1000-RF-tag emulation with an 8bit micro processorNaoyasu Kamiya∗, Jin Mitsugi∗, Kenichi Sugimoto†, Osamu Nakamura∗ and Jun Murai∗

∗ Auto-ID Lab. Japan, Keio University, Japan† Graduate School of Media Design, Keio University, JapanE-mail: {kamiya, mel, mitsugi, osamu, jun}@sfc.wide.ad.jp

Abstract—UHF band passive RFID is an essential identificationtechnology in pervasive computing world. One of the industrialdemands to UHF band passive RFID is fast reading of many RFtags. This is particularly relevant for item-level-tagging of CDs,DVDs, books and consumer electronics. Many high-speed MACprotocols have been proposed thus far. Quantitative evaluationof reading speed, however, is not easy because we need to lay outmany RF tags and measure the reading durations usually throughinterrogator APIs, which differ from interrogator to interrogator.In this paper, the authors propose two efficient multiple RFtag emulation algorithms which leverage the probabilistic natureof MAC protocol. We have implemented the algorithms ontoa programmable battery assisted passive tag (BAP), equippedwith an 8bit micro processor and evaluated the reading speedof commercial interrogators. It was revealed that the BAP canefficiently emulate the behavior of more than 1000 Gen2 RFtags for the reading speed evaluation with a trivial penalty onaccuracy.

I. I NTRODUCTION

Radio Frequency IDentification (RFID) is the quintessentialtechnology for pervasive computing. It has been widely used inmany industries, such as consumer electronics, airline, books,foods and healthcare. Currently, pallet-level-tagging and case-level-tagging in Supply Chain Management (SCM) are thepredominant usage of passive RFID. The item-level-taggingwill be possible in the future when we achieve lower unit pricefor an RF tag, better interrogators and a supporting informationsystem. Industries, consumer electronics in particular, seekindividual item-level-tagging as it aims at long-term manage-ment of their products as in ”from cradle to grave”[1]. Theidea is that RF tag-ID and consumers are uniformly managed,and in case of a product recall for example, the retailercan locate where each item is and who the owner is. Thismechanism also proves helpful in the events of products beingstolen, abandoned and recycled. For item-level-tagging to besubstantiated, it is ultimately preferable for an interrogator tobe able to read all the RF tags (items) in the inventory whichcould be over 1000 RF tags at once[2].

UHF band (860-960MHz) passive RFID is the leadingcandidate for this role as it can accommodate a relativelylong-range automatic identification, probably up to 5 meters.Therefore, the acceleration of RFID inventory process (multi-ple RF tag interrogation) have become active research topics.The aim is to be able to read multiple RF tags faster and moreaccurately in SCM (Fig.1).

When reading mutiple RF tags, anti-collision is crucial.Anti-Collision of RFID is two major classes of binary treemethod and ALOHA method[3]. This paper assumes the latter

Fig. 1. Image of SCM using RFID

Fig. 2. Example of state machine on RF tag.

method, particularly a frame slotted ALOHA method that isoften used on UHF band passive RFID. RF tags correspondanti-collision basis frame slotted ALOHA method to have astate machine. Fig.2 shows a example of state machine.

Recently, different MAC protocols (Anti-Collision algo-rithms) have been researched and developed to optimize RFIDinventory with dynamic frame slotted ALOHA method, analgorithm which allows frame size to be changed dynami-cally depending on the remaining (backlogged) number ofRF tags within the inventory[4][5][6]. At the present stage,most of performance evaluations for MAC protocols are doneby numerical simulations. To actually test and use a MACprotocol, we need to have an interrogator with the MACprotocol read multiple RF tags, measure its inventory time andcompare the result with those of other MAC protocols (Fig.3).This testing method however has two major disadvantages; 1)experimenters had to physically prepare and lay out a numberof actual RF tags for each experiment. 2) it is hard to ensurereproducibility because radio propagation environment variesevery time and it is virtually impossible to place RF tags in thesame precise manner (eg. angle, interval) as before. Besides,

Fig. 3. Experimental image for high-speed reading of multiple RF tags

each RF tag has a slightly different sensitivity and backscatterpower level.

