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Applied Soft Computing 30 (2015) 249–264

Contents lists available at ScienceDirect

Applied Soft Computing

j ourna l h o mepage: www.elsev ier .com/ locate /asoc

FID reader-to-reader collision avoidance model withultiple-density tag distribution solved by artificial

mmune network optimization

honghua Lia,∗, Jianming Lia, Chunhui Hea, Chengpei Tangb,∗∗, Jieying Zhoua

School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Engineering, Sun Yat-sen University, Guangzhou 510006, China

r t i c l e i n f o

rticle history:eceived 19 January 2013eceived in revised form 26 January 2015ccepted 27 January 2015vailable online 3 February 2015

eywords:rtificial immune networkultiple-density tag distributionptimizationeader-to-reader collisionesource allocation

a b s t r a c t

Radio frequency identification (RFID) is an emerging non-contact technique where readers read data fromor write data to tags by using radio frequency signals. When multiple readers transmit and/or receivesignals simultaneously in a dense RFID system, some reader collision problems occur. Typically, in a mod-ern warehouse management system, the warehouse space is partitioned into blocks for storing differentgoods items on which RFID tags are affixed. The goods items with the equal size are placed in the sameblock. Because the sizes of goods items are possibly different among blocks, the density values of tagsthat are affixed on the goods items are different from each other. In this case, tags in each block are dis-tributed randomly and uniformly while tags in the whole warehouse space (i.e., all blocks are consideredas a whole) follow a non-uniformly random distribution. For the sake of academic research, this situationis defined as a multiple-density tag distribution. From the viewpoint of resource scheduling, this articleestablishes an RFID reader-to-reader collision avoidance model with multiple-density tag distribution(R2RCAM-MTD), where the number of queryable tags is used as the evaluation index. Correspondingly, animproved artificial immune network (AINet-MTD) is used as an optimization method to solve R2RCAM-MTD. In the simulation experiments, four cases with different blocks in a warehouse management systemare considered as testbeds to evaluate the effectiveness of R2RCAM-MTD and the computational accuracyof AINet-MTD. The effects of time slots and frequency channels are investigated, and some comparativeresults are obtained from the proposed AINet-MTD algorithm and the other existing algorithms. Further,

the identified tags and the operating readers are graphically illustrated. The simulation results indicatethat R2RCAM-MTD is effective for reader-to-reader collision problems, and the proposed AINet-MTDalgorithm is more efficient in searching the global optimal solution of R2RCAM-MTD than the exist-ing algorithms such as genetic algorithm (RA-GA), particle swarm optimization (PSO), artificial immunenetwork for optimization (opt-aiNet) and artificial immune system for resource allocation (RA-AIS).

© 2015 Elsevier B.V. All rights reserved.

. Introduction

Radio frequency identification (RFID) is a kind of near fieldommunication technology that enables objects to be identifiedithout direct contact. Generally, an RFID system typically con-

ists of multiple tags where the unique identification codes ofbjects are stored and a reader that communicates with tags overireless channels [1,2]. Because RFID has such many advantages

∗ Corresponding author. Tel.: +86 20 39943520; fax: +86 20 39943315.∗∗ Corresponding author. Tel.: +86 20 28933086; fax: +86 20 39332312.

E-mail addresses: lizhongh@mail.sysu.edu.cn (Z. Li), tchengp@mail.sysu.edu.cnC. Tang).

ttp://dx.doi.org/10.1016/j.asoc.2015.01.056568-4946/© 2015 Elsevier B.V. All rights reserved.

as non-contact, easy implementation, high environmental adap-tation and enough durability, it has been applied compatibly invarious fields, e.g., supply chain management [3,4], indoor position-ing system [5,6], agricultural management [7,8] and manufacturingindustry [9,10]. Therefore, RFID has been becoming a hot topic inboth academic researches and industrial applications, as one ofindispensable emerging technologies in Internet-of-Things (IOT)[11].

To improve the identification efficiency of a stationary RFID sys-tem, multiple readers are usually installed in a group to add thetag interrogation area. In this case, when readers located closely

enough operate simultaneously, the resulting reader collisionproblem may have negative effects on the interrogation perfor-mance of RFID system [12]. Generally speaking, reader collision

2 omputing 30 (2015) 249–264

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50 Z. Li et al. / Applied Soft C

roblems can be divided into two kinds: reader-to-reader collisionnd reader-to-tag collision. Up to date, a great number of reader col-ision avoidance methods have been proposed for these two kindsf reader collision problems [13]. To be specific, for stationary RFIDystems, there are three classes of reader collision avoidance meth-ds: the coverage-based methods [14], the control mechanism-ased methods [15] and the scheduling-based methods [16]. Theoverage-based methods aim at minimizing reader collision byeducing overlapping areas of readers and thus improving the tagdentification rate. The control mechanism-based methods attempto make readers aware of the status of neighboring readers inrder to mitigate reader collision. Besides, the scheduling-basedethods transfer the reader collision problem as an optimization

roblem with respect to the allocation of communication resources17,18]. In this case, time slots, frequency channels and power val-es are optimally configured among readers by using such heuristiclgorithms as simulated annealing algorithm (SA) [19,20], geneticlgorithm (RA-GA) [16], particle swarm optimization (PSO) [21] andrtificial immune system (RA-AIS) [22,23].

By investigating most of existing reader collision avoidanceethods, it is easy to find out that tags are always considered

xplicitly or implicitly (by default) to obey a randomly uniformistribution [16,21]. For the sake of evaluation, in this case, theag identification rate of a stationary RFID system is simply equiva-ent to the total effective interrogation area covered by readers. Inractical applications, however, it is hard that tags are kept to dis-ribute uniformly or regularly. For example, in a typical warehouse

anagement system, the warehouse space is often sectionalizednto several small blocks for different usages, and thus the same-ize goods items can be stored into the same block. In this case,lthough tags affixed on the goods items distribute randomly andniformly in a single small block, the distribution of all tags ison-uniform in the warehouse space as a whole. In reality, this

s the case of multiple-density tag distribution [23]. Therefore, it isecessary that some reader collision avoidance models should beeveloped for such stationary RFID systems with multiple-densityag distribution.

