Dai et al., 2022 - Google Patents
Adaptive digital twin for vehicular edge computing and networksDai et al., 2022
- Document ID
- 9178659601454132143
- Author
- Dai Y
- Zhang Y
- Publication year
- Publication venue
- Journal of Communications and Information Networks
External Links
Snippet
To better support the emerging vehicular applications and multimedia services, vehicular edge computing (VEC) provides computing and caching services in proximity to vehicles, by reducing network transmission latency and alleviating network congestion. However, current …
- 230000003044 adaptive 0 title abstract description 38
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
- H04L41/145—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning involving simulating, designing, planning or modelling of a network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/12—Network-specific arrangements or communication protocols supporting networked applications adapted for proprietary or special purpose networking environments, e.g. medical networks, sensor networks, networks in a car or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing packet switching networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Dai et al. | Adaptive digital twin for vehicular edge computing and networks | |
Khan et al. | Digital-twin-enabled 6G: Vision, architectural trends, and future directions | |
Fan et al. | Digital twin empowered mobile edge computing for intelligent vehicular lane-changing | |
Tang et al. | Survey on digital twin edge networks (DITEN) toward 6G | |
Qiu et al. | Edge computing in industrial internet of things: Architecture, advances and challenges | |
Lu et al. | Communication-efficient federated learning and permissioned blockchain for digital twin edge networks | |
Zhang et al. | Deep learning empowered task offloading for mobile edge computing in urban informatics | |
Li et al. | An end-to-end load balancer based on deep learning for vehicular network traffic control | |
Abdulazeez et al. | Offloading mechanisms based on reinforcement learning and deep learning algorithms in the fog computing environment | |
Zhang | Mobile edge computing | |
Christopoulou et al. | Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey | |
Chen et al. | Green Internet of vehicles: Architecture, enabling technologies, and applications | |
Kherbache et al. | Digital twin network for the IIoT using eclipse ditto and hono | |
Gu et al. | AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions | |
Bárcena et al. | Enabling federated learning of explainable AI models within beyond-5G/6G networks | |
Rahbari et al. | Fast and fair computation offloading management in a swarm of drones using a rating-based federated learning approach | |
Sheraz et al. | A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G | |
Ergun et al. | A survey on how network simulators serve reinforcement learning in wireless networks | |
Xiao et al. | Learning while offloading: Task offloading in vehicular edge computing network | |
Tao et al. | O-RAN-Based Digital Twin Function Virtualization for Sustainable IoV Service Response: An Asynchronous Hierarchical Reinforcement Learning Approach | |
Yusheng et al. | A cloud-edge collaborative security architecture for industrial digital twin systems | |
Li et al. | Multi-Agent Federated DRL Enabled Resource Allocation for Air-Ground Integrated IoV Network | |
Zhang | Digital Twin: Architectures, Networks, and Applications | |
Ma et al. | Video data offloading techniques in Mobile Edge Computing: A survey | |
Li et al. | Toward Reinforcement-Learning-Based Intelligent Network Control in 6G Networks |