CN110189174A - A kind of mobile intelligent perception motivational techniques based on quality of data perception - Google Patents
A kind of mobile intelligent perception motivational techniques based on quality of data perception Download PDFInfo
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Abstract
The invention proposes a kind of mobile intelligent perception motivational techniques based on quality of data perception, and steps are as follows: S1, aware platform issue perception task set；S2, Edge Server obtain the task of aware platform publication, and perception task is sent to user；S3, participant carry out perception data acquisition, and by collected perception data and bid and upload to Edge Server；Perception data that S4, Edge Server are uploaded according to participant is bidded and prestige value matrix, passes through reverse auction algorithm and victor's set is chosen in critical remuneration；S5, Edge Server calculate the perception deviation of each participant, and perception deviation is sent to Prestige Management server；S6, Prestige Management server update participant prestige value matrix.The method of the present invention can motivate user honestly to upload perception data well, inhibit malicious user, guarantee the quality of perception data and minimize platform cost.
Description
Technical field
The invention mainly relates to a kind of mobile intelligent perception motivational techniques of the multidimensional data based on quality of data perception, belong to
Intelligent perception technical field.
Background technique
Mobile intelligent perception needs a large amount of ordinary users to participate in, but during participating in perception, participant needs to put into
Regular hour and energy, it is also desirable to the resources such as electricity, flow, storage, the communication of equipment of itself are consumed, in addition, perception data
Acquisition usually require to easily cause user to hidden using the sensor built in mobile device, such as camera, microphone, GPS
The worry of private leakage.Participant's lazy weight seriously limits the development of mobile intelligent perception, if without reasonable excitation set
System, the enthusiasm that user participates in perception task is not high, and will lead to perception task can not be efficiently accomplished.
Current incentive mechanism mainly has remuneration payment, amusement game, social networks, virtual integral etc., so that participant
Perception task can actively be quickly completed.The artificial horde intelligence perception such as Yang introduces two models: being based on Stackelberg
Incentive mechanism of the Game Design centered on platform, for maximizing aware platform effectiveness；Based on auction algorithm devise with
The incentive mechanism of user-center does not have to manipulation auction price for keeping user's authenticity.In order to motivate user to join for a long time
With perception task, Gao et al. proposes a kind of incentive mechanism based on VCG auction, detecting period is divided into multiple time slots in line selection
User is selected, maximizes social welfare in each time slot.
But a large amount of participant does not ensure that perception task is completed in high quality, presently, there are incentive mechanism
Generally have ignored perception data quality problems, the perception data quality that platform is collected is not quite similar and is easy that there are malicious attacks
The presence of person, a large amount of low quality datas will affect the accuracy of data analysis result, cause the benefit of task publisher that cannot reach
To actual desired.Zhang et al. is selected using user location as one of motivator, according to user geographical location using greedy algorithm
The user of task can preferably be completed by selecting, so that improving task completes quality, while also be used according to execution task in each region
Amount amount to carry out remuneration payment to the user of different zones.But some malice participant can forge location information or upload empty
False data obtains illegal consideration.
How to comprehensively consider the privacy of participant, perceive the cost of participation rate, the quality of data and aware platform, designs one
Reasonable incentive mechanism, makes aware platform under the premise of paying smaller cost, and satisfied perception is obtained in terms of quality and quantity two
Data are of great significance to the long-term stability development for promoting mobile gunz sensory perceptual system platform.
Summary of the invention
Aiming at the problem that current mobile intelligent perception motivational techniques not can guarantee perception data quality, the invention proposes one
The mobile intelligent perception motivational techniques that kind is perceived based on the quality of data, utilizing Edge Server to carry out, space-time is anonymous, and transmission perceives
Task introduces quality of data constraint and reputation updating mechanism, selects victor's set rationally using reverse auction model come minimum
Allelopathic knows platform assembly sheet, solves the problems, such as the meter for mitigating Cloud Server while participant's secret protection and malice participant are attacked
Pressure is calculated, time delay is reduced.
