular Video -- 4 Datasets for Evaluation -- 4.1 KITTI -- 4.2 NuScenes -- 4.3 Quality Measure -- 5 Future Directions -- 5.1 Temporal Information Exploration -- 5.2 Fusion with Cheap Radar -- 6 Conclusion -- References -- 8 Multi-sensor Fusion for Perception in Complex Traffic Environments -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Cooperative Perceptions -- 2.2 Tasks -- 2.3 Data Representations for LiDAR and Image -- 3 Fusion Methodology -- 3.1 Decision-Level Fusion -- 3.2 Decision-Feature-Level Fusion -- 3.3 Feature-Level Fusion -- 4 Future Directions -- 4.1 Information Loss Due to Heterogeneous Dimensions -- 4.2 Misalignment Due to Noisy Projection Matrix -- 4.3 Conflicts Due to Data Resolution -- 5 Conclusion -- References -- 9 A Cooperative Positioning Enhancement for Blockchain-Enabled Vehicular Networks -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Related Works -- 2.1 Machine Learning Applied in Vehicle Positioning -- 2.2 Blockchain Technology for Internet of Vehicles -- 3 System Model -- 3.1 Positioning Scenarios -- 3.2 Feasibility Analysis of Sharing Positioning Error -- 4 Positioning Error Prediction Algorithm -- 4.1 Design of the DNN Algorithm -- 4.2 EPSO Optimized DNN Algorithm -- 4.3 Positioning Accuracy Enhancement Method. 5 Blockchain and Smart Contracts -- 5.1 Vehicular Blockchain -- 5.2 Secure Data Sharing Scheme for Enhanced Positioning Accuracy -- 6 Simulation and Conclusion -- 6.1 Positioning Error Prediction and Correction -- 6.2 Comparison Between Different Methods -- 6.3 Conclusion -- References -- 10 Cooperative Cloud-Edge Computing for Integrated Sensing and Communication in Internet of Vehicles -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Intelligent Computing -- 2.2 ISAC for Cooperative Cloud-Edge Computing -- 3 Integrated Sensing and Communication for Cooperative Cloud-Edge Computing in Internet of Vehicles -- 3.1 Collaborative Cloud-Edge-End Computing for Deep Learning Training and Inference -- 3.2 Joint Communication and Computation Resource Allocation in Cooperative Cloud-Edge Computing Internet of Vehicles -- 4 Future Directions -- 4.1 Robustness, Stability and Security -- 4.2 Multi-agent Reinforcement Learning -- 5 Conclusion -- References -- 11 Big Data for Internet of Vehicles and Smart Transportation -- 1 Introduction -- 2 Background of Big Data -- 2.1 Big Data Origin/Features and Data Mining -- 2.2 Software Infrastructure of Big Data -- 2.3 Hardware Infrastructure of Big Data -- 3 Vehicle and Transportation Big Data Platform -- 3.1 Overview of Vehicle and Transportation Big Data Platform -- 3.2 Vehicle and Transportation Data Source Layer -- 3.3 Vehicle and Transportation Data Aggregation Layer -- 3.4 Vehicle and Transportation Data Storage Layer -- 3.5 Vehicle and Transportation Data Processing Layer -- 3.6 Vehicle and Transportation Data Analysis and Management Layer -- 3.7 Vehicle and Transportation Data Application Layer -- 4 Application of Big Data in Vehicle and Transportation Industry -- 4.1 Application of Big Data in Internet of Vehicles. 4.2 Digital Twin Applications for Internet of Vehicles and Intelligent Transportation -- 5 Conclusion -- References -- 12 Noval Enabling Technology for V2X Network: Blockchain -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 1.3 Structural Organization -- 2 Background -- 2.1 Blockchain Technology -- 2.2 V2X -- 3 Blockchain for V2X Scenarios -- 3.1 Trust Management -- 3.2 Data Sharing -- 3.3 Resource Sharing -- 3.4 Trading Application -- 3.5 Collaboration Application -- 3.6 Transportation Management -- 3.7 Evidence Service -- 4 Future Directions -- 4.1 Blockchain Performance -- 4.2 Quantum Attacks -- 4.3 Heterogeneous Service -- 4.4 Incentive Mechanism -- 4.5 Integration with