Those problems can be solved by multiple RF tags em-ulator. Although not for RF tags, Wang[7] has developed anupper-layer protocol emulator that emulates multiple SoftwareDefined Radio (SDR) devices in wireless SDR network. Theupper-layer protocol emulation requires the implementationof actual SDR state machines that correspond to the numberof SDR devices that it wants to emulate. As far as RF tagemulation is concerned, there is an emulator[8] that emulatesa single RF tag with power reception. But there hasn’t beenan emulator for multiple RF tags, which is where our researchfocused.

To implement multiple RF tags emulator, one would thinkto apply the same fundamentals of the aforementioned upper-layer protocol emulator. In other words, we implement actualRF tag state machines that correspond to the number ofRF tags we want to emulate. But this requires the emulatorhardware of costly calculation, and is not necessarily effi-cient. So we devised two efficient emulation algorithms[9]for multiple RF tags, using the probabilistic nature of MACprotocol1. Our algorithms require only a single state machinefor multiple RF tags emulation, thus not necessitating costlyhardware calculation, enabling a low-cost hardware to emulatea relatively large number of RF tags. This paper delineatesthe implementation methodology for 1000 RF tags emulationusing a BAP[12] equipped with an 8bit micro processor.

The remainder of this paper is organized as follows. InSection II, the two multiple RF tags emulation algorithmspresented in[9] are outlined for the completeness of the paper.In Section III, after identifying the bottleneck for increasingthe number of emulated RF tags, we propose a creativeimplementation methodology to avoid the bottleneck by pre-calculating the essential part of the emulation. In Section IV,we show by experiments that it is possible to emulate the

1While our algorithms hold applicability to all of ALOHA method, forquantitative evaluation, we adopt EPCglobal Class-1 Generation-2 protocol[10]. This specification is identical to ISO/IEC 18000-6 type C[11] based onframe slotted ALOHA method in this particular instance.

MAC behavior of over 1000 RF tags with a programmablebattery assisted passive tag which is controlled by an 8bitmicro processor. Finally, Section V concludes the paper.

II. M ULTIPLE RF TAG EMULATION ALGORITHM

In this section, we first explain the methodology for multipleRF tags emulation using one state machine. We then intro-duce two emulation algorithms; Dependent Trial Model andIndependent Trial Model. Lastly, we will discuss the accuracyand efficiency of both emulation algorithms with numericalsimulations.

A. Multiple RF tags emulation using one state machine

The simplest multiple RF tags emulation algorithm imple-ments an independent state machine for each RF tag to beemulated. This is called ”Multi State Machine Model” inwhich each state machine independently operates within aninventory frame and returns its response for every slot basedon its random number generation (Fig. 4 left).

For multiple RF tags emulation using one state machine,that determines RF tag’s response for each slot based onthe probability of the slot being eitherE (Empty) or S(Success) orC (Collision). The probability calculation factorsare the number of emulated RF tags and the frame size. Morespecifically, we generate a random number ’R’ between 0-1for every slot and define:

1) if R 5 E, then Empty2) if E <R 5 (E+S), then Success3) else, Collision

Fig.4(right) shows multiple RF tags emulation method usingone state machine.

B. Dependent Trial Model

To accurately emulate the response of multiple RF tags, weneed to calculateE, S and C, considering the outcome ofprevious slots. The emulation algorithm predicated upon thisprincipal is named Dependent Trial Model, as the probabilityof Empty, Success and Collision for the next slot depends onits previous slot. Eq.(1) showsE, S andCa, wherea indicatesthe number of RF tags colliding,L indicates the frame size,iindicates the slot number (0 toL−1), n indicates the numberof RF tags that have not replied in the current frame. If sloti is Success, then we subtract 1 formn. If slot i is Collision,then we subtracta from n.

E = (1 − 1L − i

)n

S =(

n

1

)(1 − 1

L − i)n−1(

1L − i

)

C2 =(

n

2

)(1 − 1

L − i)n−2(

1L − i

)2

...

Cn =(

n

n

)(1 − 1

L − i)n−n(

1L − i

)n (1)

Fig. 4. Multiple RF tags emulation method using one state machine.

