CN110309686A - It is a kind of based on etc. region divisions RFID anti-collision algorithm - Google Patents
It is a kind of based on etc. region divisions RFID anti-collision algorithm Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
- G06K7/10009—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves
- G06K7/10019—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers.
- G06K7/10029—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the time domain, e.g. using binary tree search or RFID responses allocated to a random time slot
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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
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The present invention relates to a kind of RFID anti-collision algorithms.It is especially a kind of based on etc. region divisions RFID anti-collision algorithm.Increase the low problem of the throughput in the problem and communication process of brought system stability difference mainly for label in current extensive label application scenarios.It is proposed it is a kind of based on etc. region divisions RFID anti-collision algorithm (BEAD), including the group areas structure such as a kind of and a kind of optimal timeslot number optimization algorithm, the algorithm establishes a kind of novel packet configuration by carrying out impartial region division to the label in reader identification range, and it uses dynamic prediction weights estimation number of tags respectively in each group, it is combined with optimal timeslot number Adjusted Option and then quickly identification is carried out efficiently to label.
Description
Technical field
The present invention relates to a kind of RFID anti-collision algorithms.It is especially a kind of based on etc. region divisions RFID anti-collision algorithm
(An RFID anti-collision algorithm based on equal area division,BEAD).The algorithm is logical
It crosses and a kind of novel packet configuration is established to the impartial region division of label progress in reader identification range, and at each group
It is middle to use dynamic prediction weights estimation number of tags respectively, it is combined in turn with optimal timeslot number Adjusted Option to label
Carry out efficiently quickly identification.
Background technique
Radio frequency identification (Radio Frequency Identification RFID) technology is as thing network sensing layer
One of core technology, be widely used every field.When existing simultaneously multiple readers or label in RFID system
When, the matching that will cause between label and reader that interferes with each other between radio signal causes confusion, collision problem is generated,
The following low problem of system throughput has seriously affected the working efficiency of RFID identification system.
The multi-tag RFID anti-collision algorithm of current main-stream mainly includes randomness algorithm based on time-division multiplex class and really
Deterministic algorithm two major classes, wherein Aloha class algorithm is the representative of randomness algorithm, and deterministic algorithm is then mostly derived from tree search
Algorithm.Compared to ALOHA class algorithm, although tree search algorithm can guarantee tag recognition rate, system identification week is increased
Phase improves the complexity of system design.Basic Aloha class algorithm include pure Aloha algorithm, FSA algorithm, DFSA algorithm and
GDFSA algorithm etc..One frame is divided into multiple time slots by FSA algorithm, and defines label in each frame only in response to primary.Work as label
After colliding, label, which is delayed to next frame, to be continued to transmit data.But the algorithm stability is poor.GDFSA algorithm will dynamic
Principle and labeled packet method are in conjunction with improving RFID anti-collision algorithm several times for adjustment time slot, but the recognition efficiency of the algorithm is not
It is effectively improved.
As it can be seen that existing RFID anti-collision algorithm is difficult to meet the efficient identification to extensive RFID label tag, and its time slot
Utilization rate and system stability are relatively low, this proposes higher want to the label anti-collision algorithm of extensive RFID application scenarios
It asks.
Summary of the invention
The technical problem to be solved by the present invention is to increase to be brought for label in extensive label application scenarios at present
System stability difference problem and the low problem of throughput in communication process.Propose it is a kind of based on etc. region divisions
RFID anti-collision algorithm (BEAD) include the group areas structure such as a kind of and a kind of optimal timeslot number optimization algorithm, wait regions point
Label in reader identification range is carried out impartial region division to establish a kind of novel packet configuration, according to mark by group structure
Label quantity number reader identification range is divided into 1 Dao N number of group.Optimal timeslot number optimization algorithm is by calculating DFSAC-II
Method improves to keep every estimation for taking turns remaining number of tags more accurate after obtaining dynamic prediction weight, and according to number of tags tune
Whole optimal timeslot number preferably solves multi-tag collision problem in turn.
The group areas structure such as described is since reader periphery label distribution is different, and RFID system has label
Number is smaller, the higher characteristic of system identification efficiency, according to etc. the thought of region divisions be according to the quantity of label by reader
All labels in identification range are grouped, and each group are divided into the fan-shaped region of area equation, by dispersing label
Number, and every group of label is separately identified to achieve the purpose that improve system throughput.