In the last decade, inspired by the biological immune sys-em, artificial immune network has been developed as a heuristicvolutionary algorithm to solve some scientific and engineer-ng problems. Initially, the artificial immune network was usedor data clustering and multimodal function optimization [24,25].ater, various versions of artificial immune network were issuedy improving its algorithmic structures and operators (e.g., clone,utation, suppression and recruitment). Self-reproduction, hyper-utation and parallel evolution of antibodies enable artificial

mmune network to own powerful global search capability. Hence,t is possible that artificial immune network is applied to optimallyssign communication resources to RFID readers.

This paper attempts to establish a reader-to-reader collisionvoidance model for stationary RFID systems with multiple-densityag distribution (R2RCAM-MTD) by considering that time slots andrequency channels are optimally allocated. By investigating sev-ral cases with different multiple-density tag distribution schemes,2RCAM-MTD is evaluated functionally. On the other hand, an

mproved artificial immune network (AINet-MTD) is proposed tofficiently solve R2RCAM-MTD.

The remainder of this paper is organized as follows. Section 2escribes the RFID reader-to-reader collision problem and reviewshe principle of immune network. Section 3 establishes the RFIDeader-to-reader collision avoidance model with multiple-densityag distribution (R2RCAM-MTD), and gives the technical details

f artificial immune network (AINet-MTD). Further, a series ofomparative numerical experiments are arranged in Section 4o evaluate the performances of AINet-MTD and the other fourxisting algorithms. Finally, some conclusions are drawn in

Fig. 1. Illustration of the reader-to-reader collision problem.

Section 5. In addition, some variables and symbols, supplementaryfigures and supplementary datasets are listed in Appendices A–C,respectively.

2. Reviews of related theories

2.1. RFID reader-to-reader collision problem

Generally, a basic RFID system consists of one or more readers,a number of tags and an application system storing and managingdata. For a stationary RFID system whose readers are fixed phys-ically in the deployment space, the operating procedures can bedescribed as follows. Firstly, a reader with an antenna transmitsactively radio frequency (RF) signals within its effective interro-gation range. Secondly, if a passive tag with an antenna is locatedin this range, it will be powered and activated via electromagneticcoupling signals from this reader, and will send data stored in thetag chip back to this reader at a pre-selected frequency channel.Thirdly, the reader receives data from the tag and delivers it towardthe application system for further processing.

To improve the interrogation capability of the stationary RFIDsystem, however, it is necessary that multiple readers should bedeployed in the same working space. In this case, the possiblereader collision problems (especially the reader-to-reader colli-sion) will result into the reduction in the interrogation area ofreaders. To be specific, when a desired reader attempts to receivethe signals from tags correctly, the power of signal that one ormore neighboring readers (also called interfering readers) trans-mit simultaneously is possibly larger than the power of backscattersignal from the tag. As a result, the desired reader should have asmaller effective interrogation radius.

Fig. 1 illustrates a simple RFID system with two stationary read-ers – a desired reader R1 and an interfering reader R2 – whosedistance is denoted as d2,1. As shown in Fig. 1, the desired reader R1can reach the maximum interrogation radius rmax,1 without inter-ference from other readers (marked as a blue dashed-line circle).However, if R1 and R2 operate at the same time, and R1 locateswithin the interference range of R2 (i.e., the distance between R1and R2 is less than rinterfer), the interrogation radius of R1 will

nicate with tags correctly. Consequently, the tags located outsidethe black solid-line circle cannot be identified successfully by R1.Therefore, how to avoid such reader collision is becoming a researchhotspot in the field of RFID.

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 251

Table 1Terminological definition (BIS versus AIS).

Term Definition in BIS Definition in AIS

Antigen Substance recognizedby immune cells andactivating the immuneresponse.

Objective problemneed to be solved,including constraintconditions.

Antibody Molecule propagatedby B-cell after thestimulation of antigen.

The feasible candidatesolution satisfying theprojective problem andits conditions.

Affinity Binding ability ofantibody and antigen.

Fitness value of theobjective problemcorresponding to the

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Especially speaking, it is known from Eq. (1), if all the tag density

feasible solution.

.2. Principle of immune network

The biological immune system (BIS) is one of fundamental evo-utionary systems that the human being owns, which is capable ofxcreting cells/antibodies to destroy the invading antigens with theelp of such mechanisms as antigen recognition, cell evolution, cellemorization, cell proliferation and cell concentration adjustment

24]. In detail, once some new antigens invade the organism, thectivated B lymphocytes (B-cells) propagate a number of antibodieso destroy those antigens with the help of T lymohocytes (T-cells).uring the antibody reproduction, these antibodies iteratively suf-

er from the somatic mutation and concentration suppression at high rate. As this process continues, these antibodies graduallyecome mature and thus evolve as memory cells which have more

ife cycles. This process is called the primary response. Since then,f the same or similar antigens appear again in the organism, these

emory cells will response more quickly, more sharply and moreffectively to destroy the invading antigens. Instead of the primaryesponse, this process is called the secondary response.

Inspired by BIS, the artificial immune system (AIS) has beeneveloped to solve real-world problems such as function optimiza-ion, intrusion detection, data mining, and pattern recognition. Upo date, AIS can be clustered into four families: clone selectioneries, negative selection series, danger theory series and immuneetwork series. Especially, artificial immune networks are widelysed in some specific fields. Corresponding to biological immuneystems, artificial immune networks have several concepts that cane defined in terminology. For the sake of clear description, someerms in both BIS and AIS are defined in Table 1.

Artificial immune network is an important branch of artificialmmune systems. As a classic artificial immune network, opt-aiNet

as designed for multimodal function optimization [25]. Opt-aiNetmploys a uniform cloning where each antibody has a fixed num-er of clones, and introduces an affinity-based Gaussian mutationhere the mutation degree depends on its affinity. Meanwhile, theeasure of Euclidean distance is used to evaluate to what extent

ntibodies are similar, and the suppression threshold is used to con-rol the concentration of antibodies. Following opt-aiNet, differentariants have been developed to solve all kinds of scientific andngineering problems.

