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Task dependency-based clustering method applied to Internet of Vehicles

A clustering method, the technology of the Internet of Vehicles, applied in the field of communication, can solve the problem that the task clustering algorithm cannot be well applied to the vehicle self-organizing network, and achieve the effect of reducing the number of tasks that need to be processed and saving resources

Pending Publication Date: 2020-09-04
DUOLUN TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the deficiencies in the prior art, the present invention provides a clustering method based on task dependence applied in the Internet of Vehicles to solve the problem that the current task clustering algorithm in the prior art is not well applicable to the vehicle The problem with self-organizing networks

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  • Task dependency-based clustering method applied to Internet of Vehicles
  • Task dependency-based clustering method applied to Internet of Vehicles
  • Task dependency-based clustering method applied to Internet of Vehicles

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Embodiment Construction

[0032] In order to facilitate the understanding of those skilled in the art, the present invention will be further described below in conjunction with the embodiments and accompanying drawings, and the contents mentioned in the embodiments are not intended to limit the present invention.

[0033] refer to image 3 As shown, a clustering method based on task dependence applied in the Internet of Vehicles of the present invention includes the following steps:

[0034] Step 1: Construct the task arrival model and directed acyclic graph task processing model in the Internet of Vehicles network;

[0035] Step 2: Carry out correlation learning between the samples of the task set generated by each vehicle in the task arrival model and the characteristics of each vehicle in the task arrival model;

[0036] Step 3: Add relevant features from the original task to the cluster of the target task;

[0037] Step 4: Clustering different task sets.

[0038] As an example, consider the case...

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Abstract

The invention discloses a task dependency-based clustering method applied to the Internet of Vehicles. The method comprises the following steps of constructing a task arrival model and a directed acyclic graph task processing model in the Internet of Vehicles; performing correlation learning on the samples of the task set generated by each vehicle in the task arrival model and the features of eachvehicle in the task arrival model; adding the related features from the source task into a cluster of the target task; and performing clustering processing on different task sets. According to the method, the task processing efficiency in the vehicle network can be improved, so that the processing time delay and the energy consumption can be reduced in the environment of the vehicle-mounted heterogeneous network.

Description

technical field [0001] The invention relates to a task dependency-based clustering method applied in the Internet of Vehicles, belonging to the technical field of communication. Background technique [0002] As a special form of mobile ad hoc network (MANET), vehicular ad hoc network (VANET) enables vehicles to communicate with other vehicles as well as infrastructure, capable of providing various applications; such as traffic flow control, Internet access, and in-vehicle multimedia applications Wait. A variety of complex in-vehicle applications have brought great challenges to the processing capabilities of the Internet of Vehicles server. On the one hand, while considering the improvement of server processing performance, on the other hand, the processing of vehicle tasks also needs to be considered. However, due to the special network characteristics of VANETs such as dynamic topology, channel competition mechanism and various quality of service (QoS) requirements, the c...

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Application Information

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IPC IPC(8): G06K9/62G06F17/16G06F9/50
CPCG06F17/16G06F9/5027G06F18/23Y02D10/00
Inventor 宋智军赵海涛朱秋阳叶剑李小庆刘海青
Owner DUOLUN TECH CO LTD