Crowd sensing Internet of Vehicles user screening method combining clustering and CMAB

A technology of crowd sensing and screening methods, which is applied in special data processing applications, instruments, calculations, etc., and can solve problems such as efficiency reduction and information discarding

Active Publication Date: 2020-10-23
FUZHOU UNIV
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

However, there is a problem in the classical MAB framework that the information observed from some arms is discarded, resulting in a decrease in efficiency

Method used

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  • Crowd sensing Internet of Vehicles user screening method combining clustering and CMAB
  • Crowd sensing Internet of Vehicles user screening method combining clustering and CMAB
  • Crowd sensing Internet of Vehicles user screening method combining clustering and CMAB

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Embodiment Construction

[0052] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0053] The present invention proposes a crowd-sensing Internet of Vehicles user screening method combining vehicle trajectory clustering and combined multi-arm gambling machine (CMAB), which is specifically implemented according to the following steps:

[0054] Step S1: Construct user trajectory clustering features for the driving trajectories of Internet of Vehicles users, and cluster users with similar trajectories into the same cluster users.

[0055] Given the road network first, divide the minimum bounding rectangle covering the entire road network into a series of grid units. A trajectory containing a vehicle's start location, trip location, and goal location is projected onto a gridded road network and represented as a time-ordered sequence of grid cells covering their locations. The distance between different trajectories is calcul...

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Abstract

The invention relates to a crowd sensing Internet of Vehicles user screening method combining clustering and CMAB. The method comprises the following steps: constructing user trajectory clustering features for Internet of Vehicles user driving trajectories; solving an optimal worker combination by using the CMAB model and taking the trajectory clustering information as a basis for user task allocation; and verifying and analyzing the method according to the real taxi track data set. According to the user screening method combining the user trajectory information with the CMAB model, the phenomenon of poor perception data quality caused by low skill level of users participating in perception tasks is overcome, and the selected worker set can have similar driving trajectories; meanwhile, themethod can enable a task allocation algorithm based on a user screening result to tend to be stable faster and earlier. Therefore, the invention is suitable for practical application scenes.

Description

technical field [0001] The invention relates to the field of crowd-sensing Internet of Vehicles, in particular to a crowd-sensing Internet of Vehicles user screening method combined with clustering and combined multi-arm gambling machine (CMAB). Background technique [0002] With the continuous updating of embedded devices and the gradual expansion of Internet coverage, social sensing activities in smart cities have become a research hotspot in the field of information and communication. Crowd sensing technology replaces the traditional sensor network, making the sensor more real-time, convenient and low-cost, and more truly reflects the real-time status, avoiding the defects of the traditional model. In the research related to crowd perception, task allocation is one of the core issues studied by scholars at home and abroad. The research in this area is mainly divided into two categories: task independent selection and task coordination allocation. In the process of task a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9537G06K9/62H04L29/08
CPCG06F16/9537H04L67/12G06F18/23213
Inventor 冯心欣郭丹颖柳泽烽郑海峰
Owner FUZHOU UNIV
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