The system throughput of current RFID anti-collision algorithm depends on number of tags and this two big key factor of timeslot number,
When the ratio of unidentified number of tags and timeslot number is bigger, at this time since collision time slot increases, system throughput can be lower；When not
Identify that the ratio of number of tags and free timeslot is smaller, system throughput also decreases.
Reader successfully identifies the ratio of label Yu total timeslot number in a system throughput i.e. frame, if institute is sometimes
Gap is the label recognition failures due to collision of free timeslot or all responses, then throughput becomes minimum value 0.This
Outside, in a recognition cycle, if all labels are all successfully identified, throughput reaches maximum 1.
The optimal timeslot number optimization algorithm is to be limited for label cost by hardware condition, and timeslot number cannot divide wantonly
It in the case where matching, needs to carry out optimal group to label on the basis of label is estimated, and dynamic adjustment time slot on this basis
Number is to further increase system throughput.Due in multi-tag identification process, when number of tags is equal to frame length, ideal situation
Lower system can reach maximum throughput rate, but actual number of tags is unknown for reader, therefore label estimation is to be directed to
Reader needs dynamically to estimate in multi-tag identification process remaining number of tags, and optimal frame length is arranged for it, so as to reality
Existing maximum throughput rate.For accurate estimation label number, optimization, rudimentary algorithm stream are made to DFSAC-II label algorithm for estimating
Cheng Shi；Total number of labels to be identified is assumed that first and collides the functional relation expression formula between timeslot number, is followed by used
DFSAC-II algorithm adjusts the functional relation expression formula, obtains dynamic prediction weight k, finally comes according to dynamic prediction weight k pre-
Survey the unidentified number of tags of next round.
Detailed description of the invention
Fig. 1 is the group areas structure chart such as of the invention
Fig. 2 is system throughput figure under optimal timeslot number of the invention
Fig. 3 is timeslot number and packet count corresponding relationship of the invention
Fig. 4 is the specific flow chart of BEAD algorithm of the invention
Fig. 5 is total timeslot number comparison diagram before and after optimization algorithm of the invention
Fig. 6 is system throughput comparison diagram before and after optimization algorithm of the invention
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawing, the present invention it is a kind of based on etc. region divisions RFID
Anti-collision algorithm (BEAD) includes the group areas structure such as a kind of and a kind of optimal timeslot number optimization algorithm, etc. group areas structure
A kind of novel packet configuration is established by the way that the label in reader identification range is carried out impartial region division, according to label
Reader identification range is divided into 1 Dao N number of group by the number of quantity.Optimal timeslot number optimization algorithm passes through to DFSAC-II algorithm
It improves to keep every estimation for taking turns remaining number of tags more accurate after obtaining dynamic prediction weight, and is adjusted according to number of tags
Optimal timeslot number carries out efficiently quickly identification to label in turn.
The group areas structure such as described is since reader periphery label distribution is different, and RFID system has label
Number is smaller, the higher characteristic of system identification efficiency, thus according to etc. region divisions thought by the institute in reader identification range
There is label to be grouped, achievees the purpose that improve system identification efficiency by dispersion number of tags.Etc. group areas structure chart
As shown in Figure 1.In a wireless communication system, channel changes over time, therefore needs to consider distance between label and reader
Caused by path loss.In wireless channel, the logarithm of the distance between mean receiving power (dBm) and transmitter and receiver
It is inversely proportional, therefore sets the mean receiving power of system as Pr。
In above formula, if reader identification range is d；N is path fading index, i.e., with the speed being lost apart from Growth Route
Degree, general value is between 2~5；d0For near-earth reference distance.