In order to solve the above technical problems, present invention employs following technological means:
A kind of mobile intelligent perception motivational techniques based on quality of data perception, comprising the following steps:
S1, aware platform issue perception task set；
S2, Edge Server obtain the task of aware platform publication, and perception task is sent to fringe node covering model
Enclose interior user；
S3, perception task participant carry out perception data acquisition, and collected perception data and bidding is uploaded to
Edge Server；
Perception data that S4, Edge Server are uploaded according to participant, the prestige bidded and be stored on Prestige Management device
Value matrix chooses victor's set by reverse auction algorithm and critical remuneration, and winning information and remuneration information is sent to
Victor；
S5, Edge Server calculate the perception deviation of each participant according to the perception data that participant uploads, and will sense
Know that deviation is sent to Prestige Management server；
S6, Prestige Management server update participant prestige value matrix.
Further, the perception task collection in the step S1 is combined into T={ τ1, τ2..., τm, m are included in set
Different perception task, wherein τjFor j-th of perception task, j=1,2 ..., m.
Further, the aware platform also issues the quality of data constraint set Q, Q corresponding with perception task set T
={ q1, q1..., qm, wherein qjFor with perception task τjCorresponding quality of data constraint.
Further, the concrete operations of step S3 are as follows:
S31, it is equipped with n participant, participant's collection is combined into U={ ω1, ω2..., ωn, wherein n >=2.
S32, participant ωiIt chooses one or more perception tasks and carries out perception data collection, wherein i=1,2 ..., n,
Participant ωiPerception data siFor v dimensional vector, si={ si1, si2..., siv, }.
S33, participant are by collected perception data siWith the b that bidsiUpload to Edge Server.
Further, the concrete operations of step S4 are as follows:
S41, Edge Server obtain the history prestige value matrix [r stored on Prestige Management serverij], [rij] ∈ [0,
1]n×m, wherein rijIndicate participant ωiParticipate in subtask τjThe degree of reliability, as participant ωiSubtask τ is participated in for the first timej
When, enable rij=0.5.
S42, Edge Server are according to decision variable δijWith prestige value matrix [rij] quality of data matrix R is calculated, it is specific public
Formula is as follows:
R=[θij]=[δij·rij] ∈ [0,1]n×m (1)
Wherein, θijIndicate participant ωiParticipate in subtask τjWhen participant ωiThe quality of data of offer.
In S43, first round auction, Edge Server selects the maximum participant of the quality of data in quality of data matrix R to make
For victor, victor is added in victor's set S, and victor is removed from participant's set:
In S44, new round auction, Edge Server chooses cost-effectiveness from the participant of removed victor set
Contribute maximum participant as new victor, the calculation formula of cost-effectiveness contribution is as follows:
Wherein, Fi(S) participant ω when given victor's set S is indicatediCost-effectiveness contribution, Hi(S) it indicates given to obtain
Participant ω when victor set SiComplete one group of subtask ΓiTotal weighting contributrion margin, biFor participant ωiBid.
S45, victor that S45 is selected is removed from participant's set, obtains new participant set U ', and from set
The middle selection cost-effectiveness of U ' contributes maximum participant ωl′。
The cost-effectiveness contribution of S46, the victor for enabling S45 select are greater than participant ωl′Cost-effectiveness contribution, calculating obtains
The critical remuneration of victor, formula are as follows:
Wherein, pkFor victor ωkCritical remuneration, HkIt (S) is victor ωkTotal weighting contributrion margin, Hl′(S) it is
Participant ωl′Total weighting contributrion margin, bl′For participant ωl′Bid.
S47, when the critical remuneration of victor be greater than when bidding of the victor, by the victor be added victor's set S
In.
S48, step S44 to S47 is repeated, until victor's set S meets the quality of data requirement of perception set of tasks T.
S49, according to victor set S, winning information and remuneration information are sent to victor, wherein the report of victor
Reward is the critical remuneration of victor.
Further, the concrete operations of step S5 are as follows:
S51, Edge Server polymerize the perception data of all participants, polymerization result STIt is as follows:
S52, participant ω is calculatediPerception data siWith final polymerization result sTDeviation:
Wherein, diFor participant ωiPerception data deviation.
S53, to Multidimensional Awareness data siIt is normalized, perception data deviation diModification are as follows:
Wherein, [Lk, Rk] indicate that kth ties up the interval range where perception data.