Emerging Technologies -- 5 Conclusion -- References -- 13 An Introduction to Trust Management in Internet of Vehicles -- 1 Introduction -- 2 Security Problems in IoVs -- 2.1 Characteristics of IoVs -- 2.2 Security Requirements of IoVs -- 2.3 Common Malicious Attacks in IoVs -- 3 Taxonomy of Trust Management in IoVs -- 3.1 Entity-Centric Trust Model -- 3.2 Data-Centric Trust Model -- 3.3 Hybrid Trust Model -- 4 Simulation Tools for IoVs -- 4.1 Veins -- 4.2 Eclipse MOSAIC -- 4.3 The ONE Simulator -- 5 Future Research Opportunities -- 5.1 Social Relationships in IoVs -- 5.2 Development of Standard Evaluation Procedures and Datasets -- 5.3 Practical Issues of IoV Trust Models -- 6 Conclusion -- References -- 14 Misbehaviour Detection Mechanisms in Internet of Vehicles -- 1 Introduction -- 2 Threats Against Internet of Vehicles -- 2.1 Threats to the In-Vehicle Environment -- 2.2 Threats to the Wireless Communication Environment -- 3 Detection Mechanisms Classification -- 3.1 Detection Mechanisms Based on Message Content -- 3.2 Detection Mechanisms Based on Message Processing Behaviour -- 3.3 Detection Mechanisms Combined with Sensor -- 4 Further Discussions. 5 Conclusion. |
| Record Nr. | UNINA-9910755083903321 |
| Hangzhou, China : , : Springer Nature Singapore Pte Ltd., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Flying ad hoc networks : cooperative networking and resource allocation / / Jingjing Wang, Chunxiao Jiang
| Flying ad hoc networks : cooperative networking and resource allocation / / Jingjing Wang, Chunxiao Jiang |
| Autore | Wang Jingjing <active 2014-> |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (297 pages) |
| Disciplina | 629.1326 |
| Collana | Wireless networks (Springer (Firm)) |
| Soggetto topico |
Drone aircraft - Control systems
Vehicular ad hoc networks (Computer networks) |
| ISBN |
981-16-8849-4
981-16-8850-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Acronyms -- 1 Introduction of Flying Ad Hoc Networks -- 1.1 Basic Classification and Regulation of UAVs -- 1.2 Differences Between FANET, VANET, MANET, and AANET -- 1.3 Compelling Applications of FANET -- References -- 2 Communication Channels in FANET -- 2.1 UAV Communication Channel Characteristics -- 2.1.1 UAV Link Budget -- 2.1.2 UAV Channel Fading -- 2.1.3 Channel Impulse Response and Metrics -- 2.2 UAV Communication Channel Modeling -- 2.2.1 Air-to-Ground Channels -- 2.2.1.1 A2G Channels in Urban Areas -- 2.2.1.2 Low-Altitude Channels in Cellular Networks -- 2.2.1.3 A2G Channels in Rural and Over-Water Areas -- 2.2.1.4 Evaporation Duct for Over Sea -- 2.2.1.5 Aircraft Shadowing in A2G Channels -- 2.2.2 Air-to-Air Channels -- 2.2.3 UAV-MIMO Channels -- 2.2.3.1 UAV-MIMO Channel Modeling -- 2.2.3.2 Antenna Diversity -- 2.2.3.3 Spatial Multiplexing -- 2.3 Challenges and Open Issues -- 2.3.1 Antennas for UAV Channel Measurement -- 2.3.2 Channels of UAV Applications in IoT and 5G -- 2.3.3 Channels in Vertical Industrial Applications -- 2.3.4 Channels of UAV FSO Communications -- References -- 3 Seamless Coverage Strategies of FANET -- 3.1 Introduction of Seamless Coverage Problems -- 3.1.1 Problem Domain and Challenges -- 3.1.2 State of the Art -- 3.2 UAV Seamless Coverage Strategy for Dense Urban Areas -- 3.2.1 System Model -- 3.2.2 Cyclic Recharging and Reshuffling Optimization -- 3.2.2.1 UAV Power Model -- 3.2.2.2 CRRS Constraint -- 3.2.3 Problem Formulation -- 3.2.4 Distributed Particle Swarm Optimization Aided Solution -- 3.2.4.1 Analysis and Simplification -- 3.2.4.2 Distributed-PSO Algorithm Design -- 3.2.4.3 Algorithmic Convergence Analysis -- 3.2.4.4 Algorithmic Complexity Analysis -- 3.2.5 Simulation Results -- 3.2.6 Conclusions -- 3.3 UAV Seamless Coverage Strategy for QoS-Guaranteed IoT.