Recently, an artificial immune system for resource allocationRA-AIS) was proposed to solve RFID reader-to-reader collisionvoidance model (R2RCAM) [22]. From the viewpoint of algorith-ic structure, RA-AIS is in fact an artificial immune network. In

A-AIS, the antigen corresponds to the resource allocation to read-rs in R2RCAM, the antibody corresponds to the feasible solutionith respect to resource allocation and the affinity corresponds to

he effective interrogation area of readers. By resorting to powerfullobal search capability, RA-AIS can obtain larger effective interro-ation area in solving RFID reader collision problem. However, the

Fig. 2. Schematic graph of a stationary RFID system with multiple-density tag dis-tribution.

tediousness of the mutation operator impairs the computationalefficiency of RA-AIS to some extent.

3. Artificial immune network based reader-to-readercollision avoidance method

3.1. Reader-to-reader collision avoidance model withmultiple-density tag distribution

As mentioned in Section 2.1, multiple RFID readers are ofteninstalled into a group and tags are affixed on the goods items insome real-world applications, such as warehouse management.To facilitate storage and management, a warehouse space is oftendivided into several blocks, and thus different types of goods itemsare placed in an independent block, respectively. If we assume thattags are distributed randomly and uniformly in each block, thesetags obey a single-density tag distribution in a single block. Due tothe fact that the distribution of tags is different in density for eachblock, the distribution of all tags in the whole warehouse spacecan be defined as the multiple-density tag distribution. Next, wewill establish a reader-to-reader collision avoidance model withmultiple-density tag distribution (R2RCAM-MTD), as far as an RFIDsystem with multiple stationary readers that are deployed in a sec-torized warehouse is concerned.

Considering an example as shown in Fig. 2, the warehouse spaceis sectionalized into P × Q blocks. If we define �(p,q) be the tag den-sity in the block located at the block coordinate (p,q), the tag densitymatrix of the warehouse space can be given by

� =

⎡⎢⎢⎢⎢⎣

�(1, 1) · · · �(1, q) · · · �(1, Q )

· · · · · · · · · · · · · · ·�(p, 1) · · · �(p, q) · · · �(p, Q )

· · · · · · · · · · · · · · ·�(P, 1) · · · �(P, q) · · · �(P, Q )

⎤⎥⎥⎥⎥⎦ ,

1 ≤ p ≤ P, 1 ≤ q ≤ Q. (1)

values are equal, i.e.,

�(1, 1) = · · · = �(p, q) = · · · = �(P, Q ), (2)

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52 Z. Li et al. / Applied Soft C

he tags in the whole space obey a randomly uniform distribution.n this case, the tag density matrix in Eq. (1) can be defined as aingle-density tag distribution matrix.

Generally, if there exist two or more tag density values that arenequal, i.e., if p1,p2 ∈ [1,P] and q1,q2 ∈ [1,Q] satisfy

(p1, q1) /= �(p2, q2) (3)

he tags in the whole space do not follow a randomly uniform dis-ribution. Instead of the single-density tag distribution, in this case,he tag density matrix in Eq. (1) can be defined as a multiple-densityag distribution matrix.

Let’s assume that NReader readers are installed randomly in thearehouse space, including a desired reader Ri and NReader−1 inter-

ering readers Rj (j = 1, 2, . . ., NReader, j /= i). As shown in Fig. 2, dj,i ishe Euclidean distance between Ri and Rj, and ri(k) is the effectiventerrogation radius of Ri at the kth time slot. As well, NFreq fre-uency channels and NSlot time slots are available to be allocatedor these readers. To successfully identify the target tag at the kthime slot, the signal-to-interference-plus-noise ratio (SINRi(k)) of

signal received by Ri backscattered from the target tag must bereater than a threshold value (i.e., SINRmin). In other words, if Ri isxpected to identify the tag with a distance x, SINRi(k,x) must satisfy

INRi(k, x) = BPi(k, x)Ii(k) + Noisei

≥ SINRmin, (4)

here BPi(k,x) is the backscatter signal power received by Ri fromhe tag, Ii(k) is the total interference power measured at Ri at theth time slot from the other operating readers, and Noisei is theackground noise power measured at Ri. According to [18], BPi(k,x)an be defined as

Pi(k, x) = ˛bωETagPiGTGR(PLossx−� )2, (5)

here �b� is the normalized spectrum power, ETag is the effec-ive power reflection coefficient of the tag, Pi is the transmittingignal power of the reader Ri, GT represents the transmittingntenna gain, GR represents the receiving antenna gain, PLossenotes the reference path loss at the distance of one meter from

desired reader, and � is the path-loss exponent (always � ≥ 2).n the other hand, the total interference power Ii(k) is determinedy

Ii(k) =NReader∑j=1,j /= i

ωj(k)Ij,i(k)dj,i

= hGTGR

NReader∑j=1,j /= i

ωj(k)Pjˇmask(|CHi(k) − CHj(k)|)PLossd−�j,i

.

(6)

here, h is the fading coefficient, Pj represents the transmittingignal power of Rj, CHi(k) denotes the frequency channels selectedy Ri at the kth time slot and CHj(k) denotes the frequency chan-els selected by Rj at the kth time slot. Here, CHi(k)∈[1,NFreq] and

ri(k) = min

⎛⎝( ˛bωETagPiGTGRP2

Loss

SINRminhGT GR

∑NReaderj=1,j /= i

ωj(k)Pjˇmask(|CHi(k) − CH

s.t. ∀i ∈ NR, ∀k ∈ NS, CHi(k) ∈ CH.