S54, perception data deviation set { d is calculatedi, i ∈ U } medianAnd by perception data deviation set and middle position
Number is sent to Prestige Management server.
Further, in step S6 Prestige Management server update participant prestige value matrix, the credit value of participant
Calculation formula is as follows:
Wherein,Indicate participant ω after updatingiParticipate in subtask τjThe degree of reliability, γ and η are for scaling
Positive number, λ is adjusting parameter.
Using following advantage can be obtained after the above technological means:
The invention proposes a kind of mobile intelligent perception motivational techniques based on quality of data perception, in mobile intelligent perception
Edge Server is introduced in system, and anonymization processing is carried out to the data that participant submits, protects participant's privacy, meanwhile, side
Edge server has certain calculating and storage capacity, can mitigate the calculating pressure of Cloud Server, reduces time delay, guarantees system
Service quality.The method of the present invention introduces quality of data constraint and prestige value matrix, according to participant in history in aware platform
The performance of the task of the same race of upper participation comprehensively considers the subtask set of participant's selection, bids and the factors such as credit value,
On the basis of meeting quality of data requirement, biggish participant is contributed using based on recursive greedy algorithm selection cost-effectiveness
As victor, and pay corresponding critical remuneration.The complexity of the method for the present invention is lower, computational efficiency is higher, can obtain
Optimal platform cost.In addition, devising a kind of reputation updating function in the method for the present invention, true participant can be encouraged to submit
Honest data inhibit malice participant to submit false data.The method of the present invention can be in quality, the minimum for guaranteeing perception data
User is motivated to participate in perception task under the premise of platform cost, motivational techniques are more reliable, more efficient.
Detailed description of the invention
Fig. 1 is a kind of step flow chart of the mobile intelligent perception motivational techniques based on quality of data perception of the present invention.
Fig. 2 is a kind of main body interaction figure of the mobile intelligent perception motivational techniques based on quality of data perception of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments and specification
Attached drawing carries out clear, complete description to technical solution of the present invention, it is clear that specific embodiment described herein is only to solve
The present invention is released, is not intended to limit the present invention.
Those skilled in the art can understand that unless otherwise defined, all terms used herein (including skill
Art term and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Also
It should be understood that those terms such as defined in the general dictionary should be understood that have in the context of the prior art
The consistent meaning of meaning will not be explained in an idealized or overly formal meaning and unless defined as here.
The present invention proposes a kind of mobile intelligent perception motivational techniques based on quality of data perception, such as Fig. 1 combination Fig. 2 institute
Show, specifically includes the following steps:
S1, aware platform issue perception task set；Perception task of the aware platform publication comprising m different perception tasks
Set T={ τ1, τ2..., τm, wherein τjFor j-th of perception task, j=1,2 ..., m.Meanwhile aware platform is also issued
The quality of data corresponding with perception task constrains set Q={ q1, q1..., qm, wherein qjFor with perception task τjCorresponding number
It is constrained according to quality.Quality of data constraint is the threshold value that aware platform is arranged at the beginning of task publication according to specific tasks, is collected
Perception data the quality of data should not less than platform setting the quality of data constraint.Quality of data constraint is for motivating ginseng
The data of high quality, such as the intelligent perception system for analyzing whole city's traffic congestion situation are provided with person, data acquisition is appointed
Business needs a large number of users periodically to shoot traffic live-pictures or video in the same area, and quality of data constraint is for motivating ginseng
The image or video of clear high quality are uploaded with person.
S2, Edge Server obtain the task of aware platform publication, and perception task is sent to fringe node covering model
Enclose interior user.It include multiple fringe nodes in Edge Server, each fringe node can broadcast perception in its coverage area
Task.
S3, perception task participant carry out perception data acquisition, and collected perception data and bidding is uploaded to
Edge Server；Concrete operations are as follows:
S31, it is equipped with n participant, participant's collection is combined into U={ ω1, ω2..., ωn, wherein n >=2.
In S32, perception task set comprising multiple perception tasks, by distance, traffic, the image of the factors such as interest,
Participant ωiOne or more perception tasks can be chosen and carry out perception data collection, ΓiIndicate participant ωiOne group of selection
Subtask,Wherein, i=1,2 ..., n.Participant ωiPerception data siFor v dimensional vector, si={ si1, si2...,
siv, the perception data of multidimensional can be used to indicate complicated sensing results.
S33, participant complete subtask ΓiNeed certain cost ci, cost herein includes that the electricity of mobile device disappears
Consumption, the loss of communication cost expense, the computing capability of participant etc., participant can determine the b that bids according to the cost of oneself consumptioni,
Due to the selfishness and tactic of participant, the b that bids that participant sends to aware platform under normal conditionsi≥ci.Participant will
Collected perception data siWith the b that bidsiUpload to Edge Server.
Perception data that S4, Edge Server are uploaded according to participant, the prestige bidded and be stored on Prestige Management device
Value matrix chooses victor's set by reverse auction algorithm and critical remuneration, and winning information and remuneration information is sent to
Victor；Concrete operations are as follows:
S41, the prestige that same or similar perception task is participated in participant's history is stored on Prestige Management server
Value, Edge Server obtain the history prestige value matrix [r stored on Prestige Management serverij], [rij] ∈ [0,1]n×m,
In, rijIndicate participant ωiParticipate in subtask τjThe degree of reliability, credit value is higher, the reliability of the data of the participant
It is bigger.As participant ωiSubtask τ is participated in for the first timejWhen, enable rij=0.5.
S42, Edge Server are according to decision variable δijWith prestige value matrix [rij] quality of data matrix R is calculated, it is specific public
Formula is as follows:
R=[θij]=[δij·rij] ∈ [0,1]n×m (9)
Wherein, θijIndicate participant ωiParticipate in subtask τjWhen participant ωiThe quality of data of offer, δijIndicate selection
Decision variable, if participant ωiSelection participates in completing task τj, then δij=0, conversely, being then 1:
Quality of data matrix also may indicate that are as follows:
S43, the method for the present invention target be meet the quality of data constraint under conditions of, minimize aware platform expenditure.
In first round auction, enabling victor's set S is empty set, and Edge Server selects the quality of data in quality of data matrix R maximum
Participant as victor, victor is added in victor's set S, and by victor from participant gather in remove:
In the case where giving current victor's set S, the present invention defines current victor to sub- task τjTotal contribution
φj(S) are as follows:
When given victor's set S, participant ωiTo completion subtask τjContributrion margin MijAre as follows:
Wherein, qjFor subtask τjThe quality of data constraint.
Enabling weight is 1/qj, participant ωiComplete one group of subtask ΓiTotal weighting contributrion margin be Hi(S):
Define participant ωiTotal weighting contributrion margin piecei(S) with the b that bids of the participantiThe ratio between be cost-effectiveness tribute
Offer Fi(S):
S44, in the auction of a new round, Edge Server from removed victor participant gather in be selected to
This benefit contributes maximum participant as new victor.
S45, in order to guarantee that authenticity that participant bids, the method for the present invention define the concept of critical remuneration, critical report
Reward is referred to according to participant ωiThe quality of data, as participant ωiThe maximum remuneration that participant can obtain when triumph.In order to
Find victor ωkCritical remuneration, victor that S45 is selected is removed from participant's set, obtains new participant's collection
U ' is closed, is gathered based on current victor, contributes maximum participant ω from the middle selection cost-effectiveness of set U 'l′。
If S46, ωkω can be replaced in auctionl′As victor, then ωkCost-effectiveness contribution must compare
ωl′Greatly, i.e.,The critical remuneration of victor is calculated, formula is as follows:
Wherein, pkFor victor ωkCritical remuneration, HkIt (S) is victor ωkTotal weighting contributrion margin, Hl′(S) it is
Participant ωl′Total weighting contributrion margin, bl′For participant ωl′Bid.
S47, as victor ωkCritical remuneration be greater than when bidding of the victor, by the victor be added victor collection
It closes in S, conversely, choosing victor again.
S48, repeat step S44 to S47, until victor's set S meet perception set of tasks T the quality of data requirement,
That is all victors can be task τ in victor's set SjThe total data quality of offer is not less than the quality of data of the task about
Beam qj。
S49, according to victor set S, winning information and remuneration information are sent to victor, wherein the report of victor
Reward is the critical remuneration of victor.
