CN104954986B - A kind of opportunistic data transmission method based on more behavior places - Google Patents
A kind of opportunistic data transmission method based on more behavior places Download PDFInfo
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- CN104954986B CN104954986B CN201510307475.XA CN201510307475A CN104954986B CN 104954986 B CN104954986 B CN 104954986B CN 201510307475 A CN201510307475 A CN 201510307475A CN 104954986 B CN104954986 B CN 104954986B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
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- H04W8/00—Network data management
- H04W8/02—Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks
- H04W8/08—Mobility data transfer
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The present invention proposes a kind of opportunistic data transmission method based on more behavior places, to solve that more behavior site attribute users can not be sent data to exactly.User carries out place behavior registration to Cloud Server first, and Cloud Server preserves user/behavior place confederate matrix, and server immediate updating matrix simultaneously returns to newest timeslice vector to user.Source user is to starting data forwarding process after Cloud Server request for data.In data forwarding process, initiating line first posts messages tohIndividual different behavior site attribute users, whereinhFor place quantity.Forwarding enters gradient incremental stages, and message is successively forwarded to each behavior place and is intended to recipient.The intention recipient in all behavior places around is finally sent the data in a manner of multicast.Using user behavior attribute weights as forwarding condition, more behavior site users are forwarded the data to, data transmission rate is effectively increased, reduces energy consumption.
Description
Technical field
The present invention proposes a kind of opportunistic data transmission method based on more behavior places, belongs to opportunistic network and social activity
The crossing domain of network.
Background technology
At present, traditional sensor network comes with some shortcomings：(1) need manually to dispose substantial amounts of awareness apparatus in advance,
Not only expensive, with the expansion of scale, the complexity of system also sharply increases.(2) the static deployment of awareness apparatus, typically not
Meeting dense deployment so that perception visual angle is single, can not comprehensive thorough perceptive object.(3) data are forwarded according to node ID, no
It is adapted to the network scenarios of highly dynamic movement, can not be also applied to the network that special interests preference be present.
With the development of technology, the intelligent movable equipment with abundant sensor emerges in multitude, and intelligent perception is as next
For cognition technology, have become study hotspot in recent years.Major product of the smart mobile phone as mobile device, can be true
Ground reflects the behavior property of holder, and opportunities and challenges are brought for intelligent perception technology.The high-performance mobile to spread all over the world
Equipment has erected huge community network, and mobile subscriber can obtain in community network, sharing data, pass through the receipts to data
Integrating, analyzing is the customized privatization service of user so that intelligent perception technology generates good application value.
Intelligent perception by carry the individual of awareness apparatus shared by mobile Internet or obtained data carry out it is conscious,
Unconsciously cooperate to complete the social perception task of complexity.Intelligent perception technology is entered by the mobile terminal device largely popularized
Row perception activity, the problem of overcoming awareness apparatus costliness, perception data in all directions can be carried out using the mobile wisdom of people.
By analyzing the social of user, the data-collecting mode that guide is characterized as with user behavior is established, improves user's participation
With Data Collection performance.
A big key technology of the Data Collection as intelligent perception, based on social action analysis opportunistic Data Collection into
For the main way of intelligent perception Data Collection.However, opportunistic data collection techniques still face some technical barriers at present：
(1) used by mobile subscriber there is limitation in embedded device its calculating, storage capacity and battery power.(2) it is social
It is the primary feature that intelligent perception is different from other data perception patterns, how extracts the social property of people and be quantified as calculating
Unit be urgent problem to be solved.(3) under in general network condition, sensing node interaction data information only when meeting,
Data exchange chance is very valuable, reduces time delay, improves efficiency of transmission as another challenge in opportunistic Data Collection.
The content of the invention
Present invention aim to address in being applied in intelligent perception how by data opportunistic forwarding by way of it is accurate
Sending extremely has the problem of more behavior properties user groups.The present invention calculates use by portraying BM25 models according to user property
Similarity weights of the family on specific behavior place, and sent data to according to these weights with more behavior properties users,
The quantity of message copy in data transmission procedure is optimized, relative to other similar data collection algorithms, this method has low
Cost, high transmission rates characteristic.
