[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Chen et al., 2021 - Google Patents

Edge intelligent networking optimization for internet of things in smart city

Chen et al., 2021

View PDF
Document ID
7717318721519957756
Author
Chen N
Qiu T
Zhao L
Zhou X
Ning H
Publication year
Publication venue
IEEE Wireless Communications

External Links

Snippet

Massive devices connected through fifth-generation (5G) networks constitute a ubiquitous Internet of Things (IoT), providing diverse service applications in a smart city. A robust network topology structure against cyber-attacks is the foundation of highly reliable service …
Continue reading at www.cybermatics.org (PDF) (other versions)

Classifications

    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • 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

Similar Documents

Publication Publication Date Title
Chen et al. Edge intelligent networking optimization for internet of things in smart city
Qi et al. Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach
Zhang et al. Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning
Kumar et al. A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch
Sun et al. Graph-reinforcement-learning-based task offloading for multiaccess edge computing
WO2020182989A1 (en) Scheduling computation graphs using neural networks
Zheng et al. Data synchronization in vehicular digital twin network: A game theoretic approach
Meng et al. Learning-driven decentralized machine learning in resource-constrained wireless edge computing
He et al. Leveraging deep reinforcement learning with attention mechanism for virtual network function placement and routing
CN114745317A (en) Computing task scheduling method and related equipment for computing power network
Jamil et al. Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning
Wu et al. Decentralised grid scheduling approach based on multi‐agent reinforcement learning and gossip mechanism
Baghban et al. Edge-AI: IoT request service provisioning in federated edge computing using actor-critic reinforcement learning
Fan et al. DRL-D: Revenue-aware online service function chain deployment via deep reinforcement learning
Moon et al. Smart manufacturing scheduling system: DQN based on cooperative edge computing
Huang et al. Collective reinforcement learning based resource allocation for digital twin service in 6G networks
CN117914701A (en) A blockchain-based building Internet of Things performance optimization system and method
Mishra et al. Enabling cyber‐physical demand response in smart grids via conjoint communication and controller design
Liu et al. A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access
Ebrahim et al. Privacy-aware load balancing in fog networks: A reinforcement learning approach
Hu et al. Dynamic traffic signal control using mean field multi‐agent reinforcement learning in large scale road‐networks
Shu et al. Deep reinforcement learning cloud-edge-terminal computation resource allocation mechanism for IoT
Ren et al. Smig-rl: An evolutionary migration framework for cloud services based on deep reinforcement learning
Lackinger et al. Inference load-aware orchestration for hierarchical federated learning
Huang et al. Knowledge Collaboration-Based Resource Allocation in 6G IoT: A Graph Attention RL Approach