In Dependent Trial Model, the probability is calculated byeach slots and the calculation result is compared to randomnumberR and the response is replied to the interrogator.

C. Independent Trial Model

This model significantly reduces calculation cost of De-pendent Trial Model. The upside is so it can emulate manymore RF tags while the downside being the degradation ofthe accuracy of emulation. Independent Trial Model has thefollowing two characteristics:

1) It does not incorporating the result of previous slotswhen calculating the probability ofE, S andC for nextslot.

2) It does not calculate the number of colliding RF tags incase ofC.

Eq.(2) shows howE, S andC are calculated in this model.

E = (1 − 1L

)n

S =(

n

1

)(1 − 1

L)n−1(

1L

)

C = 1 − E − S (2)

In Independent Trial Model, it is possible, though notpalatable, that more or less RF tags than actually beingemulated may respond. To minimize this problem with non-resource intensive calculation, Independent Trial Model adoptsthe following constraints:

1) if Success, decrementn by 1.2) if Collision, decrementn by 2.3) if Collision andn is 1, Collision is Success.4) if n = 0, then Empty.5) if a slot is the last in a frame,

a) if n = 1, then Success.b) if n = 2, then Collision.

D. Evaluation of proposal algorithms by numerical simulation

Herein we evaluate the emulation accuracy and the process-ing time of our algorithms by comparing with the values of thenumerical simulation of Multi State Machine Model[9]. Thenumerical simulation was done with MATLAB. We conducteda test where we counted the number of slots required for aninventory to be completed (static frame slotted ALOHA, frame

Fig. 5. Evaluation for emulation accuracy whose measure is cumulativeprobability of inventory slots.

size = 16, number of RF tags = 30). In this particular test,an inventory is deemed completed when all 30 RF tags havebeen read since we know exactly how many RF tags there are,whereas the normal termination condition is when there is anempty frame as the interrogator never knows how many RFtags it is trying to read. Therefore, our inventory terminationcondition allows us to count precisely the total number of slotsthat was actually needed for all the RF tags to be read. Thetest was repeated for 10000 times for each model. Fig.5 showsthe number of slots and its cumulative probability distribution.

Other our paper[9] has shown processing times evaluationand other types of emulation accuracy evaluation in additionto the above evaluation. The results verify that, comparedwith Multi State Machine Model, Dependent Trial Model hashigher calculation efficiency(28.8% reduction) and sufficientemulation accuracy, whereas Independent Trial Model exhibitssignificantly higher calculation efficiency(85.5% reduction)and slightly degraded emulation accuracy.

III. I MPLEMENTATION TO ENABLE 1000 RFTAGS

EMULATION WITH A BAP

In this section, we identify the bottleneck for emulatinga large population of RF tags. We, then, show a creativemethod to avoid the bottleneck by pre-calculating and storing

Fig. 6. Outer appearance of BAP.

Fig. 7. Link timing on EPCglobal Class-1 Generation-2 protocol[10].

the probabilistic states before an emulation. The hardware weuse for the implementation is a programmable battery assistedpassive tag (BAP) [12] which is controlled by an 8bit microprocessor, AVR Atmega64L. The reason we used the BAPis that the BAP already has compatible state machine modelwith commercial interrogators as well as the necessary RF andbaseband circuitry and 4Mbit SRAM, which are indispensablefor the proposed implementation. We just need to modify thesoftware to implement the emulation algorithm. Fig.6 showsa BAP. BAP provides two modes of connection; wired andwireless.

A. Bottleneck for a large number of RF tags emulation

EPCglobal Class-1 Generation-2 protocol stipulates that anRF tag must respond to an interrogator command withinT1

(Fig.7), which is determined by interrogator’s transmissionspeed. In other words, the maximum number of emulatable RFtags is determined by how much computation we can manageto pack into thisT1. Thus, we modified and economized ourcalculation equations as follows in the previous study (First,we explain Independent Trial Model）;

First, Eq.(2) can be expressed as Eq.(3).

E = (L − 12Q

)n−1(1

2Q)(L − 1)

S = (L − 12Q

)n−1(1

2Q)n (3)

Fig. 8. Approximation for integer arithmetic.

