Reliable vehicle-mounted edge calculation unloading method based on reinforcement learning

An edge computing and reinforcement learning technology, applied in specific environment-based services, communication between vehicles and infrastructure, vehicle components, etc., to achieve the effect of maximizing service utility and performance improvement

Active Publication Date: 2021-06-08
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to incomplete coverage and intermittent connectivity in dynamic vehicular networks, it is a challenge to design a reliable and efficient computation offloading strategy

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Reliable vehicle-mounted edge calculation unloading method based on reinforcement learning
  • Reliable vehicle-mounted edge calculation unloading method based on reinforcement learning
  • Reliable vehicle-mounted edge calculation unloading method based on reinforcement learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0073] In order to describe the technical solutions disclosed in the present invention in detail, further elaboration will be made below in conjunction with specific embodiments and the accompanying drawings.

[0074] One of the development priorities of future autonomous driving technology lies in the design of on-board edge computing products. Edge computing refers to an open platform that integrates network, computing, storage, and application core capabilities on the side close to the source of objects or data, and provides the nearest end services. Its applications are launched on the edge side to generate faster network service responses and meet the basic needs of the industry in terms of real-time business, application intelligence, security and privacy protection. Edge computing sits between the physical entity and the industrial connection, or on top of the physical entity. With cloud computing, historical data from edge computing can still be accessed.

[0075] Th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a reliable vehicle-mounted edge calculation unloading method based on reinforcement learning, which is used in scenes such as limited coverage of road side units (RSUs) caused by urban obstacles or insufficient calculation capability of the road side units, and a vehicle can perform reliable calculation unloading by means of an unmanned aerial vehicle (UAV). According to the method, a multi-objective optimization problem of maximizing system utility and minimizing power consumption is converted into two sub-problems of a power distribution problem and a calculation unloading problem, and task distribution and calculation are completed by the unmanned aerial vehicle and the road side unit together. According to the method, the chance constraint is created for the transmission power, the chance constraint is converted by using a Chebyshev inequality, the minimum transmission power is derived, and the reliability of task transmission is ensured. According to the method, the calculation unloading efficiency is improved through the improved deep reinforcement learning model, the predicted target Q value in the deep reinforcement learning model is adjusted by using the result of the multi-target optimization problem, and offline training and online updating of the edge server are realized.

Description

technical field [0001] The invention belongs to the technical field of communication of the Internet of Vehicles, and in particular relates to a method for unloading reliable vehicle-mounted edge computing based on reinforcement learning. Background technique [0002] Edge computing is one of the most promising technologies to provide powerful computing capabilities to vehicles, and in order for vehicles to respond quickly in complex traffic scenarios, it enables mobile vehicles to offload computing tasks to various edge servers, such as roadside unit and roadside unit. Due to incomplete coverage and intermittent connectivity in dynamic vehicular networks, it is a challenge to design a reliable and efficient computation offloading strategy. Due to the advantages of low price, easy deployment and flexible mobility, UAVs have been applied and developed in fields including but not limited to intelligent sensor data acquisition, target tracking, disaster area monitoring and com...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/44H04L29/08H04L12/24H04B7/155H04B7/185
CPCH04W4/44H04L67/10H04L41/145H04B7/155H04B7/18504Y02T10/40Y02D30/70
Inventor 王俊华岳玉宸高广鑫
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap