CN108768591A - A method of the number of retransmissions dynamic based on the triggering of real-time packet loss information adjusts - Google Patents
A method of the number of retransmissions dynamic based on the triggering of real-time packet loss information adjusts Download PDFInfo
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- CN108768591A CN108768591A CN201810521031.XA CN201810521031A CN108768591A CN 108768591 A CN108768591 A CN 108768591A CN 201810521031 A CN201810521031 A CN 201810521031A CN 108768591 A CN108768591 A CN 108768591A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0006—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission format
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/12—Arrangements for detecting or preventing errors in the information received by using return channel
- H04L1/16—Arrangements for detecting or preventing errors in the information received by using return channel in which the return channel carries supervisory signals, e.g. repetition request signals
- H04L1/18—Automatic repetition systems, e.g. Van Duuren systems
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Abstract
The present invention proposes a kind of method of the number of retransmissions dynamic adjustment triggered based on real-time packet loss information, including step：The system mode for collecting discrete linear time-invariant system is transferred to long-range estimator after passing it through Kalman filtering processing；It is reached or is lost according to data packet, error covariance the characteristics of restraining or increased dramatically of remote status estimation, design retransmits the index of desirability, and independently selects the variation function of the index to adjust the increase and decrease width of index；For continually changing desirability index, the increase and decrease activation threshold value of number of retransmissions is designed；In transmission process, when desirability index reaches increase and decrease activation threshold value, number of retransmissions will increase and decrease accordingly, and update for data transmission next time.Algorithm according to the present invention, identical in energy expenditure, dynamic adjustment number of retransmissions algorithm can more optimize the performance remotely estimated than mean allocation number of retransmissions algorithm.
Description
Technical field
The present invention relates to the information physical system fields in communication engineering, especially a kind of to be triggered based on real-time packet loss information
Number of retransmissions dynamic adjustment method.
Background technology
In information physical system, the performance for improving remote status estimation is researcher's concern always, because
In actual life, remote status estimation plays critically important effect.In test application, long-range estimation can detect fire
Calamity, earthquake and environmental pollution；Industrial production needs long-range estimation in real time to adjust control command, it is also desirable to by long-range estimation
To carry out fault diagnosis and information security detection.
Up to the present, the performance boost method of remote status estimation has been achieved for many achievements in research.But at present still
It has the following problems：1) channel status has uncertainty, and data packet may be caused to generate delay, error code in transmission process very
To the case where loss, the accuracy remotely estimated is reduced；2) how to balance estimation performance and energy expenditure relationship be also there is an urgent need for
It solves the problems, such as；3) the information transmission of large-scale control system may face the threat of information security and novel attack.
Invention content
Goal of the invention：In order to solve the above technical problems, the present invention proposes a kind of re-transmission triggered based on real-time packet loss information
The method of number dynamic adjustment.This method can utilize real-time data-bag lost information, realize under resource constrained environment,
Dynamic adjustment number of retransmissions is utmostly to reduce data loss rate, without pre-allocating each data packet in advance in its transmission time slot
Number of retransmissions can be obtained the good transmission result of effect.
Technical solution：For the above-mentioned technique effect of realization, technical solution proposed by the present invention is：
A method of the number of retransmissions dynamic based on the triggering of real-time packet loss information adjusts, and is sensed by packet loss event-driven
Device adjustment is sent to the number of retransmissions of the data packet of remote status estimator, including step：
(1) linear time invariant system is built：
xk+1=Axk+wk
yk=Cxk+vk
Wherein, xkFor the state variable at k moment, ykFor the state measurement data at k moment, wkAnd vkIt is respectively independent mutually
System noise and measurement noise, A and C are respectively the state matrix and output matrix of system；
(2) state variable and state measurement data of sensor acquisition each time slot of linear time invariant system, to collected
Data carry out Kalman filtering, obtain the optimal State Estimation value of sensor sideWith error covarianceAnd by optimum state
Estimated valueIt is sent to remote status estimator as data packet；
(3) the error covariance P of long-range estimator is calculatedk：
Wherein,ForConvergency value, γkRefer to judge whether in time slot k data packet reaches the judgement of long-range estimator
Mark, γk=1 expression data packet successfully arrives at, γk=0 expression data packet does not reach；A ' shows that the transposition of A, Q indicate system noise wk
Variance matrix；
(4) setting retransmits desirability index：
Wherein, ZkIndicate that the re-transmission desirability of time slot k, ε indicate to retransmit the variation function of desirability index, the table of ε
It is up to formula：
In formula, f (ε) is the function about ε, Δ PkFor PkWithDifference, i.e.,
(5) threshold value h is increased according to re-transmission demand parameter settingsWith reduction threshold value hl, calculate the data in the transmission of each time slot
The number of retransmissions of packet：
Wherein, RkIndicate the number of retransmissions of corresponding data packet when time slot k, and when k=1, Rk=0；It is system at one
The maximum retransmission of time slot；δkExpression event trigger sequence,
Further, the optimal State Estimation value of the sensor sideWith error covarianceExpression formula be respectively：
Wherein, E expressions ask it expectation, Y to indicate that time slot 1 arrives the measurement data packet set of time slot K,
Y={ y1,K,yK}。
Further, described that threshold value h is increased according to re-transmission demand parameter settingsWith reduction threshold value hlMethod be：
1) S is definedkIndicate the data packet transmission state of time slot k, Sk=(γk,Zk,Rk)；
2) Markov chain method S is crossedkState-transition matrix, and find out SkSteady-state distribution:
Obtain the average transmission energy needed for time slot k transmission corresponding data packets
Amount；Wherein, π0,π1,Kπms-1The steady-state distribution probability value of each state is indicated respectively.
3) it under conditions of sensor node finite energy, to minimize bursts dropping ratio as target problem, solves most
Excellent increase threshold value hsWith reduction threshold value hl。
Advantageous effect：Compared with prior art, the present invention has the advantage that：
1, the present invention reduces transmission and establishes answering for process without allocating number of retransmissions in advance to each data packet
Miscellaneous degree；
2, it is directed to different channels noise type, can reach information according to real time data packet adjusts number of retransmissions, does not depend on
It is flexible and convenient to use in noise model；
3, according to the different number of retransmissions upper limits, the increase and decrease activation threshold value of the variation function and number of retransmissions of adjustable index
The stability for taking into account transfer point limited resources and long-range estimation performance is conducive to close to the excellent of the long-range estimation performance of truly analysis
It is bad.
Description of the drawings
Fig. 1 is the flow chart of the method for the number of retransmissions dynamic adjustment of the present invention triggered based on real-time packet loss information；
Fig. 2 is the schematic diagram that error covariance changes after data packetloss in the present invention；
Fig. 3 is to retransmit desirability triggering in the present invention to increase threshold value hsWith reduction threshold value hlProcess schematic；
Fig. 4 is the schematic diagram of activation threshold value selection and algorithm effect when maximum retransmission is 3 in embodiment；Wherein scheme
4 (a) is corresponding packet loss under different activation threshold values, optimal activation threshold value may be selected under given energy limit, Fig. 4 (b) is
The packet loss comparative situation of three kinds of algorithms of different.
Fig. 5 is the schematic diagram of activation threshold value selection and algorithm effect when maximum retransmission is 5 in embodiment；Wherein scheme
5 (a) is corresponding packet loss under different activation threshold values, optimal activation threshold value may be selected under given energy limit, Fig. 5 (b) is
The packet loss comparative situation of three kinds of algorithms of different.
Specific implementation mode
The present invention is further described below in conjunction with the accompanying drawings.
The present invention proposes a kind of method of the number of retransmissions dynamic adjustment triggered based on real-time packet loss information, and flow is as schemed
Shown in 1, include the following steps：
Step S101 acquires the state variable of its different moments as transmission number to some discrete linear time-invariant system
According to packet.
In this example, using following linear time invariant system：
xk+1=Axk+wk
yk=Cxk+vk
Wherein, xkFor the state variable at k moment, xk∈Rn；ykFor the state measurement data at k moment, yk∈Rn；wkAnd vkPoint
Not Wei independent system noise and measurement noise mutually, wk∈Rn, vk∈Rn, wkAnd vkVariance matrix be respectively Q and R, wherein
RnSet of real numbers is tieed up for n；A and C is respectively the state matrix and output matrix of system.
Communication network is established in sensor and long-range estimator, is used for transmission the measurement data packet from time slot 1 to time slot K
Y={ y1,K,yK, the data packet set that long-range estimator receives can use M={ m1,K mkL,mKIndicate, mkFor long-range estimator
In the data packet that time slot k is received.
Clearly as the unstability of communication, Y and M will will produce difference.Now, γ is definedkJudge as index
Whether data packet reaches long-range estimator, γ when time slot kk=1 indicates that data packet reaches when time slot k, γkWhen=0 expression time slot k
Data packet does not reach.Sensor local state estimation as a result,With its error covarianceIt can be calculated with following formula：
And the estimated value of remote status estimatorWith its error covariance PkIt can be calculated with following formula：
Step S102 is transferred to long-range estimator using Kalman filtering to being collected into after data are handled.
Classical Kalman filtering algorithmic formula is as follows：
Wherein, it includes two parts：Predicted portions and measurement updaue.The first two formula shows prediction process, rear three public affairs
Formula is modified and updates to measurement result using prediction result.In the present system, it is assumed that sensor has certain computing capability,
Therefore sensor shape optimal State Estimation can be obtained in sensor local runtime Kalman filteringWith error covarianceIt examines
The presence for considering measurement noise, by optimal State EstimationLong-range estimator is sent to than sending measured value y from sensorkMore close
It fits, thus the state estimation of long-range estimatorWith error covariance PkIt can be described as：
Wherein, when transmission success, the state estimation of long-range estimatorWith error covariance PkAs sensor shape is optimal
State estimationWith error covarianceIf not reaching smoothly, according to the state estimation of last momentWith error covariance
Pk-1It is iterated.
It is known to work as initial error covarianceWhen,It will converge to rapidlyThis explanation,It may be used to indicate number
Whether smoothly reached in time slot k according to packet.It is indicated to simplify, the error covariance of long-range estimator may be expressed as：
Step S103, design retransmit the index of desirability, and independently select the variation function of the index to adjust index
Increase and decrease width.
By previous step it is found that if data packet reaches long-range estimator, the error covariance of long-range estimator will be received rapidly
Hold back toOtherwise PkIt can be according to Pk-1Its value of iteration.Therefore we introduceAs one reflection data packet whether when
Between k lose real-time change standard.
Fig. 2 show the schematic diagram that error covariance changes after data packetloss, if data packet in time slot k Successful transmissions,
ΔPk=0, once conversely, data-bag lost, then Δ Pk> 0.Obviously, Δ PkIt will increased dramatically when data packet is continuously lost.It is existing
It introduces each data packet and is given at it and possess certain number of retransmissions in time slot and increase the probability of its data packet Successful transmissions.According to
ΔPkChanging value, we design retransmit desirability index variation function of ε it is as follows：
Wherein, f (ε) is the function about ε, can design different variation functions according to demand.On this basis, design weight
Pass the index Z of desirabilityk, ZkValue is bigger, more needs to increase number of retransmissions.In ZkUnder conditions of initial value is 0, it can be used to down
Combine the formula describing Z of variation function of εk：
It can be obtained by the formula, ZkValue depend on data packet time slot k arrivals situation and upper primary re-transmission demand journey
Spend index Zk-1。
Step S104, for continually changing desirability index, setting increases threshold value hsReduce threshold value h with reversedl.Tool
Body process can be it can be seen from Fig. 3 that be more than to increase threshold value h when retransmitting desirability indexsWhen, number of retransmissions is being less than maximum re-transmission
NumberWhen, event trigger sequence adds (a δk=1), conversely, being more than reversed reduction threshold value h when retransmitting desirability indexlWhen,
Event trigger sequence subtracts one (δk=-1).Event trigger sequence δkFormula be expressed as follows：
Whether time slot k data are reached index γ by step S105k, retransmit the index Z of desirabilitykWith current re-transmission number
RkIt is integrated into state Sk=(γk,Zk,Rk), which determines the variation of subsequent time activation threshold value, specially：
Pass through Markov chain method SkState-transition matrix, and find out SkSteady-state distribution, obtain time slot k transmit
Average transmission energy needed for corresponding data packet；SkSteady-state distribution formula be：
Wherein, π0,π1,Kπms-1The steady-state distribution probability value of each state is indicated respectively.
Under conditions of sensor node finite energy, to minimize bursts dropping ratio as target problem, solve optimal
Increase threshold value hsWith reduction threshold value hl。
Updated activation threshold value is applied to the adjustment of number of retransmissions, for data transmission next time after update.It retransmits
Number more new formula is expressed as follows：
Wherein, RkIndicate the number of retransmissions of corresponding data packet when time slot k, and when k=1, Rk=0.
So far, complete algorithm cycle terminates, and data can be substantially reduced in more data transmissions by continuous iteration
Loss rate.
Scheme of the present invention is described further below by embodiment.
Fig. 4 and Fig. 5 are the present invention in different maximum retransmissionsSelection under, to increase and decrease threshold value selection and its effect
The experimental result picture of fruit.
Fig. 4 is the schematic diagram of activation threshold value selection and algorithm effect when maximum retransmission is 3.As shown in Fig. 4 (a),
In maximum retransmissionWhen being 3, pass through Markov chain method state Sk=(γk,Zk,Rk) state-transition matrix, and ask
Go out steady-state distributionObtain required average transmission energy.It is limited in node energy
Under the conditions of (be set as 11 herein), the threshold value (h herein of Loss Rate can effectively be reduced by choosing it mostl=-4).Inventive algorithm and its
Shown in comparison figure such as Fig. 4 (b) of his algorithm, inventive algorithm is transmitted compared to without intervention, the method for distributing number of retransmissions in advance,
Data packet transmission information can not only be obtained in real time and is used, moreover it is possible to effectively be reduced Loss Rate, be increased the performance remotely estimated.
Fig. 5 is the schematic diagram of activation threshold value selection and algorithm effect when maximum retransmission is 5.Such as Fig. 5 (a), most
Big number of retransmissionsWhen being 5, pass through Markov chain method state Sk=(γk,Zk,Rk) state-transition matrix, and find out steady
State is distributedObtain required average transmission energy.In the limited condition of node energy
Under (be set as 11 herein), the threshold value (h herein of Loss Rate can effectively be reduced by choosing it mostl=-2).Fig. 5 (b) show the present invention
Compared to without transmission is intervened, the method for distributing number of retransmissions in advance can not only obtain data packet transmission information and add algorithm in real time
To utilize, moreover it is possible to effectively reduce Loss Rate, increase the performance remotely estimated.
Above-described embodiment can absolutely prove that the present invention introduces the re-transmission triggered based on event for remote status estimation procedure
Algorithm only need to be adapted dynamically number of retransmissions, the feasibility of experiment display algorithm structure according to real time data Loss Rate, it was demonstrated that
The reliability of method proposed by the present invention.