The problem of Data Transport Protocol that the invention is directed in existing intelligent perception application only considers single act attribute, is utilized
The stability of user behavior, using server/user model, user behavior attribute is extracted by server, and by BM25 models
User behavior attribute is portrayed, user/behavior place confederate matrix is formed and is stored among server, server uploads according to user
Behavioural information immediate updating confederate matrix, it is ensured that real-time, the accuracy of data.Whenever source user needs to send information to
During targeted customer, source user need to ask for global information to server, and data are sent by source user opportunistic, and user data is hidden
Private is guaranteed.It is incremented by data transmission phase using ladder by the way of multicast is combined, effectively reduces message copy number
Amount, reduce and send energy consumption.Comparison according to the correlation weights between user on behavior place carries out data forwarding, improves
Data transmission success.
The present invention technical solution be：
The invention provides a kind of opportunistic data transmission method based on more behavior places.Cloud Server is built, is passed through
BM25 portrays user property formation user/behavior place confederate matrix and is stored in server.Source user to server request for data,
And opportunistic data forwarding is carried out on the similarity weights of appointed place according to user.
Explanation of nouns：
Timeslice tsij：The time that user i is consumed in behavior place j；
User's mean activity timeslice number utsi：The averagely daily activity time piece quantity of user；
All user's mean activity timeslice number avts：The averagely daily activity time piece quantity of all users；
Duration regulation attribute E：For balancing Active duration between all users and unique user；
Behavior property BeAij：Correlations of the user i on behavior place j；
Place balanced nature BAj：The activity venue factor more and less for balancing flow of the people, reflection people's behavior place
Distribution situation in all users；
Behavior place weight w eightij：User i is to behavior place j correlation weights；
Behavior place threshold values thj：Behavior place j borders weights, user's i behaviors place weight w eightij≥thjWhen, recognize
For the Interests User that user i is behavior place j.
Specific steps：
Step 1：If number of users is n, place quantity is m, and user i ∈ { 1 ..., n } are in behavior place j ∈ { 1 ..., m }
The time of consumption is with timeslice quantity (time slice) tsijMeasurement.The length (length of time slice) of timeslice
Tl is fixed as, if user i is T in the behavior place j total times consumedij, then tsij=Tij/tl。tsijReflection behavior place j
To user i correlation, timeslice quantity is bigger, and user is more related to the place.Each user i periodically submits to server to close
The time T that j is consumed in placeij, referred to as behavior place is registered；
Step 2：The mobile trajectory data that server is submitted according to user i, times of the counting user i to certain behavior place
Piece vector T Si=(tsi1,tsi2,…tsim), and result of calculation is stored in service in the form of user/behavior place confederate matrix
Device end.The user couple in user/behavior place confederate matrix is updated when server receives user i track data every time immediately
The information answered, and will statistics gained timeslice vector T SiIt is sent to user.Wherein user/behavior place confederate matrix form is such as
Shown in table 1：
Table 1：User/behavior place confederate matrix
| Behavior place 1 | Behavior place 2 | Behavior place 3 | Behavior place 4 | |
| User 1 | Timeslice ts11 | Timeslice ts12 | Timeslice ts13 | Timeslice ts14 |
| User 2 | Timeslice ts21 | Timeslice ts22 | Timeslice ts23 | Timeslice ts24 |
| User 3 | Timeslice ts31 | Timeslice ts32 | Timeslice ts33 | Timeslice ts34 |
| User 4 | Timeslice ts41 | Timeslice ts42 | Timeslice ts43 | Timeslice ts44 |
Step 3：Source user s specifies TBP=((p1,th1),(p2,th2),…,(ph,thh)), h≤m, wherein pjAnd thj, j
∈ 1 ..., and h } it is respectively behavior place and corresponding behavior place threshold values；S sends behavior place vector P to server；
Step 4：Server calculates all user's mean activity timeslice number avts, and is sent to source user s.Wherein user
Mean activity timeslice number avts computational methods are：
Step 5：Server calculates all place balanced nature vector BA (wherein according to user/behavior place confederate matrix
Place balanced nature BA corresponding to all behavior places that BA vectors are specified for source userjSet), and send to source user s.
Wherein, place j ∈ { 1 ..., h } place balanced nature BAjComputational methods be：
pvjThe total number of persons (flow of the people) averagely accessed daily for behavior place j, i.e., jth row are not 0 in confederate matrix
Number.BAjAs global factor, consideration be behavior place j relative importance.Understood according to formula (2), given area personnel
Sum, more people reached this place, then the value of balance factor will be smaller.What balance factor reflected is interested site in institute
There is the distribution situation in personnel, the final behavior weights in the less but important behavior place of flow of the people are balanced with this.
Step 6：User's mean activity timeslice number avts and place balanced nature vector BA is sent to by source user s
Each user r that meets, calculates each weight w eight on place j ∈ { 1 ..., h }sjAnd weightrj；Behavior place weights
Computational methods are as follows：
Calculate Active duration regulation attribute Ei:
Wherein utsiRepresent that user i consumes activity time piece sum daily,Duration adjusts attribute
Represent considerations of the user i to timeslice sum.k1, b is empirical parameter.B is used to adjust user's i timeslice ratios, value model
Enclose for [0,1].When b values are 0 the complete time will be used in representation formula when b values are 1 without using timeslice ratio
Piece ratio
Calculating behavior factor B eAij：
BeAijUser i is represented to behavior place j degree of correlation, its value is higher, illustrates that user i is more related to place j.
User i is for the computational methods of behavior place j weights：
weightij=BeAij·BAj (5)
Step:7：Into the initiating line stage, if weightrj≥weightsjIf the user is rj, then source user
Forward the data to rj, otherwise do not forward；
Step 8：Repeat step:6-step 7, until all h behavior places all search out all corresponding all rj；
Step 9：Source user s deletes the data of itself preservation, and now source user is initialized the incremental route of h bars；
Step:10：User r on every circuitjInquire weights of the user q each run on behavior place j
Weightqj；
Step 11：Into gradient incremental stages, if weightqj≥weightrjj, then rjForward the data to q, rjDelete
Except data；Otherwise do not forward；
Step:12：10-step 11 of repeat step, until weightqj≥thj；
Step:13：Into the multicast stage, in data lifetime, meet weight for any user qqj≥thjIf
Meet any user t to meetThen user q copies data to user t, terminates.
Beneficial effect
The present invention is solved in intelligent perception application on more behavior place opportunistic data transmission problems.Through being put down in ONE
The checking of platform the simulation experiment result knows there is the advantages of following notable relative to traditional single act place opportunistic transmission plan：
1. by portraying more behavior properties of user, data forwarding foundation of the using weights as more behavior places, energy
It is enough effectively to transfer data in the customer group with more behavior properties, improve transfer rate and data send it is accurate
Rate.
2. employing the data transfer mode that gradient is incremented by and multicast is combined in data-gathering process, effectively reduce
Data trnascription quantity in repeating process, reduce transmission energy consumption.
Brief description of the drawings
Opportunistic data transmission method flow charts of the Fig. 1 based on more behavior places.
Fig. 2 user/cloud platform interaction models.
Fig. 3 user/behavior place confederate matrix.
The more behavior locality data transmission method schematic diagrames of Fig. 4.
Embodiment
Embodiments of the present invention are introduced below in conjunction with accompanying drawing.