Second, because floating point computation intensively ex-hausts an 8bit micro processor, we multiplyE and S by216 and make them integers, thus obtaining Eq.(4).216 wasselected as it can directly be used to accord with EPCglobalClass-1 Generation-2 RN16.

EI = 216(L − 12Q

)n−1(1

2Q)(L − 1)

SI = 216(L − 12Q

)n−1(1

2Q)n (4)

To multiply emulatable RF tags, we modify Eq.(4) to Eq.(5).

EI = 216−Q(L − 12Q

)n−1(L − 1)

SI = 216−Q(L − 12Q

)n−1n (5)

Eq.(5) can avoid integer overflow by following calculatingroutine.

uint16 t x = (1 ≪ 16−q);...for(ii=1; ii<=n−1; ii++) {

x = x ∗ (l−1);x = x ≫ (q−1);if(x&1) {

x = (x ≫ 1)+1;}else {

x = x ≫ 1;}

}ei = x ∗ (l−1);si = x ∗ n;

For Dependent Trial Model, we substitute the frame size (L,2Q) with L − i to facilitate integer computation as in Eq.(6).

EI = 216−Q(L − i − 1

L − i)n−12Q(

L − i − 1L − i

)

SI = 216−Q(L − i − 1

L − i)n−12Q(

n

L − i) (6)

We have implemented the algorithms to the BAP andmeasured the maximum number of emulatable RF tags foreach model withT1 = 250µs (40kbps of interrogator to RFtag communication link) comes down as follows:

Dependent Trial Model: 7 RF tagsIndependent Trial Model: 38 RF tagsThose numbers are obviously not large enough for item-

level-tagging.

Fig. 9. Implementation of Independent Trial Model with pre-calculatedprobabilistic states.

B. A creative implementation by pre-calculating the proba-bilistic states

Thus for further computation efficiency, we devise a methodto pre-calculate the probabilistic states shown in Fig.8 beforean inventory and utilize the 4Mbit SRAM of BAP to storethe states. By doing this, the micro processor only needs toaccess those computed values with the corresponding framesize. This is dramatically effective as the read-access time forSRAM is always small and consistent whereas the probabilitycomputation time grows proportionally for a bigger value ofn. Because we express the probabilistic state in 16 bit integers,131,072 patterns can be stored in a 4Mbit SRAM (131072 =(4 × 1024 × 1024)/(16 × 2)).

EPCglobal Class-1 Generation-2 protocol stipulates thatQis between 0–15. Consequently, 16 patterns of frame sizeLexist (20 = 1, 21 = 2, 22 = 4..., 215 = 32768). On anotherfront, (L − i) exists 32768 patterns (12, 2, 3..., 32768).

Dependent Trial Model is required to, but not necessarily inreality, store (32768× n) patterns of probability calculationresult because it does not include(L − i) in probabilitycalculation equations. Dependent Trial Model needs to storeCI values (ie,C2 × 216 to Cn × 216) in addition toEI andSI . As a result, Dependent Trial Model needs a large amountof memory capacity.

Independent Trial Model just stores (16× n) patternsprobability calculation results because it is not included(L−i)and onlyL is included. Additionally Independent Trial Modeljust storeEI andSI because it does not calculate the numberof colliding RF tags. As a result, Independent Trial Modelgives us 8191 as the possible maximum number of emulatableRF tags (8191 = 131072/16−1, from 0 to 8191) with a trivialpenalty in the emulation accuracy shown in Fig.5. Fig.9 showsthe implementation of Independent Trial Model with pre-calculated probabilistic states.

IV. PERFORMANCEEVALUATION IMPLEMENTED IN BAP

Herein we test the emulation of 1000 RF tags, specificallyusing Independent Trial Model with pre-calculated probabilis-tic states implemented in BAP. The emulation is evaluated

2L=1,i=0 or L=2,i=1 or L=4,i=3... orL=32768,i=32767

Fig. 10. Evaluation for emulation accuracy whose measure is success slotpercentage（Frame size = 128, 256）

TABLE IMAXIMUM NUMBER OF RF TAGS CHANGING TRANSMISSION RATE

Implementation 40kbps 80kbpsDependent Trial Model 7 2Independent Trial Model 38 11Independent Trial Model with pre-calculatedprobabilistic states 8191 8191

from three angles; 1) emulation accuracy. 2) the maximumnumber of RF tags probability calculatable withinT1. 3)inventory completion speed with commercial interrogators.