S5, Edge Server calculate the perception deviation of each participant according to the perception data that participant uploads, and will sense
Know that deviation is sent to Prestige Management server；Concrete operations are as follows:
After S51, Edge Server receive the credit value of Prestige Management server transmission, Edge Server polymerize all participations
The perception data of person, polymeric rule are the weighted average for calculating each all perception datas of task, and wherein weight is every ginseng
With the credit value of person.Polymerization result STIt is as follows:
S52, participant ω is calculatediPerception data siWith final polymerization result sTDeviation:
Wherein, diFor participant ωiPerception data deviation.The reliability and deviation d of perception dataiIt is related, diIt is smaller,
Then indicate that perception data is more reliable.
The perception data s that S53, participant uploadiIt is a multi-C vector data, each element is not very likely one
In a order of magnitude, if without normalized, perception data deviation diIt is difficult accurately to reflect the reliability of data.Cause
This, Edge Server normalizes to the value of each dimension of perception data in [0,1] section.Assuming that [Lk, Rk] indicate kth dimension
According to the interval range at place, then the perception data deviation after normalization are as follows:
S54, perception data deviation set { d is calculatedi, i ∈ U } medianAnd by perception data deviation set and middle position
Number is sent to Prestige Management server.
S6, Prestige Management server update participant prestige value matrix.The rule that credit value updates are as follows: participant is doing
After some reliable rows are out, credit value can be gradually increased, but after making some dishonest or malice behavior, prestige
Value should significantly reduce.In addition, in order to improve the enthusiasm of participant, participant in systems can be by the way that continually provide can
Higher reputational value is obtained by data, and the perception data for continually providing mistake should possess extremely low credit value.Assuming that malice
The ratio of participant is less than half, if the perception data deviation of participant is less thanIt may be considered that this participant is made that really
Implementation is that the method for the present invention also introduces adjusting parameter λ, for participant ωiIf his perception data deviation
Credit value, and d should then be increasediSmaller, credit value also should increase faster.On the contrary, if perception data deviationThen credit value can reduce, andValue it is bigger, credit value also should reduce faster.The credit value of participant
Calculation formula is as follows:
Wherein,Indicate participant ω after updatingiParticipate in subtask τjThe degree of reliability, γ and η are for scaling
Positive number.WhenWhen, show that participant has submitted an authentic data, if participant persistently submits authentic data,
Credit value can gradually converge to 1.WhenThen credit value is constant compared with last time.And work asShow that participant mentions
Primary insecure perception data is handed over, credit value can decline compared with last time, if persistently submitting corrupt data, credit value can be fast
Speed converges to 0.
The method of the present invention calculation amount is little, computational efficiency is high, because using Edge Server, the calculating pressure of Cloud Server
Power is smaller, and time delay can be effectively reduced.Quality of data constraint and prestige value matrix are introduced in the method for the present invention simultaneously, it both can be with
The quality for guaranteeing perception data can also motivate participant to upload high-quality data, the method for the present invention comprehensively considered privacy of user,
The problems such as perception data quality, platform cost, provides a kind of more efficient, more reliable mobile intelligent perception motivational techniques.
Embodiments of the present invention are explained in detail above in conjunction with attached drawing, but the invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
It puts and makes a variety of changes.
Claims (7)
1. a kind of mobile intelligent perception motivational techniques based on quality of data perception, which comprises the following steps:
S1, aware platform issue perception task set；
S2, Edge Server obtain the task of aware platform publication, and perception task is sent in fringe node coverage area
User；
S3, perception task participant carry out perception data acquisition, and by collected perception data and bid and upload to edge
Server；
Perception data that S4, Edge Server are uploaded according to participant, the credit value square bidded and be stored on Prestige Management device
Battle array chooses victor's set by reverse auction algorithm and critical remuneration, and winning information and remuneration information is sent to triumph
Person；
S5, Edge Server calculate the perception deviation of each participant according to the perception data that participant uploads, and will perceive inclined
Difference is sent to Prestige Management server；
S6, Prestige Management server update participant prestige value matrix.
2. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 1, feature exist
In the perception task collection in the step S1 is combined into T={ τ1, τ2..., τm, appoint in set comprising m different perception
Business, wherein τjFor j-th of perception task, j=1,2 ..., m.
3. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 2, feature exist
In the aware platform also issues the quality of data constraint set Q, Q={ q corresponding with perception task set T1, q1...,
qm, wherein qjFor with perception task τjCorresponding quality of data constraint.
4. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 3, feature exist
In the concrete operations of step S3 are as follows:
S31, it is equipped with n participant, participant's collection is combined into U={ ω1, ω2..., ωn, wherein n >=2；
S32, participant ωiIt chooses one or more perception tasks and carries out perception data collection, wherein i=1,2 ..., n are participated in
Person ωiPerception data siFor v dimensional vector, si={ si1, si2..., siv, }；
S33, participant are by collected perception data siWith the b that bidsiUpload to Edge Server.
5. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 4, feature exist
In the concrete operations of step S4 are as follows:
S41, Edge Server obtain the history prestige value matrix [r stored on Prestige Management serverij], [rij] ∈ [0,1]n×m,
Wherein, rijIndicate participant ωiParticipate in subtask τjThe degree of reliability, as participant ωiSubtask τ is participated in for the first timejWhen, it enables
rij=0.5；
S42, Edge Server are according to decision variable δijWith prestige value matrix [rij] quality of data matrix R is calculated, specific formula is such as
Under:
R=[θij]=[δij·rij] ∈ [0,1]n×m
Wherein, θijIndicate participant ωiParticipate in subtask τjWhen participant ωiThe quality of data of offer；
In S43, first round auction, Edge Server selects in quality of data matrix R the maximum participant of the quality of data as obtaining
Victor is added in victor's set S victor, and victor is removed from participant's set:
In S44, new round auction, Edge Server chooses cost-effectiveness contribution from the participant of removed victor set
For maximum participant as new victor, the calculation formula of cost-effectiveness contribution is as follows:
Wherein, Fi(S) participant ω when given victor's set S is indicatediCost-effectiveness contribution, Hi(S) given victor is indicated
Participant ω when set SiComplete one group of subtask ΓiTotal weighting contributrion margin, biFor participant ωiBid；
S45, the victor that S45 is selected is removed from participant's set, obtains new participant set U ', and from set U '
It chooses cost-effectiveness and contributes maximum participant ωl′；
The cost-effectiveness contribution of S46, the victor for enabling S45 select are greater than participant ωl′Cost-effectiveness contribution, calculate victor
Critical remuneration, formula is as follows:
Wherein, pkFor victor ωkCritical remuneration, HkIt (S) is victor ωkTotal weighting contributrion margin, Hl′It (S) is participation
Person ωl′Total weighting contributrion margin, bl′For participant ωl′Bid；
S47, when the critical remuneration of victor be greater than when bidding of the victor, will the victor be added victor's set S in；
S48, step S44 to S47 is repeated, until victor's set S meets the quality of data requirement of perception set of tasks T；
S49, according to victor set S, winning information and remuneration information are sent to victor, wherein the remuneration of victor is
The critical remuneration of victor.
6. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 5, feature exist
In the concrete operations of step S5 are as follows:
S51, Edge Server polymerize the perception data of all participants, polymerization result STIt is as follows:
S52, participant ω is calculatediPerception data siWith final polymerization result sTDeviation:
Wherein, diFor participant ωiPerception data deviation；
S53, to Multidimensional Awareness data siIt is normalized, perception data deviation diModification are as follows:
Wherein, [Lk, Rk] indicate that kth ties up the interval range where perception data；
S54, perception data deviation set { d is calculatedi, i ∈ U } medianAnd perception data deviation set and median are sent out
Give Prestige Management server.
7. a kind of mobile intelligent perception motivational techniques based on quality of data perception according to claim 6, feature exist
In, the prestige value matrix of Prestige Management server update participant in step S6, the credit value calculation formula of participant is as follows:
Wherein,Indicate participant ω after updatingiParticipate in subtask τjThe degree of reliability, γ and η are for scalingJust
Number, λ is adjusting parameter.
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