Hj(k)∈[1,NFreq]. According to the definition of the spectrum maskevel in EPC global Class 1 Gen 2 [26], ˇmask(•) is a function withespect to the frequency channel interval between two readers.ote that the fading is not considered here because the distance

ting 30 (2015) 249–264

between readers is on the line of sight. Besides, the sign functionωj(k) satisfies

ωj(k) ={

1, if the reader Rj operates at the kth slot,

0, otherwise.(7)

By integrating Eqs. (4)–(7), SINRi(k,x) can be completelyexpressed as

SINRi(k, x) = ˛bωETagPiGTGR(PLossx−� )2

hGT GR

∑NReader

j=1,j /= iωj (k) Pjˇmask

(∣∣CHi (k) − CHj (k)∣∣)PLossd−�

j,i+ Noisei

,

s.t. ∀i ∈ NR, ∀k ∈ NS, CHi(k) ∈ CH,

(8)

where NR = {1, 2, . . ., NReader}, NS = {1, 2, . . ., NSlot} and CH = {1, 2,. . ., NFreq}. If we define ri(k) be the effective interrogation radius ofRi at the kth time slot, ri(k) can be obtained by solving

ri(k) = arg maxx

SINRi(k, x) ≥ SINRmin. (9)

However, ri(k) must be less than or equal to the maximum inter-rogation radius rmax,i(k) of Ri with no interference at the kth timeslot. According to [27], the maximum interrogation radius rmax,i(k)is determined by

rmax,i(k) =(

�i(k)4�

)√PiGTGR

Pmin× 1 − M4

d

(1 + Md)2, (10)

where �i(k) denotes the wavelength of Ri at the kth time slot, Pminis the minimum power required for the tag to operate and Md is themodulation depth.

By combining Eqs. (8)–(10), ri(k) is expressed as

PLossd−�j,i

+ Noisei

)1/(2�)

, rmax,i(k)

⎞⎠ ,

(11)

Once the effective interrogation radius ri(k) is calculated byusing Eq. (11), the tag identification capability of the RFID sys-tem can be also determined. The tag identification capability canbe measured by the number of tags that readers are able to querywithin the effective interrogation area [12]. For the sake of simplic-ity, it is called the total number of queryable tags. According to Fig. 2,the total number of tags that reader Ri can query at the kth timeslot can be mathematically defined as

NTi(k) =P∑

p=1

Q∑q=1

�(p, q) × Si(k, p, q), (12)

where Si(k,p,q) is the effective interrogation area of Ri in the blockof �(p,q) at the kth time slot. Note that Si(k,p,q) = 0 means the factthat Ri can identify no tags in the block of �(p,q) at the kth time slot.

Above all, from the viewpoint of resource scheduling, thereader-to-reader collision avoidance model with multiple-densitytag distribution, i.e., R2RCAM-MTD, can be modeled as the follow-ing optimization problem

Maximize NT =NReader∑ NSlot∑

NTi(k),

=NReader∑

i=1

NSlot∑k=1

P∑p=1

Q∑q=1

ωi(k) × �(p, q) × Si(k, p, q),

∀i ∈ NR, ∀k ∈ NS, CHi(k) ∈ CH.

(13)

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 253

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Especially, when �(p,q) = 1 for all p (p = 1, 2, ..., P) and q (q =, 2, ..., Q ), the tag density matrix � as shown in Eq. (1) becomesn all ones matrix. In this case, R2RCAM-MTD described in Eq. (13)s equivalent to the model R2RCAM given in [22].

.2. Artificial immune network for R2RCAM-MTD

In this subsection, an improved artificial immune network for2RCAM-MTD, i.e., AINet-MTD, is proposed to aim at optimizing thellocation of time slots and frequency channels. In the proposedINet-MTD algorithm, the antigen corresponds to the objectiveptimization problem shown in Eq. (13), the antibody correspondso the feasible allocation solution with respect to time slots andrequency channels. Accordingly, the affinity between the antigennd the antibody corresponds to the total number of queryableags (i.e., the function value of the objective problem with respecto a feasible solution). Therefore, for this specific application of2RCAM-MTD, the major immune operators in AINet-MTD shoulde redesigned or revised, where the evolved antibodies would beubject to the practical constraints of RFID systems. The techni-al details of the proposed AINet-MTD algorithm are described asollows.

.2.1. Antibody encodingAccording to R2RCAM-MTD in Eq. (13), a feasible solution means

candidate antibody string in AINet-MTD. Because available fre-uency channels are discrete for readers, they can be denoted by

nteger numbers. Therefore, the antibody string can be encoded as sequence of integer numbers, as described in Fig. 3. Seen fromig. 3, an antibody string includes NReader segments, and each seg-ent has NSlot bits. For example, CHi(1), ..., CHi(k), ..., CHi(NSlot) is

sed for Ri. Where, CHi(k) denotes the frequency channel allocatedo Ri at the kth time slot and CHi(k) ∈ CH. Note that CHi(k) = 0 meanshat Ri does not operate at the kth time slot. Clearly, an antibodyas the length of NSlot × NReader bits.

.2.2. Affinity evaluationFor such a maximization problem as R2RCAM-MTD in Section

.1, its objective function is selected as the affinity function of theroposed AINet-MTD algorithm. This means that an affinity valueorresponds to every candidate antibody (i.e., every candidate allo-ation solution). Therefore, we rewrite the objective function in Eq.13) in form of the affinity function with respect to the candidatentibody string Ab, that is

Affinity(Ab) =NReader∑

i=1

NSlot∑k=1

P∑p=1

Q∑q=1

ωi(k) × �(p, q) × Si(k, p, q),

and ∀i ∈ NR, ∀k ∈ NS, CHi(k) ∈ CH.

(14)

By using Eq. (14), we can evaluate the affinity value of any can-idate antibody, and then take advantage of this affinity value touide the evolution of antibody population.

.2.3. InitializationIn the initialization phase, some parameters of immune oper-

tors are initialized and NIni candidate antibodies are randomly

roduced to construct a candidate antibody population. Obviously,ll candidate antibodies must be subject to the constraints in Eq.14). Then, the affinity values of all candidate antibodies are evalu-ted according to the affinity function shown in Eq. (14).

a candidate antibody.

3.2.4. CloningClone operator means that children antibodies are reproduced

from the same parent antibody. The clone strategy determines towhat degree the parent antibody participates in reproduction. Theuniform clone strategy used in AINet-MTD is very simple and easyto implement. In detail, NClone (a predefined multiplier) childrenantibodies are cloned from each parent antibody whatever its affin-ity is. All cloned children antibodies but the parent one undergoesthe following mutation operator.

3.2.5. MutationUsually, it is believed that the parent antibodies may generate

new antibodies with higher affinity when they experience hypermutation. Thus, as the only and most important operator in arti-ficial immune network, the mutation operator should be actedeffectively. Considering the encoding format with integers usedin AINet-MTD, the mutation operator should be redesigned care-fully. According to Eq. (11), the effective interrogation radius ofeach reader is related with the difference between the frequencychannels allocated to readers. The total interference is quite greatwhen the same channel allocated to readers within a certain dis-tance. Therefore, a specific allocation mechanism is used to avoidthe same frequency channel from being allocated to readers withina distance less than the lower bound dmin.

To start with the mutation operator in AINet-MTD, two emptysets, i.e., the waiting reader set WR and the mutated reader set MR,are predefined to store some temporary data during the mutationprocess. Firstly, for any candidate antibody Ab, we select randomlya bit CHi(k) to be mutated and add Ri where CHi(k) lies into WR.Secondly, we select randomly an element Rw from WR, and the bitCHw(k) to be mutated is determined. Thirdly, for CHw(k), we selectrandomly an available frequency channel number as the mutatedresult CH’w(k).

After mutation, the waiting reader set WR and the mutatedreader set MR are updated. Firstly, we add Rw into MR and removeRw from WR. Secondly, we find the readers (i.e., elements of SR)whose mutual distance from Rw is less than dmin and whose allo-cated channel is the same as CH’w(k), and add these readers into WR.The iterative process above is repeated until the waiting reader setWR is empty. For the sake of clear description, the pseudocode ofthe mutation operator is shown in Fig. 4.

3.2.6. SuppressionBy executing the cloning operator and the mutation operator,

it is possible that antibodies become similar in antibody encodingand thus their affinity is almost equal. Their effects are redundantin searching the global optima of the objective function. In thiscase, the concentration of antibodies is used as a measure indexto evaluate the similarity degree of antibodies in the immune net-work. The higher concentration means that those antibodies aremore similar. According to the immune network theory [24], thoseantibodies with high affinity and low concentration should be pro-moted, and antibodies with low affinity and high concentrationshould be suppressed in order to guarantee the diversity of anti-bodies. Thus, the suppression operator offers a chance to removethe redundant antibodies and improve the diversity of antibodies.

Because redundancy of similar antibodies reduces the diversity ofantibodies, the suppression operator plays a role of adjusting theconcentration of antibodies. The suppression operator will be trig-gered only when there is no significant difference in the average

254 Z. Li et al. / Applied Soft Computing 30 (2015) 249–264

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Table 2Parameter settings of R2RCAM-MTD.

Parameter Value

Normalized spectrum power(�b�)

0.86

Effective power reflectioncoefficient (ETag)

0.1

Transmitting/receivingantenna gain (GT, GR)

6 dBi

Reference path loss at thedistance of 1 m (PLoss)

−31.6 dB

Maximum transmitting powerof the reader (NClone)

0 dB

Path-loss exponent (�) 2.5Minimum power required for

the tag operation (Pmin)−15 dBm

Modulation depth (Md) 0.1Operating frequency 902–928 MHzChannel bandwidth 500 kHzLower bound distance (dmin) 16 mNoise Power −90 dBmMinimum SINR(SINRmin) 11.6 dBSpectrum mask, ˇmask (�CH) �CH = 0 0 dB

�CH = 1 −20 dB�CH = 2 −50 dB�CH = 3 −60 dB�CH > 3 −65 dB

Table 3Parameter settings of the proposed AINet-MTD algorithm.

Parameter Value

Clone multiplier (NClone) 10Percentage of suppression (�) 10%Percentage of recruitment (b) 40%

Fig. 4. Pseudocode of the mutation operator.

ffinity of candidate antibodies during two neighboring genera-ions. In AINet-MTD, the similarity-based suppressor is employed.or any two candidate antibodies, the one with lower affinity wille removed if their Euclidean distance is less than a suppressionhreshold Ths. By measure of the concentration of the population,he suppression threshold Ths can be adjusted dynamically whichollows

ThS = min(Du,v, u /= v)

+� × (max(Du,v, u /= v) − min(Du,v, u /= v)).(15)

here � ∈ (0, 1] is a controlling factor which is used to adjust thehreshold and Du,v is the Euclidean distance between the candidatentibodies Abu and Abv.

After the suppression operator is executed, a certain number ofntibodies satisfying the constraints are reproduced randomly andecruited into the antibody population. Note that the size of theandidate antibody population size should be less than or equal tohe upper limit Nmax.

When the number of iterations reaches the predefined maxi-um generations, the proposed AINet-MTD algorithm terminates.For clarity of clear description, the complete flowchart of the

roposed AINet-MTD algorithm is given in Fig. 5.

. Simulations and results

This section investigates the effects of the number of time slotsnd frequency channels, and compares the performance betweenhe proposed AINet-MTD algorithm and four existing methods, i.e.,enetic algorithm (RA-GA), particle swarm optimization (PSO), arti-cial immune network for optimization (opt-aiNet) and artificial

mmune system for resource allocation (RA-AIS).

.1. Experimental environment

.1.1. Parameter settingsIn the experimental simulations, readers are deployed randomly

n a warehouse space of 100 m × 100 m, and their detailed positions

re listed as supplementary datasets in Appendix C. Following EPC-lobal Class 1 Gen 2 protocols [26], a reader communicates with itsarget tags at the same frequency channel. For the sake of clar-ty, the parameters in R2RCAM-MTD are set as in Table 2, while

Maximum population size (Nmax) 100

the parameters in the proposed AINet-MTD algorithm are set as inTable 3. Besides, for all compared algorithms, the initialized popu-lation size is set to be 40 and the maximum number of iterationsis set to be 500, respectively. All experimental simulations are runrepeatedly for 30 trials in Matlab Platform with Microsoft WindowsXP, Intel Core(TM) i3 CPU 2.4 GHz and 2 GB RAM memory.