A. Emulation accuracy

We tested for 1000 frames with a fixed frame size (128and 256) so as to obtain Fig.10 which shows Success slotpercentage versus number or RF tags (from 2 to 1000). Forcomparison, we also incorporated in the graph the result ofnumerical simulation of Multi State Machine Model.

Fig.10 indicates that Independent Trial Model with pre-calculated probabilistic states implemented in BAP holdsalmost identical curve with its numerical simulation counter-part. Since success slot percentage per frame differs from itsnumerical simulation by less than 0.2%, the emulation for anentire inventory, which usually consists of scores of frames,can contain its error-bound within a few %.

B. Maximum number of RF tags probability calculatablewithin T1

Since the maximum number of emulatable RF tags isdetermined by how much computation can be executed withinT1, we counted the maximum number of emulatable RF tagsfor three models (Dependent Trial Model, Independent TrialModel, Independent Trial Model with pre-calculated proba-bilistic states) with two common interrogators transmissionspeeds, 40kbps (T1 = 250µs) and 80kbps (T1 = 125µs). TableI shows the result.

The probability computation time is bottleneck when theprobability is computed while in inventory, the maximumnumber of emulatable RF tags decreases when interrogatorstransmission speed raises. This is dramatically effective as the

Fig. 11. Experimental set up

Fig. 12. Evaluation of implementations

read-access time for SRAM is always small and consistentwhereas the probability computation time grows proportionallyfor a bigger value ofn. With SRAM, the maximum numberof emulatable RF tags remains unaltered by interrogatorstransmission speed. Consequently, Independent Trial Modelwith pre-calculated probabilistic states provides a remarkablyeffectual threshold for evaluating an interrogator with highspeed transmission.

C. Inventory completion speed with commercial interrogators

For each model, we measured the inventory completionspeed defined in this case by the number of slots counted bythe end of the last frame. The interrogator connects to BAPon wire that combines an attenuator. (Fig.11).

The interrogator’s transmission speed is 40kbps. And theinterrogator uses dynamic frame slotted ALOHA method, analgorithm which allows frame size to be changed dynamicallydepending on the remaining number of RF tags, for accelera-tion of the inventory. The number of emulated RF tags are setat 7, 38, 50, 100, 200, 500 and 1000. For each of which werepeat an inventory for 1000 times. Fig.12 shows the averagenumber of slots required to complete the inventory.

For the 7 RF tag emulation case, Independent Trial Modelwith pre-calculated probabilistic states has an average of 69.66slots, only differing by 4.7% from 66.53 of Dependent TrialModel, which has been established as the very accurate emu-lation algorithm. Furthermore, Independent Trial Model with

pre-calculated probabilistic states has successfully emulated1000 RF tags. With dynamic frame slotted ALOHA method,the average slots number tends to increase in linear proportionto the number of RF tags[6].

V. CONCLUSION

This paper presented how MAC protocol of more than 1000RF tags can be emulated with an 8bit micro processor in aprogrammable battery assisted passive tag. Such emulationessentially mitigates the burden to lay out a number of RFtags when we evaluate the reading speed of commercialinterrogators. It also secures the fairness among evaluations.In terms of computation cost, the emulation is done most effi-ciently with one state machine using the probabilistic nature ofmultiple RF tags behavior. Dependent Trial Model has strongaccuracy in its emulation and yet suffers a relatively highcomputation cost. Independent Trial Model has less accuracy,but has significantly efficient computation cost. The bottleneckfor the number of RF tags which can be emulated is theexecutable computations within the allowed response time,which depends on the air protocol standard. The emulation canbe made dramatically further efficient, therefore, by storingits conceivable computation results in advance of inventory.This is particularly true for Independent Trial Model since thenumber of probabilistic states that need to be stored is withinthe range of an affordable embedded memory. It is shown byexperiment that MAC behavior of over 1000 RF tags can beemulated with this pre-calculation using 8bit micro processorwith 4Mbit SRAM.

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