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Mobile crowd sensing data equalization method based on blockchain right proving mechanism

A mobile crowd-sensing and proof mechanism technology, applied in the field of mobile crowd-sensing data balance based on the blockchain equity proof mechanism, can solve the problems of long sensing time, affecting sensing quality, affecting the speed of sensing data convergence, etc., to achieve accelerated Convergence speed, the effect of achieving data balance

Active Publication Date: 2020-12-01
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 crowd sensing data equalization method based on blockchain right proving mechanism
  • Mobile crowd sensing data equalization method based on blockchain right proving mechanism
  • Mobile crowd sensing data equalization method based on blockchain right proving 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...

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Abstract

The invention discloses a mobile crowd sensing data equalization method based on a blockchain right proving mechanism in the field of mobile crowd sensing networks. The system is used for executing the following steps that: S1: in a time slot period, a sensing platform performs pyramid tree algorithm-based data clustering on received sensing data, and calculates the number of sensing data contained in each cluster; S2, whether the quantity of the perception data contained in the added cluster is smaller than a quantity threshold value or not is judged, and a quantity judgment parameter value is output; S3, each sensing node calculates a utility function value of each sensing node according to the evaluation parameter value; and S4, the active state of the sensing node is determined according to the utility function. According to the method, in the whole mobile crowd sensing task execution process, the sensing platform selects as few participants as possible to complete the sensing task, the quality requirement for sensing node space coverage of a specified sensing area is met, and data balance is achieved in the space 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 Applications(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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