4.1.2. Case settingsIn the simulations, four test cases standing for different ware-

house management schemes are used to evaluate the effects of timeslots and frequency channels, as well as the performances of dif-ferent algorithms. Fig. 6(a)–(d) gives the illustrations of these fourtest cases, i.e., case A (1 × 1 block), case B (1 × 2 blocks), case C (2 × 2blocks) and case D (3 × 3 blocks), respectively. Seen from Fig. 6, if �0is the reference distribution density of tags, the tag density matricesof four cases are described as follows.

Case A, �A = [�0] , (16)

Case B, �B =[

5�0 �0]

, (17)

Case C, �C =[

5�0 �0

2�0 10�0

], and (18)

Case D, �D =

⎡⎢⎣

5�0 �0 6�0

2� 10� 3�

⎤⎥⎦ . (19)

0 0 0

7�0 4�0 9�0

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 255

Fig. 5. Flowchart of the proposed AINet-MTD algorithm.

256 Z. Li et al. / Applied Soft Computing 30 (2015) 249–264

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Fig. 6. Graphical representation of fou

.2. Effects of time slots and frequency channels

.2.1. Effects of time slotsTo investigate the effects of different number of time slots, the

umber of available frequency channels is set to be 10. For eachase, we evaluate the total number of queryable tags when differ-nt numbers of times slots are considered (increasing from 3 to3 with an increment of 2). Therefore, for a specific case with aertain number of readers, we can plot a curve that reflects theelationship of the total number of queryable tags with the num-er of times slots. Meanwhile, different numbers of readers (e.g.,Reader = 4, 8, 12, 16, 20 and 24, respectively) are taken into account

n four cases. Fig. 7(a)–(d) illustrates the effects of different num-ers of time slots for four cases, respectively. It is observed fromig. 7, the total number of queryable tags grows up almost linearlys the number of time slots increases, whichever case we consider.he reason is that there are more time slots for readers to selecto identify tags so that their total number of queryable tags can

ecome larger.

On the other hand, as you see in Fig. 7, for a specific number ofime slots, the total number of queryable tags firstly increases andhen decreases as the number of reader varies from 4 to 24. For the

s with different tag density matrices.

example in Fig. 7(c), the curve of the total number of queryable tagswith 8 readers always lies above other curves whatever the numberof time slots is. To be specific, there is a rise in the curve of the totalnumber of queryable tags if the number of readers increases from 4to 8, while there is a decline if the number of readers increases from8 up to 24. Specially, the curve of the total number of queryable tagswith 24 readers always lies below all the other curves. In this sce-nario, fewer readers mean less reader collision while denser readersmean more reader collision.

In summary, whichever case is considered, we can find that themore time slots are, the greater the total number of queryable tagsis. However, when too many time slots are considered, the identi-fication rate at which a reader queries tags is lower, because it hasto wait more before the next interrogation. Thus, without loss ofgenerality, 5 time slots are used in the following simulations.

4.2.2. Effects of frequency channels

Similarly, to investigate the effects of different numbers of fre-

quency channels, 5 time slots are used. As the number of availablefrequency channels increases from 6 to 16 with an increment of2, we evaluate the total number of queryable tags for different

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 257

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umbers of readers (e.g., 4, 8, 12, 16, 20 and 24 readers, respectively)n four cases. Fig. 8 shows the effects of frequency channels.

Seen from Fig. 8, when there are few readers (e.g., 4 or 8 read-rs), the total number of queryable tags grows up slowly as morevailable frequency channels are provided. The reason is that therere such enough frequency channels to be allocated to readers thateaders can escape from reader collision. Specially, for NReader = 4,he curves are almost straight lines in all four cases with the increas-ng of frequency channels, which indicate that four readers canlways be allocated at different channels to operate at each time slothen 6 or more frequency channels are available. On the contrary,hen there are more readers (such as 16, 20 and 24 readers), the

otal number of queryable tags increases quickly with the increas-ng of the number of available frequency channels. This is becausehat more frequency channels imply more potential for readers tovoid reader collision, as a result, the total number of queryableags increases indeed.

For few frequency channels (e.g., 6 channels), on the other

and, the total number of queryable tags firstly increases and thenecreases as more readers are given. However, for more frequencyhannels, e.g., 16 channels in Fig. 8(a), (b) and (d), we can find thathe more readers are, the larger the total number of queryable tags

DCase(d)

time slots.

is. The reason is that more available frequency channels are help-ful for readers to mitigate reader collision and further make morereaders operate simultaneously, so as to improve the total numberof queryable tags. Without loss of generality, 10 frequency channelsare used in the following simulations.

4.3. Evaluation of algorithmic performances

4.3.1. Performance comparisons in different casesTo investigate the algorithmic performances, a series of com-

parative simulations are executed among the proposed AINet-MTDalgorithm and the existing algorithms such as RA-GA, PSO, opt-aiNet and RA-AIS. In these simulations, four cases shown in Fig. 6are considered, respectively. Note that the reference tag density �0is set to be 1 tag per square meter without loss of generality. Asa result from Section 4.2, 5 time slots and 10 frequency channelsare available. The performance metrics with respect to the total

number of queryable tags are obtained by the best, the worst, themean and the standard deviation (std) of 30 times. To make it moreclearly, the t-test is used to evaluate the effectiveness and the accu-racy of different search algorithms. As a result, the 95% confidence

258 Z. Li et al. / Applied Soft Computing 30 (2015) 249–264

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DCase(d)CCase(c)

f frequ

if

FSb5fqTcqaawADTtotr

Fig. 8. Effects o

ntervals are also calculated from each group of data samples outputrom different algorithms.