The above is only a preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of method of the number of retransmissions dynamic adjustment based on the triggering of real-time packet loss information, which is characterized in that pass through packet loss thing
Part driving sensor adjustment is sent to the number of retransmissions of the data packet of remote status estimator, including step：
(1) linear time invariant system is built：
xk+1=Axk+wk
yk=Cxk+vk
Wherein, xkFor the state variable at k moment, ykFor the state measurement data at k moment, wkAnd vkRespectively mutual independent system
Noise and measurement noise, A and C are respectively the state matrix and output matrix of system；
(2) state variable and state measurement data of sensor acquisition each time slot of linear time invariant system, to collected data
Kalman filtering is carried out, the optimal State Estimation value of sensor side is obtainedWith error covarianceAnd by optimal State Estimation
ValueIt is sent to remote status estimator as data packet；
(3) the error covariance P of long-range estimator is calculatedk：
Wherein,ForConvergency value, γkRefer to judge whether when time slot k is transmitted data packet reaches the judgement of long-range estimator
Mark, γk=1 expression data packet successfully arrives at, γk=0 expression data packet does not reach；The transposition of A ' expressions A, Q indicate system noise
wkVariance matrix；
(4) setting retransmits desirability index：
Wherein, ZkIndicate that the re-transmission desirability of time slot k, ε indicate to retransmit the variation function of desirability index, the expression formula of ε
For：
In formula, f (ε) is the function about ε, Δ PkFor PkWithDifference, i.e.,
(5) threshold value h is increased according to re-transmission demand parameter settingsWith reduction threshold value hl, calculate the data packet in the transmission of each time slot
Number of retransmissions：
Wherein, RkIndicate the number of retransmissions of corresponding data packet when time slot k, and when k=1, Rk=0；It is system in a time slot
Maximum retransmission；δkExpression event trigger sequence,
2. a kind of method of number of retransmissions dynamic adjustment based on the triggering of real-time packet loss information according to claim 1,
It is characterized in that, the optimal State Estimation value of the sensor sideWith error covarianceExpression formula be respectively：
Wherein, E expressions ask it expectation, Y to indicate that time slot 1 arrives the measurement data packet set of time slot K, Y={ y1,K,yK}。
3. a kind of method of number of retransmissions dynamic adjustment based on the triggering of real-time packet loss information according to claim 2,
It is characterized in that, it is described that threshold value h is increased according to re-transmission demand parameter settingsWith reduction threshold value hlMethod be：
1) S is definedkIndicate the data packet transmission state of time slot k, Sk=(γk,Zk,Rk)；
2) Markov chain method S is crossedkState-transition matrix, and find out SkSteady-state distribution:
Obtain the average transmission energy needed for time slot k transmission corresponding data packets；
Wherein, π0,π1,Kπms-1The steady-state distribution probability value of each state is indicated respectively.
3) it under conditions of sensor node finite energy, to minimize bursts dropping ratio as target problem, solves optimal
Increase threshold value hsWith reduction threshold value hl。
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| CN110366232A (en) * | 2019-06-19 | 2019-10-22 | 东南大学 | Sensor transmissions energy control method for remote status estimation |
| CN111935556A (en) * | 2020-06-29 | 2020-11-13 | 王柳渝 | Big data wireless network transmission method and system of online education platform |
| CN115276917A (en) * | 2022-07-29 | 2022-11-01 | 东北大学 | Remote state estimation transmission control method using historical information |
| CN116527060A (en) * | 2023-05-29 | 2023-08-01 | 北京理工大学 | Information compression and anomaly detection method based on event trigger sampling |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110096416A (en) * | 2019-03-13 | 2019-08-06 | 中国平安人寿保险股份有限公司 | Abnormal alarm method, apparatus, computer installation and readable storage medium storing program for executing |
| CN110366232A (en) * | 2019-06-19 | 2019-10-22 | 东南大学 | Sensor transmissions energy control method for remote status estimation |
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| CN111935556A (en) * | 2020-06-29 | 2020-11-13 | 王柳渝 | Big data wireless network transmission method and system of online education platform |
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| CN115276917A (en) * | 2022-07-29 | 2022-11-01 | 东北大学 | Remote state estimation transmission control method using historical information |
| CN115276917B (en) * | 2022-07-29 | 2023-06-20 | 东北大学 | Remote state estimation transmission control method utilizing historical information |
| CN116527060A (en) * | 2023-05-29 | 2023-08-01 | 北京理工大学 | Information compression and anomaly detection method based on event trigger sampling |
| CN116527060B (en) * | 2023-05-29 | 2024-01-05 | 北京理工大学 | Information compression and anomaly detection method based on event trigger sampling |
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Application publication date: 20181106 |