3.3.1 System Model -- 3.3.2 Problem Formulation -- 3.3.3 Block Coordinate Descent Based Joint Optimization -- 3.3.3.1 Node Assignment Scheduling -- 3.3.3.2 UAV Trajectory Planning -- 3.3.3.3 UAV Transmit Power Control -- 3.3.3.4 Algorithmic Architecture and Convergence Analysis -- 3.3.4 Simulation Results -- 3.3.4.1 Resulting Strategies -- 3.3.4.2 Energy Efficiency -- 3.3.4.3 Optimality Analysis -- 3.3.5 Conclusions -- 3.4 UAV Seamless Coverage Strategy for Minimum-Delay Placement -- 3.4.1 System Model -- 3.4.1.1 Physical Layer Model of the UAV-Enabled Network -- 3.4.1.2 Queuing Model and System Dynamics -- 3.4.1.3 ABS Placement Scheduling -- 3.4.2 Problem Formulation -- 3.4.3 Markov Decision Process Transformation -- 3.4.3.1 Constrained Markov Decision Process -- 3.4.3.2 The Lagrangian Approach -- 3.4.4 Backward Induction and R-Learning Based Optimization -- 3.4.4.1 Solution to the Problem in Case 1 -- 3.4.4.2 Solution to the Problem in Case 2 -- 3.4.4.3 Solution to the Problem in Case 3 -- 3.4.4.4 Analysis of Computational Complexity -- 3.4.5 Simulation Results -- 3.4.5.1 Impact of the ABS' Total Energy -- 3.4.5.2 Impact of the Asymmetry Wireless Tele-Traffic -- 3.4.5.3 Impact of the Wireless Tele-Traffic Rate -- 3.4.5.4 Impact of the Ground Devices' Location -- 3.4.6 Conclusions -- 3.4.7 The Proof of Theorem 1 -- References -- 4 Cooperative Resource Allocation in FANET -- 4.1 Introduction of Cooperative Resource Allocation Problems -- 4.1.1 Problem Domain and Challenges -- 4.1.2 State of the Art -- 4.2 UAV Position Control with Interference -- 4.2.1 System Model -- 4.2.2 Problem Formulation -- 4.2.2.1 Constraints -- 4.2.2.2 Uplink Resource Allocation Formulation -- 4.2.3 Hovering Altitude and Power Control Solution -- 4.2.3.1 Stage 1: Joint Subchannel and Power Control -- 4.2.3.2 Lagrangian Dual Decomposition Method. 4.2.3.3 Stage 2: Hovering Altitude Optimization -- 4.2.3.4 Joint Hovering Altitude and Power Control -- 4.2.3.5 Algorithm Implementation -- 4.2.3.6 Supplementary Analysis -- 4.2.4 Simulation Results -- 4.2.5 Conclusions -- 4.3 UAV Trajectory Design for Space-Air-Ground Networks -- 4.3.1 System Model -- 4.3.2 Problem Formulation -- 4.3.3 The Solution for Optimization Problem -- 4.3.3.1 Smart Devices Connection Scheduling Optimization -- 4.3.3.2 Power Control Optimization -- 4.3.3.3 The UAV Trajectory Optimization -- 4.3.3.4 Optimization of Joint Smart Device Connection Scheduling, Power Control, and UAV Trajectory Design -- 4.3.3.5 Computational Complexity Analysis -- 4.3.4 Simulation Results -- 4.3.5 Conclusions -- 4.4 Multi-UAV-Aided IoT NOMA Uplink Transmission -- 4.4.1 System Model -- 4.4.1.1 Channel Model -- 4.4.1.2 Interference Model -- 4.4.2 Problem Formulation -- 4.4.3 IoT Nodes Clustering and Subchannel Assignment -- 4.4.4 Power Allocation and Flight Height Design -- 4.4.4.1 Power Allocation Design of IoT Nodes -- 4.4.4.2 Flight Heights Design of UAVs -- 4.4.4.3 Joint Power Allocation and Flight Height Optimization -- 4.4.5 Simulation Results -- 4.4.6 Conclusions -- References -- 5 Mobile Edge Computing in FANET -- 5.1 Introduction of Mobile