Tables 4–7 give the numerical simulation results in four cases.or the sake of clear observation, the best results are typed in bold.een from Tables 4–7, there is a similar trend in the total num-er of queryable tags as the number of readers increases from

to 30. In detail, when the number of readers NReader increasesrom 5 to 30 (with an increment of 5), the total number ofueryable tags performs firstly increasing and then decreasing.herefore, there must exist a peak when NReader equals some spe-ific value. Here, when NReader = 10, the average total number ofueryable tags obtained by AINet-MTD is 6549.2665, 22,470.868nd 41,384.978 for cases A, B and C, respectively. It is clear that

peak occurs with 10 readers for case A, B or C. On the contrary,hen NReader = 15, there exists a peak of 41,255.568 captured byINet-MTD in the average total number of queryable tags for case. In this case, the 95% confidence interval is (39,518, 42,993).hese results demonstrate that more readers can contribute to

he total number of queryable tags; however, when the numberf reader reaches a certain value, the total number of queryableags begins to decline due to the heavier collision problem amongeaders.

ency channels.

Table 4 gives the comparison results of case A (with only oneblock) from different algorithms. Case A is a special example withsingle-density tag distribution, and looks like a uniformly ran-dom tag distribution as shown in [23]. Because of single-densitytag distribution, it is evident that the total number of queryabletags is proportional to the total effective interrogation area. Seenfrom Table 4, the best, the worst and the mean values of the totalnumber of queryable tags optimized by the proposed AINet-MTDalgorithm are greater than those by RA-GA, PSO, opt-aiNet and RA-AIS. For example, when NReader equals 25, the mean total number ofqueryable tags optimized by AINet-MTD is 4695.04; while there are906.44 by RA-GA, 646.02 by PSO, 434.67 by opt-aiNet and 2283.19by RA-AIS, respectively. Correspondingly, compared to other exist-ing algorithms, the proposed AINet-MTD algorithm harvests animprovement by 417.96%, 626.76%, 980.14% and 105.64%, respec-tively. These results demonstrate that the proposed AINet-MTDalgorithm has more promising performance in solving R2RCAM-MTD when compared with the other four algorithms.

Similarly, Tables 5–7 show the comparison results of cases B,C and D, whose tag density matrices are �B, �C and �D, respec-tively. Seen from Tables 5–7, the proposed AINet-MTD algorithmis still more efficient in optimizing R2RCAM-MTD than RA-GA,

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 259

Table 4The total number of queryable tags in case A (the best is in bold).

No. of readers Algorithm Best Worst Mean Std 95% confidence interval

NReader = 5 RA-GA 6.7175e+03 3.2003e+03 5.1828e+03 8.6421e+02 (4.8601e+03, 5.5055e+03)PSO 5.7113e+03 1.3215e+03 3.5721e+03 8.9456e+02 (3.2380e+03, 3.9061e+03)opt-aiNet 4.7454e+03 1.9500e+03 3.4837e+03 6.5983e+02 (3.2373e+03, 3.7301e+03)RA-AIS 6.9962e+03 3.5751e+03 5.4381e+03 8.8330e+02 (5.1083e+03, 5.7680e+03)AINet-MTD 7.1445e+03 4.3301e+03 5.6510e+03 7.1355e+02 (5.3846e+03, 5.9175e+03)

NReader = 10 RA-GA 4.6120e+03 2.5281e+03 3.5071e+03 5.5412e+02 (3.3002e+03, 3.7140e+03)PSO 3.2031e+03 1.2924e+03 1.7644e+03 2.4258e+02 (1.6738e+03, 1.8550e+03)opt-aiNet 2.1526e+03 1.2751e+03 1.6981e+03 2.3514e+02 (1.6103e+03, 1.7859e+03)RA-AIS 6.8232e+03 4.2749e+03 5.1662e+03 5.4390e+02 (4.9631e+03, 5.3693e+03)AINet-MTD 7.8196e+03 5.6961e+03 6.5493e+03 5.4101e+02 (6.3473e+03, 6.7513e+03)

NReader = 15 RA-GA 2.5784e+03 1.5914e+03 2.0682e+03 2.3948e+02 (1.9788e+03, 2.1576e+03)PSO 1.3456e+03 8.7651e+02 1.0823e+03 1.1886e+02 (1.0379e+03, 1.1266e+03)opt-aiNet 1.1302e+03 8.4517e+02 9.4619e+02 6.9098e+01 (9.2039e+02, 9.7199e+02)RA-AIS 4.0907e+03 2.9080e+03 3.3888e+03 2.7479e+02 (3.2862e+03, 3.4914e+03)AINet-MTD 7.2894e+03 5.4323e+03 6.4093e+03 4.7072e+02 (6.2335e+03, 6.5850e+03)

NReader = 20 RA-GA 1.5395e+03 1.0210e+03 1.2962e+03 1.3789e+02 (1.2448e+03, 1.3477e+03)PSO 1.0418e+03 4.9544e+02 7.3402e+02 1.3703e+02 (6.8285e+02, 7.8518e+02)opt-aiNet 8.0860e+02 4.3947e+02 6.0710e+02 6.2764e+01 (5.8367e+02, 6.3054e+02)RA-AIS 3.1394e+03 2.1683e+03 2.7609e+03 2.1614e+02 (2.6802e+03, 2.8416e+03)AINet-MTD 6.6186e+03 4.1944e+03 5.7376e+03 5.2746e+02 (5.5406e+03, 5.9345e+03)

NReader = 25 RA-GA 1.1476e+03 6.7962e+02 9.0644e+02 1.1916e+02 (8.6195e+02, 9.5094e+02)PSO 8.0918e+02 4.6240e+02 6.4602e+02 9.2223e+01 (6.1158e+02, 6.8045e+02)opt-aiNet 5.0229e+02 3.7970e+02 4.3467e+02 2.5925e+01 (4.2499e+02, 4.4435e+02)RA-AIS 2.5433e+03 2.0615e+03 2.2832e+03 1.2490e+02 (2.2366e+03, 2.3298e+03)AINet-MTD 6.1622e+03 3.5816e+03 4.6950e+03 6.7050e+02 (4.4447e+03, 4.9454e+03)

NReader = 30 RA-GA 8.2683e+02 5.0277e+02 6.4634e+02 6.8847e+01 (6.2063e+02, 6.7205e+02)PSO 7.6225e+02 3.2817e+02 5.5978e+02 1.0221e+02 (5.2162e+02, 5.9795e+02)opt-aiNet 4.3975e+02 2.9362e+02 3.4940e+02 3.4877e+01 (3.3638e+02, 3.6242e+02)RA-AIS 2.2933e+03 1.8427e+03 1.9983e+03 1.0880e+02 (1.9577e+03, 2.0389e+03)AINet-MTD 4.4487e+03 3.0398e+03 3.8043e+03 3.7049e+02 (3.6660e+03, 3.9427e+03)

Table 5The total number of queryable tags in case B (the best is in bold).