Optimization is made on the basis of DFSAC-II label algorithm for estimating.Provide successfully timeslot number, free timeslot number and
Colliding timeslot number is respectively Tg、Tl、Te.It is inquired assuming that being taken turns by n, total number of labels to be identified and collision timeslot number have linear letter
Number relationship.Then the (n+1)th wheel number of tags predictor formula is as follows:
mpredict(n+1)=kTe(n) (2)
Correspondingly, calculating the to be estimated of present frame using DFSAC-II algorithm after the (n+1)th wheel number of tags estimation starts
Number of tags is adjusted the prediction result of the n-th wheel:
madjust(n+1)=2.3922Te(n+1) (3)
Theoretical error value then can be obtained with the adjustment result that the prediction result of the n-th wheel subtracts the (n+1)th wheel:
ε=mpredict(n+1)-madjust(n+1)=kTe(n)-2.3922Te(n+1) (4)
The smallest error in order to obtain, by square obtaining minimum about k derivation to theoretical error value:
Enabling above formula is 0, and the dynamic prediction weight for solving label estimation is as follows:
Since algorithm can not accomplish the collision timeslot number for obtaining next round in advance in practical implementation, so by formula
It is modified accordingly, dynamic prediction can be obtained by the way that the timeslot number of epicycle and last round of timeslot number are substituted into above-mentioned formula
Weight k, is specifically expressed as follows:
K is substituted into formula (2), next round prediction label number can be obtained are as follows:
The system throughput of current RFID anti-collision algorithm depends on number of tags and this two big key factor of timeslot number,
When the ratio of unidentified number of tags and timeslot number is bigger, at this time since collision time slot increases, system throughput can be lower；When not
Identify that the ratio of number of tags and free timeslot is smaller, system throughput also decreases.So for optimization system throughput,
It analyzes and has carried out certain optimization to it on the basis of existing label algorithm for estimating.
One frame is divided into T time slot by BEAD algorithm regulation, i.e., timeslot number is T, while being provided in reader identification range
Total number of labels to be identified be m.Since the process that label carries out selection to time slot is a process of irrelevant independent choice,
It is unrelated with other labels, therefore the match cognization process of label and time slot can be regarded as a multiple Bernoulli trials, it is converted
For the mathematical problem of a bi-distribution.Specific probability is expressed as follows:
K ∈ [0, m] and k rounding in formula.
K=1 is taken, then can show successfully that the probability of time slot, i.e. only one label of time slot select general according to above formula
Rate is as follows:
It can similarly obtain, as k=0, the probability of free timeslot, i.e. a time slot do not have the probability of any label selection such as
Under:
Then as k >=2, the probability of time slot is collided, i.e. a time slot has the probability of multiple label simultaneous selections can be by general
The principle that rate summation is 1 is expressed as follows:
Pe=P (X >=2)=1-P (X=1)-P (X=0) (12)
In an ideal case, it is specified that the expectation of success time slot, free timeslot expectation and collision time slot expectation are used respectively It indicates, mathematic(al) representation difference is as follows:
Regulation system throughput is SRFID, i.e. reader successfully identifies the ratio of label Yu total timeslot number, formula in a frame
It is as follows:
Formula (16) derivation can be obtained:
The functional relation for asking extreme value that timeslot number and number of tags can be obtained above formula is as follows:
Above formula shows to be approximately equal to number of tags when the assignable timeslot number of system, and when number of tags is far longer than 1, system throughput
Rate reaches extreme value.In addition, if the label that all time slots are free timeslot or all responses identifies mistake due to collision
It loses, then throughput becomes minimum value 0.In addition, if all labels are all successfully identified, handling up in a recognition cycle
Rate reaches maximum 1.
Since label cost is limited by hardware condition, timeslot number cannot distribute wantonly, so the base for needing to estimate in label
Optimal group is carried out to label on plinth, and dynamic adjusts timeslot number to further increase system throughput on this basis.Usually
In the case of, timeslot number value is usually about 2 in RFID systemxFunction, and due to being limited by hardware cost, timeslot number is maximum
Take 28, more than will lose practical application value, therefore the value of timeslot number is [22,23,24,25,26,27,28] manifold.Fig. 2 is difference
Under fixed timeslot number, number of tags increases the effect tendency changed to system throughput, it can be seen that two adjacent timeslot numbers
Intersection point is the critical point that number of tags influences system throughput variation, and adjacent two fixed timeslot numbers, which are substituted into formula (8), can obtain time slot
The corresponding number of labels of number, further acquires grouping critical point.The throughput relationship of adjacent two timeslot number is expressed as follows:
From T=22Starting gradually to substitute into entire manifold into formula (19) can obtain:
For example, working as T=22When, m ≈ 5.5 takes m=5, then 5 be when timeslot number is 22When maximum number of tags.Specific grouping
Situation is as shown in Figure 3.