Edge Computing Problems -- 5.1.1 Problem Domain and Challenges -- 5.1.2 State of the Art -- 5.2 Load-Balance Oriented UAV-Aided Edge Computing -- 5.2.1 System Model -- 5.2.1.1 Network Model -- 5.2.1.2 Communication Model -- 5.2.1.3 Computation Model -- 5.2.2 Problem Formulation -- 5.2.3 Joint UAV Deployment and Task Scheduling -- 5.2.3.1 Load Balance for UAVs -- 5.2.3.2 GAP Based Node Assignment -- 5.2.3.3 Deep Reinforcement Learning Aided Task Scheduling -- 5.2.3.4 Differential Evolution Based Multi-UAV Deployment -- 5.2.4 Simulation Results -- 5.2.5 Conclusions. 5.3 Latency and Reliability Guaranteed UAV-Aided Edge Computing -- 5.3.1 System Model -- 5.3.1.1 Joint Communications and Computing Optimization -- 5.3.2 Problem Formulation -- 5.3.3 Hybrid Binary Particle Swarm Optimization -- 5.3.4 Simulation Results -- 5.3.5 Conclusions -- 5.4 Energy-Efficient and Secure UAV-Aided Edge Computing -- 5.4.1 System Model -- 5.4.1.1 Local-Computing Model -- 5.4.1.2 Jamming Model -- 5.4.1.3 Secure Offloading Model -- 5.4.1.4 Edge Computing Model -- 5.4.2 Problem Formulation -- 5.4.2.1 Problem 1: Active Eavesdropper -- 5.4.2.2 Problem 2: Passive Eavesdropper -- 5.4.3 Energy-Efficient Secure UMEC Solution -- 5.4.3.1 Case 1: Active Eavesdropper -- 5.4.3.2 Case 2-1: Passive Eavesdropper at a Fixed Location -- 5.4.3.3 Case 2-2: Passive Eavesdropper at a Random Location -- 5.4.3.4 Optimal Offloading Strategy for the Secure UMEC -- 5.4.4 Analysis of Offloading and Computation -- 5.4.4.1 Zero Offloading -- 5.4.4.2 Full Offloading -- 5.4.4.3 Partial Offloading -- 5.4.4.4 Computational Overload -- 5.4.5 Simulation Results -- 5.4.5.1 Selection of Offloading Options -- 5.4.5.2 Impact of SOP Requirements -- 5.4.5.3 Impact of the UAV's Altitude and of the Eavesdropper's Location -- 5.4.6 Conclusions -- 5.5 Transmit-Energy and Computation-Delay Optimization -- 5.5.1 System Model -- 5.5.1.1 The UAV Model -- 5.5.1.2 The Channel Model -- 5.5.1.3 Cloud Computation Model -- 5.5.1.4 Edge Cloud -- 5.5.1.5 Remote Cloud -- 5.5.2 Energy-Efficient Gateway Selection -- 5.5.2.1 The Communication Model Analysis -- 5.5.2.2 Required Transmission Time and Energy Consumption -- 5.5.2.3 An Energy-Efficient Gateway Selection Scheme -- 5.5.3 Task Scheduling and Resource Allocation Scheme -- 5.5.3.1 Average Power Consumption and Cloud Execution Delay -- 5.5.3.2 Task Scheduling and Resource Allocation Scheme Based on Lyapunov Optimization. 5.5.3.3 A Low-Complexity Iterative Algorithm -- 5.5.4 Simulation Results -- 5.5.4.1 Performance of Gateway Selection Scheme -- 5.5.4.2 Performance of Task Scheduling and Resource Allocation scheme -- 5.5.5 Conclusions -- References. |
| Record Nr. | UNINA-9910743256003321 |
Wang Jingjing <active 2014->
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ICV 2016 : IET International Conference on Intelligent and Connected Vehicles : 22-23 September 2016, Chongqing, China / / Institution of Engineering and Technology
| ICV 2016 : IET International Conference on Intelligent and Connected Vehicles : 22-23 September 2016, Chongqing, China / / Institution of Engineering and Technology |