The present invention provides a kind of opportunistic data transmission method based on more behavior places, and this method is to the more of mobile subscriber
Behavior property is portrayed, and data forwarding foundation of the using weights as more behavior places, can effectively be transferred data to
In customer group with more behavior properties.The specific implementation flow of this method is as shown in Figure 1.This method needs Cloud Server branch
Hold, to store calculating user data, and carry out real time data interworking.User/cloud platform interaction models are as shown in Figure 2.
Specific implementation step is：
Step 1：If number of users is n, place quantity is m, and user i ∈ { 1 ..., n } are in behavior place j ∈ { 1 ..., m }
The time of consumption is with timeslice quantity (time slice) tsijMeasurement.The length (length of time slice) of timeslice
Tl is fixed as, if user i is T in the behavior place j total times consumedij, then tsij=Tij/tl。tsijReflection behavior place j
To user i correlation, timeslice quantity is bigger, and user is more related to the place.Each user i periodically submits to server to close
The time T that j is consumed in placeij, referred to as behavior place is registered；
Step 2：The mobile trajectory data that server is submitted according to user i, times of the counting user i to certain behavior place
Piece vector T Si=(tsi1,tsi2,…tsim), and result of calculation is stored in service in the form of user/behavior place confederate matrix
Device end.User/behavior place confederate matrix is as shown in Figure 3.Server updates immediately when receiving user i track data every time
Information corresponding to the user in user/behavior place confederate matrix, and will statistics gained timeslice vector T SiIt is sent to user；
Step 3：Source user s specifies TBP=((p1,th1),(p2,th2),…,(ph,thh)), h≤m, wherein pjAnd thj, j
∈ 1 ..., and h } it is respectively behavior place and corresponding behavior place threshold values；S sends behavior place vector P to server；
Step 4：Server calculates all user's mean activity timeslice number avts, and is sent to source user s.Wherein user
Mean activity timeslice number avts computational methods are：
Step 5：Server calculates all place balanced nature vector BA (wherein according to user/behavior place confederate matrix
Place balanced nature BA corresponding to all behavior places that BA vectors are specified for source userjSet), and send to source user s.
Wherein, place j ∈ { 1 ..., h } place balanced nature BAjComputational methods be：
pvjThe total number of persons (flow of the people) averagely accessed daily for behavior place j, i.e., jth row are not 0 in confederate matrix
Number.BAjAs global factor, consideration be behavior place j relative importance.Understood according to formula (2), given area personnel
Sum, more people reached this place, then the value of balance factor will be smaller.What balance factor reflected is interested site in institute
There is the distribution situation in personnel, the final behavior weights in the less but important behavior place of flow of the people are balanced with this.
Following step 6-step 12 is data transfer phase, and process is as shown in Figure 4.
Step 6：User's mean activity timeslice number avts and place balanced nature vector BA is sent to by source user s
Each user r that meets, calculates each weight w eight on place j ∈ { 1 ..., h }sjAnd weightrj；Behavior place weights
Computational methods are as follows：
Calculate Active duration regulation attribute Ei:
Wherein utsiRepresent that user i consumes activity time piece sum daily,Duration adjusts attribute
Represent considerations of the user i to timeslice sum.k1, b is empirical parameter.B is used to adjust user's i timeslice ratios, value model
Enclose for [0,1].When b values are 0 the complete time will be used in representation formula when b values are 1 without using timeslice ratio
Piece ratio
Calculating behavior factor B eAij：
BeAijUser i is represented to behavior place j degree of correlation, its value is higher, illustrates that user i is more related to place j.
User i is for the computational methods of behavior place j weights：
weightij=BeAij·BAj (5)
Step:7：Into the initiating line stage, if weightrj≥weightsjIf the user is rj, then source user
Forward the data to rj, otherwise do not forward；
Step 8：Repeat step:6-step 7, until all h behavior places all search out all corresponding all rj；
Step 9：Source user s deletes the data of itself preservation, and now source user is initialized the incremental route of h bars；
Step:10：User r on every circuitjInquire weights of the user q each run on behavior place j
Weightqj；
Step 11：Into gradient incremental stages, if weightqj≥weightrjj, then rjForward the data to q, rjDelete
Except data；Otherwise do not forward；
Step:12：10-step 11 of repeat step, until weightqj≥thj；
Step:13：Into the multicast stage, in data lifetime, meet weight for any user qqj≥thjIf
Meet any user t to meetThen user q copies data to user t, terminates.