Fig. 4 is BEAD algorithm flow chart, and the specific identification process of algorithm is as follows:
(1) before identification starts, total number of labels is counted, when total number of labels is greater than 354, is covered in reader identification power
The group areas such as carry out within the scope of lid, by by reader identification range be divided into 1 to N number of area equation it is fan-shaped and then reach
The purpose that label is tentatively grouped.
(2) since the 1st group, initial slot number is set, group interior label initialization randomly chooses a time slot and to reading
Device sends information.
(3) system judges the time slot state of each frame according to the acquired label selection information of epicycle.And according to time slot
Selection situation using remaining number of tags in the current group of improved DFSAC-II algorithm estimation.
(4) by acquired epicycle number of tags information, when system makes optimal correction and distributes next round to timeslot number
Gap number.
(5) reader sends sleep command to the label that success identifies, it is made to exit epicycle query process.Collision labels are prolonged
Restart identification process to next frame late, until this group of label whole suspend mode.
(6) after the identification of this group, group number adds 1, and return step (2) starts the identification process of a new round and using identical
Mode is identified that after the completion of all groups of tag recognition within the scope of system identification, algorithm terminates.
The grouping and identification to label can be completed according to above-mentioned basic step.
In order to verify the validity of BEAD algorithm of the present invention, pass through the reliability of experimental analysis algorithm.And by BEAD algorithm
Compared with FSA_256 algorithm and DFSA algorithm carry out performance, using during throughput and tag recognition system consumption it is total when
Evaluation index of the gap number as evaluation performance superiority and inferiority.Assuming that label is evenly distributed, system label number is set as 1500, BEAD calculation
Method, FSA_256 algorithm and DFSA algorithm initial slot number are disposed as 256.In order to increase the authority of test result, mark is allowed
Number is signed since 50 ing, until total number of labels increases to 1500, record change every time under simulation result, and to each simulation result
It is averaged.
Etc. the specific group technology of group areas structure be expressed as follows:
N=down (m/354) (21)
Wherein N is packet count, and m is total number of labels, and down is to be rounded symbol downwards.In this experiment, packet count constantly becomes
Change, when total number of labels is 354, N=1；When total number of labels reaches maximum 1500, N takes 4.
It is illustrated in figure 5 the total timeslot number comparison of system, it can be seen that when number of tags 850, FSA_256 algorithm
And total number of timeslots consumed by DFSA algorithm with number of labels increase exponentially increase, this is because 256 timeslot number
It is too short, caused by causing collision frequency to increase, when total number of labels reaches 1500, FSA_256 algorithm and DFSA algorithm institute
The total number of timeslots of consumption respectively reaches 21248 and 20520.And BEAD algorithm performance is substantially better than FSA_256 algorithm and DFSA
Algorithm, required timeslot number increases slowly, linear growth trend, and when number of tags is 1500, required total timeslot number is 4935, with
FSA_256 algorithm, which is compared, reduces 76.77%.75.96% is reduced compared to DFSA algorithm.This is because BEAD algorithm according to when
Gap number and number of tags grouping relationship have carried out reasonable grouping to total number of labels, so that every group of label maximum recognized sum is no more than
354, the increase of collision time slot is effectively reduced, the RFID system recognition time under extensive label application scenarios is saved.
It is illustrated in figure 6 system throughput comparison, as can be seen from the comparison result, the throughput of system of FSA_256 algorithm
Minimum, when number of tags is 650 or so, throughput reaches maximum 28.21%.The throughput of system of DFSA algorithm has obtained accordingly
Raising, number of tags be 300 or so when, maximum throughput rate reaches 37.88%.BEAD algorithm compared to FSA_256 algorithm and
DFSA algorithm has different degrees of improvement.Reach maximum throughput rate 43.95% when number of tags is 400 or so.This be by
In the used Dynamic Weights estimation technique in combination with the collision timeslot number of the collision timeslot number and previous frame of present frame, every
Wheel timeslot number dynamically changes forecast power after updating, so that number of tags estimation is more reasonable.When system number of labels to be identified is super
When 850, the system performance of FSA_256 algorithm and DFSA algorithm is close, and gradually decreases.And the system throughput of BEAD algorithm
Still stable 30% or more.
It can be seen that the present invention it is a kind of based on etc. region divisions RFID anti-collision algorithm (BEAD), for extensive label application
The problems such as RFID identification system time slot collision rate is excessively high under scene, improves system throughput, reduces in communication process and consume
Total timeslot number, improve label increase brought by system stability difference problem, the development to thing network sensing layer technology
With certain reference.
Claims (5)
1. it is a kind of based on etc. region divisions RFID anti-collision algorithm, it is characterised in that: the present invention includes the group areas such as a kind of
Structure and a kind of optimal timeslot number optimization algorithm, etc. group areas structure the label in reader identification range is subjected to impartial area
Domain divides to establish a kind of novel packet configuration, according to number of labels number reader identification range is divided into 1 to N number of
Group.Optimal timeslot number optimization algorithm makes the remaining mark of every wheel after obtaining dynamic prediction weight by improving to DFSAC-II algorithm
The estimation for signing number is more accurate, and adjusts optimal timeslot number according to number of tags and then efficiently quickly know to label
Not.
2. it is according to claim 1 it is a kind of based on etc. region divisions RFID anti-collision algorithm, it is characterised in that: it is described
Etc. group areas structure due to reader periphery label distribution it is different, and RFID system has number of tags smaller, therefore root
According to etc. the thought of region divisions all labels in reader identification range are grouped, reached by dispersion number of tags
Improve the purpose of system identification efficiency.In a wireless communication system, channel changes over time, therefore needs to consider label and read
Read path loss caused by distance between device.In wireless channel, between mean receiving power (dBm) and transmitter and receiver
The logarithm of distance be inversely proportional, therefore set the mean receiving power of system as Pr。
In above formula, if reader identification range is d；N is path fading index, i.e., with the speed being lost apart from Growth Route,
General value is between 2~5；d0For near-earth reference distance.
Regulation success timeslot number, free timeslot number and collision timeslot number are respectively Tg、Tl、Te.It is inquired assuming that being taken turns by n, wait know
Distinguishing label sum and collision timeslot number have linear functional relation.Then the (n+1)th wheel number of tags predictor formula is as follows:
mpredict(n+1)=kTe(n)
Correspondingly, the label to be estimated of present frame is calculated using DFSAC-II algorithm after the (n+1)th wheel number of tags estimation starts
Number is adjusted the prediction result of the n-th wheel:
madjust(n+1)=2.3922Te(n+1)
Theoretical error value then can be obtained with the adjustment result that the prediction result of the n-th wheel subtracts the (n+1)th wheel:
ε=mpredict(n+1)-madjust(n+1)=kTe(n)-2.3922Te(n+1)
The smallest error in order to obtain, by square obtaining minimum about k derivation to theoretical error value:
Enabling above formula is 0, and the dynamic prediction weight for solving label estimation is as follows:
Since algorithm can not accomplish the collision timeslot number for obtaining next round in advance in practical implementation, so formula is carried out
Corresponding modification can obtain dynamic prediction weight by the way that the timeslot number of epicycle and last round of timeslot number are substituted into above-mentioned formula
K is specifically expressed as follows:
K is substituted into formula (2), next round prediction label number can be obtained are as follows:
The system throughput of current RFID anti-collision algorithm depends on number of tags and this two big key factor of timeslot number, when not
When identifying that the ratio of number of tags and timeslot number is bigger, at this time since collision time slot increases, system throughput can be lower；When unidentified
The ratio of number of tags and free timeslot is smaller, and system throughput also decreases.
3. it is according to claim 1 it is a kind of based on etc. region divisions RFID anti-collision algorithm, it is characterised in that: it is described
One frame is divided into T time slot by BEAD algorithm regulation, while providing that the total number of labels to be identified in reader identification range is m.
It is unrelated with other labels since the process that label carries out selection to time slot is a process of irrelevant independent choice, therefore can
Regard the match cognization process of label and time slot as a multiple Bernoulli trials, is translated into the mathematics of a bi-distribution
Problem.Specific probability is expressed as follows:
K ∈ [0, m] and k rounding in formula.