| Pubbl/distr/stampa | London : , : Institution of Engineering and Technology, , 2017 |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina | 388.3 |
| Soggetto topico |
Vehicular ad hoc networks (Computer networks)
Vehicles - Automatic control Intelligent control systems |
| ISBN | 1-78561-231-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910332497903321 |
| London : , : Institution of Engineering and Technology, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ICV 2016 : IET International Conference on Intelligent and Connected Vehicles : 22-23 September 2016, Chongqing, China / / Institution of Engineering and Technology
| ICV 2016 : IET International Conference on Intelligent and Connected Vehicles : 22-23 September 2016, Chongqing, China / / Institution of Engineering and Technology |
| Pubbl/distr/stampa | London : , : Institution of Engineering and Technology, , 2017 |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina | 388.3 |
| Soggetto topico |
Vehicular ad hoc networks (Computer networks)
Vehicles - Automatic control Intelligent control systems |
| ISBN | 1-78561-231-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996577845203316 |
| London : , : Institution of Engineering and Technology, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
IEEE Standard for Wireless Access in Vehicular Environments (WAVE) : Certificate Management Interfaces for End Entities / / IEEE
| IEEE Standard for Wireless Access in Vehicular Environments (WAVE) : Certificate Management Interfaces for End Entities / / IEEE |
| Pubbl/distr/stampa | New York, N.Y. : , : IEEE, , 2022 |
| Descrizione fisica | 1 online resource (261 pages) : illustrations |
| Disciplina | 388.3 |
| Collana | IEEE Std |
| Soggetto topico | Vehicular ad hoc networks (Computer networks) |
| ISBN | 1-5044-8578-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 1609.2.1-2022 - IEEE Standard for Wireless Access in Vehicular Environments |
| Record Nr. | UNISA-996577922303316 |
| New York, N.Y. : , : IEEE, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
IEEE Vehicular Networking Conference : [proceedings]
| IEEE Vehicular Networking Conference : [proceedings] |
| Pubbl/distr/stampa | Piscataway, NJ, : Institute of Electrical and Electronic Engineers |
| Disciplina | 388.3 |
| Soggetto topico |
Vehicular ad hoc networks (Computer networks)
Mobile communication systems |
| Soggetto genere / forma |
Periodicals.
Conference papers and proceedings. |
| ISSN | 2157-9865 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
VNC
Vehicular Networking Conference |
| Record Nr. | UNISA-996279336703316 |
| Piscataway, NJ, : Institute of Electrical and Electronic Engineers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
IEEE Vehicular Networking Conference : [proceedings]
| IEEE Vehicular Networking Conference : [proceedings] |
| Pubbl/distr/stampa | Piscataway, NJ, : Institute of Electrical and Electronic Engineers |
| Disciplina | 388.3 |
| Soggetto topico |
Vehicular ad hoc networks (Computer networks)
Mobile communication systems |
| Soggetto genere / forma |
Periodicals.
