1. Sensing multiple passive tags simultaneously
An Anti-collision Algorithm Using Two-Functioned Estimation for RFID Tags
Sensing multiple passive tags simultaneously

2. Highlights:
Using complementarily two-functioned estimation, an identification method based on the stochastic process.

3. Uses of RFID: Supermarkets (easy)
In the areas such as auto-distribution production line.
Warehouse security moving in and out check.

4. Vs. barcode system RFID can do everything bar codes can and much more.
No line of sight required.
Reprogrammable.
Store data.
Can be used in harsh environments.
Simultaneous tag reading with high accuracy.
Ever reducing costs. Passive tags costing as low as $0.05.
With all the benefits, RFID is a good solution for object identification and tracking.

5. Motivation:
Extremely hazard circumstances such as high temperature production processes.
Chip manufacturing plants which require a completely antiseptic condition.
The ability to identify many tags simultaneously for more advanced applications

6. Rfid system components:
Transceiver (reader): supplies power, reads and writes data to transponders.
Transponder (tag): attached to objects. Active (battery powered) and passive.
Data management subsystem: information linking the physical world.
Application layer software
Middle layer software. E.g Savant.

7. Database management subsystem:
Acts as the central nervous system.
Filtering
Tag lookups
Sends the information to application layer.
EPC: Electronic Product Code.

8. Collision:
If multiple RFID tags are being read synchronously, the radio signals will interfere with each other, and may cancel each other’s communication data.

9. Existing methods: Probabilistic.
Deterministic. E.g. using a bit-wise arbitration model during the identification phase of the target device
A complementarily two-functioned estimation algorithm.

10. Deterministic: Bit-wise arbitration model.
Compares bits in the identifiers for the tags.
Other methods includes binary-tree walking scheme.

11. Probabilistic: Randomly generated times allotted.
Readers response can be slotted or continuous.
Based on Aloha Scheme.

12. Problem Definition for Framed ALOHA:
Reader can broadcast one reading command causing all the tags in the interrogation zone to respond, and then all the tags send back the required data by radio wave.
Such process is called one reading cycle, and corresponding time named one frame time.
Then we divide one frame time into lots of small time slots, so that each tag can occupy one slot time to communicate with the reader without interfering with other tags.

13. Framed Aloha contd. Let sn be the slot number of one frame.
Reading time for one cycle can be chosen based on the sn value.
It is in the increments of 16, 32, 64, 128, and 256.
The tags occupy each slot randomly at each reading cycle ensuring 99% of tags are read in each cycle.
tn is the number of tags <=sn.

14. Example: sn=8=tn. 4 reading cycles. 3 possible cases for a frame.
One tag.
Many tags.
No tags.

15. Binomial Distribution for estimation:
Given certain slot number sn and tags number tn, the number of tags in one slot, denoted by t.
The distribution can be defined as:
We can use this to compute the probability that t numbers of tags are definitely occupied in one slot.

16. Occupation Probability:
Identifying distribution of tags to certain number of slots is a kind of problem called Occupation Probability.
Based on binomial distribution, the expected value of the number of slots that are occupied by t tags is given by:

17. Markov Process:
The whole reading process can also be regarded as a Markov Process.
Number of newly identified tags of each reading cycle only depends on the previously known number of tags during last reading cycle.
We could use the transition matrix of Markov Process to compute a lower bound of the number of reading cycles, which is necessary to identify all tags with a given accuracy level. For further reading you can use the link below. ( )
To know sn and the accuracy-level, by given value of tn, we can easily compute the necessary reading cycles number.

18. Parameters Estimation:
In a lot of identification applications, the exactly tags number is unknown at the beginning.
Parameter estimation functions are used in order to estimate the tags number.
We assume that after each reading cycle, we can check the reading performance by three parameters:
C0: the number of slots that the slot is empty.
C1: the number of slots that the slot is occupied by only one tag.
Ck: the number of slots that the slot is collided by several tags.

19. The First Estimation Function:
Since most of the collision is just between two tags, the first estimation function is easily obtained by: tn = C1 + Ck ∗ 2.

20. Second Estimation Function:
When the number of collision which is caused by many tags but not just two tags is increased, the first function can not still work accurately enough.
we can compute the expected outcome (C0, C1, Ck) using binomial distribution and occupation probability.
(C0, C1,Ck) ← (sn, tn)
function [c0,c1,ck]=getc(sn,tn)
c0=sn*((1-(1/sn))^tn);
c1=tn*((1-(1/sn))^(tn-1));
ck=sn-c0-c1;
end

21. Second Estimation Function CONTD.:
The Chebyshev’s inequality indicates that the outcome of a random experiment involving a random variable is most likely somewhere near the expected outcome value.
The way we use this data, is to compute more expected values of (C0, C1, Ck) by more possible tags number, e.g. tn= 1 to 400.
Next, we make an expected outcome value table.
After each reading cycle, we can compare the experiment result value of (C0, C1, Ck) with the expected outcome value table.
By choosing the value which has the smallest wrong-weight, we can attain the estimated sn, tn value finally.
Another method to use this function is to choose a suitable sn value at the beginning and then compute the variable tn.

22. Some tested results:
c0,c1,ck estimates for tn = 10, 20, 30, 40 and sn = 16, 32, 64, 128.

23. Some tested results CONTD.:
c0,c1,ck estimates for tn = 50, 60, 70 and 80 and sn = 16, 32, 64, 128.

24. Two-Functioned Estimation Algorithm:
We now know two estimation functions, and discussed the methods for using them.
Using them complementarily is a key concern.
The second function is a relatively steady function, we check the accuracy-level of the first function to evaluate its performance according to different values of sn and tn.
The wrong-weights of the first estimation function can be obtained by:

25. Wrong weight Computation:
We see the deficiency if we just use the first estimation function.
The expected result employing the first estimation function.
The wrong-weight computed for accuracy set at 0.95.

26. Two-Functioned Estimation Algorithm CONTD.:
We can choose the second function when the accuracy of the first function falls below 0.95 as seen in the table above.
We can obtain, for when the value of sn is 256, the accuracy-level will fall below after 170 tags by calculations.
After using the estimation functions to compute the tags number, we can adapt the sn value again by the newly tn value for better identifying performance.
We can compute the successful reading slot rate by:

27. Successful reading rates:
We can see that the highest successful rate is usually around which is very close to the maximum throughput of Aloha protocol.
We can figure out the best frame size for each tn value.
Best fit sn for tags tn.

28. Assumptions:
The algorithm assumes that the tags set in the interrogation zone is static.
A set of tags entering the reading field they must stay in that area until all tags have been identified within our expected accuracy-level.
No tags are allowed to either enter or exit during the reading process.
Conditions that the sn value is smaller than 16 or bigger than 256 are not considered.

29. A Typical reading Process:
First, we set the starting sn value to 64, and run a reading cycle to get the performance feedback (C0, C1, Ck).
Next, we use the first estimation function to compute tn value and check its accuracy-level.
If it is a satisfactory result, we use this tn value to choose a best-fit sn size and output the result.
Otherwise, we substitute the first estimation function by the second one and also resize sn by new tn value.

30. A Typical reading Process Contd.:
We use below function to adapt the value of sn by a new tn value.
function [sn]=adaptsn(sn,tn)
if (tn<low(sn)) then sn=sn/2
if (tn>high(sn)) then sn=sn*2
end
For better accuracy, we can also run the estimation process again after adapting best-fit sn value to verify.
sn and tn can be used to compute the time of reading cycles within our desired accuracy- level by regarding the reading process as a Markov Process as discussed previously.

31. A Typical reading Process Contd.:
The state diagram of complementarily two- functioned estimation algorithm.

32. Conclusion:
The paper demonstrated a complementarily two-functioned estimation algorithm for identifying multiple passive RFID tags synchronously.
Two estimation functions for computing the unknown tags number and the best fit slots number presented which may give contribution to other anti-collision methods as well.
An advanced algorithm for identifying passive tags.
With the consolidated mathematical basis and the highly precise level of estimation method it is proved either by formula/ by data, that the method shown in thins paper will work well and provide high-level accuracy in real applications.