K=1 is taken, then can obtain successfully the probability of time slot according to above formula, i.e. the probability of only one label of time slot selection is such as
Under:
It can similarly obtain, as k=0, the probability of free timeslot, i.e. a time slot do not have the probability of any label selection as follows:
Then as k >=2, the probability of time slot is collided, i.e. a time slot has the probability of multiple label simultaneous selections can be total by probability
It is expressed as follows with for 1 principle:
Pe=P (X >=2)=1-P (X=1)-P (X=0)
In an ideal case, it is specified that the expectation of success time slot, free timeslot expectation and collision time slot expectation are used respectively It indicates, mathematic(al) representation difference is as follows:
Regulation system throughput is SRFID, i.e. reader successfully identifies that the ratio of label Yu total timeslot number, formula are as follows in a frame:
Above formula derivation can be obtained:
The functional relation for asking extreme value that timeslot number and number of tags can be obtained above formula is as follows:
Above formula shows to be approximately equal to number of tags when the assignable timeslot number of system, and when number of tags is far longer than 1, system throughput is arrived
Up to extreme value.In addition, if all time slots are the label recognition failures due to collision of free timeslot or all responses,
Then throughput becomes minimum value 0.In addition, if all labels are all successfully identified, throughput reaches in a recognition cycle
To maximum 1.
4. it is according to claim 3 it is a kind of based on etc. region divisions RFID anti-collision algorithm, it is characterised in that: it is described
Label cost is limited by hardware condition, and timeslot number cannot distribute wantonly, thus need label estimation on the basis of to label into
Row optimal group, and dynamic adjusts timeslot number to further increase system throughput on this basis.Under normal conditions, RFID system
Timeslot number value is usually about 2 in systemxFunction, and due to being limited by hardware cost, timeslot number maximum takes 28, it is more than that will lose
Practical application value is gone, therefore the value of timeslot number is [22,23,24,25,26,27,28] manifold.The intersection point of two adjacent timeslot numbers is
Number of tags influences the critical point of system throughput variation, and adjacent two fixed timeslot numbers are substituted into next round prediction label number can
The corresponding number of labels of timeslot number is obtained, grouping critical point is further acquired.The throughput relationship of adjacent two timeslot number is expressed as follows:
From T=22Starting entire manifold gradually substituting into above formula can obtain:
5. it is according to claim 1 it is a kind of based on etc. region divisions RFID anti-collision algorithm, it is characterised in that: it is described
The specific identification process of BEAD algorithm is as follows:
(1) before identification starts, total number of labels is counted, when total number of labels is greater than 354, identifies Power coverage model in reader
Enclose it is interior the group areas such as carry out, by by reader identification range be divided into 1 to N number of area equation it is fan-shaped and then reach to mark
Sign the purpose being tentatively grouped.
(2) since the 1st group, initial slot number is set, group interior label initialization randomly chooses a time slot and sends out to reader
It delivers letters breath.
(3) system judges the time slot state of each frame according to the acquired label selection information of epicycle.And according to the choosing of time slot
Situation is selected using remaining number of tags in the current group of improved DFSAC-II algorithm estimation.
(4) by acquired epicycle number of tags information, system makes optimal correction to timeslot number and distributes next round timeslot number.
(5) reader sends sleep command to the label that success identifies, it is made to exit epicycle query process.Collision labels are delayed to
Next frame restarts identification process, until this group of label whole suspend mode.
(6) after the identification of this group, group number adds 1, and return step (2) starts the identification process of a new round and using same way
It is identified, after the completion of all groups of tag recognition within the scope of system identification, algorithm terminates.
The grouping and identification to label can be completed according to above-mentioned basic step.
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| CN110781700A (en) * | 2019-11-11 | 2020-02-11 | 湖南大学 | RFID multi-reader coordination method |
| CN112506499A (en) * | 2020-12-04 | 2021-03-16 | 合肥工业大学 | Method for automatically arranging measurement software labels |
| CN113810951A (en) * | 2021-08-31 | 2021-12-17 | 河北大学 | LoRaWAN anti-collision method based on sector sharing |
| CN114386444A (en) * | 2021-12-29 | 2022-04-22 | 中电海康集团有限公司 | RFID label anti-collision method and system based on fuzzy collision probability prediction |
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| CN113810951B (en) * | 2021-08-31 | 2023-08-08 | 河北大学 | LoRaWAN anti-collision method based on sector-shaped average division |
| CN114386444A (en) * | 2021-12-29 | 2022-04-22 | 中电海康集团有限公司 | RFID label anti-collision method and system based on fuzzy collision probability prediction |
| CN114386444B (en) * | 2021-12-29 | 2024-02-13 | 中电海康集团有限公司 | RFID tag anti-collision method and system based on fuzzy collision probability prediction |
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