Conference papers and proceedings. |
| ISSN | 2157-9865 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
VNC
Vehicular Networking Conference |
| Record Nr. | UNINA-9910626152303321 |
| Piscataway, NJ, : Institute of Electrical and Electronic Engineers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent computing and communication for the internet of vehicles / / Mushu Li [and three others]
| Intelligent computing and communication for the internet of vehicles / / Mushu Li [and three others] |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (90 pages) |
| Disciplina | 004.678 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Internet of things
Vehicular ad hoc networks (Computer networks) |
| ISBN | 3-031-22860-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Internet of Vehicles (IoV) -- 1.2 IoV Use Cases -- 1.3 Network Characteristics in IoV -- 1.4 Outline of the Monograph -- References -- 2 Overview of Communication and Computing in IoV -- 2.1 Communication and Computing in IoV -- 2.2 V2X Communication Protocols -- 2.3 Vehicular Computing Resource Scheduling -- References -- 3 Protocol Design for Safety Message Broadcast -- 3.1 Communication in Safety Message Broadcast -- 3.2 Network Scenario -- 3.2.1 Basic Settings -- 3.2.2 Features of Safety Message Broadcast -- 3.2.3 Assumptions -- 3.3 CIDC: A Distributed and Adaptive MAC Design -- 3.3.1 Messaging Mechanism Design -- 3.3.2 Contention Intensity Estimation Method -- 3.3.3 Channel Access Mechanism Design -- 3.4 CIDC: Network-Perspective Modeling -- 3.5 CIDC: Performance Analysis -- 3.5.1 Basic Features -- 3.5.2 Packet-to-Slot Ratio -- 3.5.3 Contention Intensity and Contention Delay -- 3.5.4 Collision Conditions and Probability -- 3.6 Performance Evaluation -- vii -- viii Contents -- 3.6.1 Performance with Accurate Contention Intensity -- Estimation -- 3.6.2 A Comparison of Analytical and Numerical Results -- 3.6.3 Performance under Contention Intensity Estimation -- Errors -- 3.7 Summary -- References -- 4 Computing Scheduling for Autonomous Driving -- 4.1 Computing in Autonomous Driving -- 4.2 System Model -- 4.2.1 Network Model -- 4.2.2 Computing Scheduling Scenarios -- 4.2.3 Age of Computing Results -- 4.3 Adaptive Computing Resource Scheduling -- 4.3.1 Restless Multi-armed Bandit Formulation -- 4.3.2 Indexability and Index Policy -- 4.4 Machine Learning-based Scheduling Policy -- 4.4.1 DRL-assisted Scheduling -- 4.4.2 Scheduling Scheme for Asynchronous Offloading -- 4.5 Performance Evaluation -- 4.5.1 Numerical Results -- 4.5.2 Simulation in a Real Dataset -- 4.6 Summary -- References -- 5 Conclusions -- 5.1 Conclusions on Intelligent Computing and Communication -- in IoV -- 5.2 Open Research Problems. |
| Record Nr. | UNINA-9910640384403321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent computing and communication for the internet of vehicles / / Mushu Li [and three others]
| Intelligent computing and communication for the internet of vehicles / / Mushu Li [and three others] |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (90 pages) |
| Disciplina | 004.678 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Internet of things
Vehicular ad hoc networks (Computer networks) |
| ISBN | 3-031-22860-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Internet of Vehicles (IoV) -- 1.2 IoV Use Cases -- 1.3 Network Characteristics in IoV -- 1.4 Outline of the Monograph -- References -- 2 Overview of Communication and Computing in IoV -- 2.1 Communication and Computing in IoV -- 2.2 V2X Communication Protocols -- 2.3 Vehicular Computing Resource Scheduling -- References -- 3 Protocol Design for Safety Message Broadcast -- 3.1 Communication in Safety Message Broadcast -- 3.2 Network Scenario -- 3.2.1 Basic Settings -- 3.2.2 Features of Safety Message Broadcast -- 3.2.3 Assumptions -- 3.3 CIDC: A Distributed and Adaptive MAC Design -- 3.3.1 Messaging Mechanism Design -- 3.3.2 Contention Intensity Estimation Method -- 3.3.3 Channel Access Mechanism Design -- 3.4 CIDC: Network-Perspective Modeling -- 3.5 CIDC: Performance Analysis -- 3.5.1 Basic Features -- 3.5.2 Packet-to-Slot Ratio -- 3.5.3 Contention Intensity and Contention Delay -- 3.5.4 Collision Conditions and Probability -- 3.6 Performance