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Mobile Crowdsensing Data Equalization Method Based on Blockchain Equity Proof Mechanism

A technology of mobile crowd sensing and data perception, which is applied in the field of mobile crowd sensing data balance based on the proof-of-rights mechanism of the block chain, can solve problems such as long sensing time, affecting the quality of sensing, and lack of data in sensing areas, etc., to achieve data The effect of balancing and accelerating the convergence speed

Active Publication Date: 2021-05-04
GUANGDONG POLYTECHNIC NORMAL UNIV
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

This feature makes: 1) Part of the perceptual area is oversampled
2) There is a problem of missing data in some perception areas, which affects the overall perception quality
3) The randomness of the arrival time of sensing nodes makes a considerable part of the sensing area exceed the number k of nodes required by the coverage model, but requires a long sensing time, which affects the convergence speed of sensing data
In general, it is difficult for mobile crowd sensing data to achieve balance in both space and time dimensions, and it is necessary to solve the problem of unbalanced mobile crowd sensing data

Method used

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  • Mobile Crowdsensing Data Equalization Method Based on Blockchain Equity Proof Mechanism
  • Mobile Crowdsensing Data Equalization Method Based on Blockchain Equity Proof Mechanism
  • Mobile Crowdsensing Data Equalization Method Based on Blockchain Equity Proof Mechanism

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Embodiment 1

[0034] The data balance problem of mobile crowd sensing is actually a problem of calculating the minimum participant group that meets the task coverage quality requirements, that is, within the sensing period T=l×t, for a given participant candidate set U={u 1 ,u 2 ,...,u n}, from which h participants are selected to form the participant set U i ={u i1 ,u i2 ,...,u ih} and several reference points in the perception area constitute the target reference point set R'={r 1 ,r 2 ,...,r m′}, then mobile crowd sensing requires each target location r in R’ j There are at least k participants on . which is

[0035] Minimize:|U i | (1)

[0036]

[0037]

[0038] Among them, Equation 1 indicates that the set of hired participants is the smallest, and Equation 2 indicates that each target location r in the target reference point set R' j There are at least k participants, and Equation 3 indicates that the participant set is a subset of the participant candidate set and t...

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Abstract

The invention discloses a mobile crowdsensing data equalization method based on a block chain proof of rights mechanism in the field of mobile crowdsensing networks. The steps for execution by the system include: S1. Perform data clustering based on the pyramid tree algorithm for the sensory data of each cluster, and calculate the number of sensory data contained in each cluster; S2, judge whether the number of sensory data contained in the added cluster is less than the quantity threshold, and output the quantity evaluation parameter value; S3, each sensing node calculates the utility function value of each sensing node according to the evaluation parameter value; S4, determines the activity state of the sensing node according to the utility function. In the entire mobile crowd sensing task execution process of the method of the present invention, the sensing platform selects as few participants as possible to complete the sensing task, so as to meet the quality requirement of spatial coverage of the sensing nodes in the designated sensing area, and realize data balance in the spatial dimension .

Description

technical field [0001] The invention relates to the field of mobile crowd-sensing networks, in particular to a mobile crowd-sensing data equalization method based on a block chain equity proof mechanism. Background technique [0002] In the mobile crowd sensing network, the distribution of sensing nodes (participants) in the sensing area is non-uniform, showing the characteristics of power law distribution. This property makes: 1) Part of the perceptual area is oversampled. 2) There is a problem of missing data in some perception areas, which in turn affects the overall perception quality. 3) The randomness of the arrival time of sensing nodes makes a considerable part of the sensing area exceed the number k of nodes required by the coverage model, but requires a long sensing time, which affects the convergence speed of sensing data. [0003] Therefore, for problems 1)-3), for large-scale crowd sensing applications, when the number of nodes in the sensing area exceeds the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50G06K9/62
CPCG06F9/5061G06F18/23G06F18/22
Inventor 岑健刘溪宋海鹰
Owner GUANGDONG POLYTECHNIC NORMAL UNIV
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