Claims (1)
1. a kind of opportunistic data transmission method based on more behavior places, it is characterised in that comprise the following steps that：
Step 1：If number of users is n, place quantity is m, and user i ∈ { 1 ..., n } consume in behavior place j ∈ { 1 ..., m }
Time with timeslice quantity (time slice) tsijMeasurement, the length of timeslice is fixed as tl, if user i is in behavior place j
The total time consumed is Tij, then tsij=Tij/tl；
Step 2：The mobile trajectory data that server is submitted according to user i, counting user i to the timeslice in certain behavior place to
Measure TSi=(tsi1,tsi2,…tsim), and result of calculation is stored in server in the form of user/behavior place confederate matrix；
Updated immediately when server receives user i track data every time in user/behavior place confederate matrix corresponding to the user
Information, and will statistics gained timeslice vector T SiIt is sent to user；
Step 3：Source user s specifies TBP=((p1,th1),(p2,th2),…,(ph,thh)), h≤m, wherein pjAnd thj, j ∈
1 ..., and h } it is respectively behavior place and corresponding behavior place threshold values；S sends behavior place vector P to server；
Step 4：Server calculates all user's mean activity timeslice number avts, and is sent to source user s, and wherein user is averaged
Activity time piece number avts computational methods are：
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Office the factor, consideration be behavior place j relative importance；
Step 6：User's mean activity timeslice number avts and place balanced nature vector BA is sent to each by source user s
Meet user r, calculates each weight w eight on place j ∈ { 1 ..., h }sjAnd weightrj；Behavior place weight computing
Method is as follows：
Calculate Active duration regulation attribute Ei:
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1]；When b values are 0 complete timeslice ratio will be used in representation formula when b values are 1 without using timeslice ratio
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BeAijUser i is represented to behavior place j degree of correlation, its value is higher, illustrates that user i is more related to place j；
User i is for the computational methods of behavior place j weights：
weightij=BeAij·BAj (5)
Step:7：Into the initiating line stage, if weightrj≥weightsjIf the user is rj, then source user is by number
According to being transmitted to rj, otherwise do not forward；
Step 8：Repeat step 6 is to step 7, until all h behavior places all search out corresponding rj；
Step 9：Source user s deletes the data of itself preservation, and now source user is initialized the incremental route of h bars；
Step:10：User r on every circuitjInquire weights Weights of the user q each run on behavior place jqj；
Step 11：Into gradient incremental stages, ifThen rjForward the data to q, rjDelete data；
Otherwise do not forward；
Step:12：Repeat step 10 is to step 11, until weightqj≥thj；
Step:13：Into the multicast stage, in data lifetime, meet weight for any user qqj≥thjIf meet
Any user t meetsThen user q copies data to user t, terminates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510307475.XA CN104954986B (en) | 2015-06-05 | 2015-06-05 | A kind of opportunistic data transmission method based on more behavior places |
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| CN101977226A (en) * | 2010-10-28 | 2011-02-16 | 北京航空航天大学 | Novel opportunity network data transmission method |
| CN103747523A (en) * | 2014-01-14 | 2014-04-23 | 上海河广信息科技有限公司 | User position predicating system and method based on wireless network |
| CN104199828A (en) * | 2014-07-26 | 2014-12-10 | 复旦大学 | Method for establishing social network based on transaction log data |
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| CN101977226A (en) * | 2010-10-28 | 2011-02-16 | 北京航空航天大学 | Novel opportunity network data transmission method |
| CN103747523A (en) * | 2014-01-14 | 2014-04-23 | 上海河广信息科技有限公司 | User position predicating system and method based on wireless network |
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