Evaluation -- vii -- viii Contents -- 3.6.1 Performance with Accurate Contention Intensity -- Estimation -- 3.6.2 A Comparison of Analytical and Numerical Results -- 3.6.3 Performance under Contention Intensity Estimation -- Errors -- 3.7 Summary -- References -- 4 Computing Scheduling for Autonomous Driving -- 4.1 Computing in Autonomous Driving -- 4.2 System Model -- 4.2.1 Network Model -- 4.2.2 Computing Scheduling Scenarios -- 4.2.3 Age of Computing Results -- 4.3 Adaptive Computing Resource Scheduling -- 4.3.1 Restless Multi-armed Bandit Formulation -- 4.3.2 Indexability and Index Policy -- 4.4 Machine Learning-based Scheduling Policy -- 4.4.1 DRL-assisted Scheduling -- 4.4.2 Scheduling Scheme for Asynchronous Offloading -- 4.5 Performance Evaluation -- 4.5.1 Numerical Results -- 4.5.2 Simulation in a Real Dataset -- 4.6 Summary -- References -- 5 Conclusions -- 5.1 Conclusions on Intelligent Computing and Communication -- in IoV -- 5.2 Open Research Problems. |
| Record Nr. | UNISA-996547968003316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Intelligent resource management in vehicular networks / / Haixia Peng, Qiang Ye, and Xuemin Shen
| Intelligent resource management in vehicular networks / / Haixia Peng, Qiang Ye, and Xuemin Shen |
| Autore | Peng Haixia |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (163 pages) |
| Disciplina | 388.312 |
| Collana | Wireless Networks |
| Soggetto topico |
Vehicular ad hoc networks (Computer networks) - Safety measures
Vehicular ad hoc networks (Computer networks) |
| ISBN | 3-030-96507-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Overview of Vehicular Networks -- 1.1.1 Vehicular Network Applications -- 1.1.2 Vehicular Network Characteristics -- 1.1.3 Vehicular Network Classifications -- 1.1.4 Overview of Vehicular Communication Technologies -- 1.2 Challenges in Vehicular Networks -- 1.2.1 Vehicular Information Sharing -- 1.2.2 Task Computing -- 1.3 Resource Management in Vehicular Networks -- 1.3.1 Spectrum Resource Management -- 1.3.2 Computing Resource Management -- 1.3.3 Intelligent Multi-Resource Management -- 1.3.3.1 Methodology -- 1.4 Aim of the Monograph -- 1.5 Summary -- References -- 2 MEC-Assisted Vehicular Networking -- 2.1 MEC-Assisted ADVNET Architecture -- 2.1.1 Problem Statement -- 2.1.2 Architecture Design -- 2.2 SDN-Enabled Resource Management -- 2.2.1 Computing and Storage Resource Management -- 2.2.2 Spectrum Management -- 2.2.3 Open Research Issues -- 2.3 Aerial-Assisted Vehicular Network: Case Study -- 2.3.1 A Drone-Assisted MVNET Architecture -- 2.3.2 Intelligent Resource Management -- 2.3.3 Case Study -- 2.4 Summary -- References -- 3 Spectrum Slicing in MEC-Assisted ADVNETs -- 3.1 System Model -- 3.1.1 Dynamic Slicing Framework -- 3.1.2 Communication Model -- 3.2 Resource Management Scheme -- 3.2.1 Network-Level Spectrum Reservation -- 3.2.2 Vehicle-Level Spectrum Reservation -- 3.2.3 Transmit Power Control -- 3.3 Problem Analysis and Suboptimal Solution -- 3.3.1 Problem Analysis -- 3.3.2 Algorithm Design -- 3.4 Simulation Results -- 3.5 Summary -- References -- 4 Intelligent Multi-Dimensional Resource Allocation in MVNETs -- 4.1 System Model -- 4.1.1 Spectrum, Computing, and Caching Allocation -- 4.2 Problem Formulation and Transformation -- 4.2.1 Problem Formulation -- 4.2.2 Problem Transformation with DRL -- 4.3 DDPG Algorithm Based Solution -- 4.3.1 DDPG-Based Algorithm.
4.3.2 HDDPG-Based Algorithm -- 4.4 Simulation Results and Analysis -- 4.5 Summary -- References -- 5 Aerial-Assisted Intelligent Resource Allocation -- 5.1 System Model and Problem Formulation -- 5.1.1 UAV-Assisted MVNET -- 5.1.2 Resource Reservation Models -- 5.1.3 Problem Formulation -- 5.2 Centralized/Distributed Multi-Dimensional ResourceManagement -- 5.2.1 Problem Transformation -- 5.2.2 SADDPG/MADDPG-Based Solutions -- 5.3 Simulation Results -- 5.3.1 Performance Evaluation for the SADDPG-Based Scheme -- 5.3.2 Performance Evaluation for the MADDPG-BasedScheme -- 5.4 Summary -- References -- 6 Conclusions and Future Research Directions -- 6.1 Conclusions -- 6.2 Future Research Directions -- References -- Index. |
| Record Nr. | UNISA-996464537403316 |
|
Peng Haixia
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