No. of readers Algorithm Best Worst Mean Std Confidence interval

NReader = 5 RA-GA 2.1597e+04 6.4536e+03 1.8315e+04 3.6725e+03 (1.6943e+04, 1.9686e+04)PSO 1.8395e+04 2.9569e+03 1.1938e+04 3.4067e+03 (1.0666e+04, 1.3210e+04)opt-aiNet 1.7195e+04 3.1642e+03 1.1762e+04 2.9247e+03 (1.0670e+04, 1.2855e+04)RA-AIS 2.4672e+04 5.8763e+03 1.7914e+04 4.5512e+03 (1.6215e+04, 1.9614e+04)AINet-MTD 2.4761e+04 1.0056e+04 2.2406e+04 2.5715e+03 (2.1446e+04, 2.3367e+04)

NReader = 10 RA-GA 1.7525e+04 9.2834e+03 1.2900e+04 1.8886e+03 (1.2195e+04, 1.3606e+04)PSO 1.1446e+04 3.4052e+03 6.6475e+03 1.5687e+03 (6.0618e+03, 7.2333e+03)opt-aiNet 7.3895e+03 3.9984e+03 5.7471e+03 8.2338e+02 (5.4396e+03, 6.0546e+03)RA-AIS 2.0910e+04 1.2357e+04 1.7139e+04 2.3883e+03 (1.6248e+04, 1.8031e+04)AINet-MTD 2.6564e+04 1.7482e+04 2.2471e+04 2.0792e+03 (2.1694e+04, 2.3247e+04)

NReader = 15 RA-GA 9.8298e+03 5.6457e+03 7.6371e+03 1.1346e+03 (7.2134e+03, 8.0607e+03)PSO 5.7589e+03 3.0007e+03 4.0809e+03 7.1079e+02 (3.8155e+03, 4.3464e+03)opt-aiNet 4.1575e+03 2.6350e+03 3.3586e+03 3.9317e+02 (3.2118e+03, 3.5054e+03)RA-AIS 1.5989e+04 9.9303e+03 1.2820e+04 1.3990e+03 (1.2298e+04, 1.3342e+04)AINet-MTD 2.4950e+04 1.6767e+04 2.1432e+04 1.9350e+03 (2.0710e+04, 2.2155e+04)

NReader = 20 RA-GA 6.7580e+03 3.0543e+03 4.7795e+03 8.7519e+02 (4.4527e+03, 5.1062e+03)PSO 4.2295e+03 1.7020e+03 3.0547e+03 6.3127e+02 (2.8190e+03, 3.2904e+03)opt-aiNet 2.8649e+03 1.6946e+03 2.2398e+03 2.5126e+02 (2.1460e+03, 2.3337e+03)RA-AIS 1.3746e+04 9.3950e+03 1.1454e+04 1.1766e+03 (1.1014e+04, 1.1893e+04)AINet-MTD 2.3331e+04 1.4988e+04 1.9350e+04 1.8225e+03 (1.8670e+04, 2.0031e+04)

NReader = 25 RA-GA 4.9208e+03 2.2910e+03 3.4570e+03 5.5865e+02 (3.2484e+03, 3.6656e+03)PSO 3.4766e+03 1.8968e+03 2.5767e+03 3.9153e+02 (2.4305e+03, 2.7229e+03)opt-aiNet 1.9563e+03 1.4521e+03 1.6739e+03 1.2534e+02 (1.6271e+03, 1.7207e+03)RA-AIS 1.1229e+04 8.1853e+03 9.4729e+03 7.7897e+02 (9.1820e+03, 9.7638e+03)AINet-MTD 1.9899e+04 1.3406e+04 1.5959e+04 1.8545e+03 (1.5266e+04, 1.6651e+04)

NReader = 30 RA-GA 3.5480e+03 1.7991e+03 2.5811e+03 4.7316e+02 (2.4045e+03, 2.7578e+03)PSO 2.6905e+03 1.1315e+03 1.9402e+03 4.1469e+02 (1.7854e+03, 2.0951e+03)opt-aiNet 1.4832e+03 1.0519e+03 1.2467e+03 1.0630e+02 (1.2070e+03, 1.2864e+03)RA-AIS 1.0133e+04 6.8882e+03 8.2055e+03 5.8900e+02 (7.9856e+03, 8.4254e+03)AINet-MTD 1.5580e+04 1.0672e+04 1.3330e+04 1.1568e+03 (1.2898e+04, 1.3762e+04)

260 Z. Li et al. / Applied Soft Computing 30 (2015) 249–264

Table 6The total number of queryable tags in case C (the best is in bold).

No. of readers Algorithm Best Worst Mean Std Confidence interval

NReader = 5 RA-GA 4.1244e+04 1.2633e+04 2.9547e+04 8.9090e+03 (2.6220e+04, 3.2873e+04)PSO 2.9583e+04 7.1377e+03 1.7938e+04 5.4869e+03 (1.5889e+04, 1.9987e+04)opt-aiNet 2.4414e+04 6.7994e+03 1.7220e+04 4.7116e+03 (1.5460e+04, 1.8979e+04)RA-AIS 3.8627e+04 1.3760e+04 2.7323e+04 7.2730e+03 (2.4605e+04, 3.0041e+04)AINet-MTD 4.5572e+04 1.6151e+04 3.6344e+04 1.0413e+04 (3.2456e+04, 4.0232e+04)

NReader = 10 RA-GA 2.9226e+04 8.0958e+03 2.1327e+04 4.5154e+03 (1.9641e+04, 2.3014e+04)PSO 1.7097e+04 4.9178e+03 1.0652e+04 2.8698e+03 (9.5807e+03, 1.1724e+04)opt-aiNet 1.2103e+04 4.7786e+03 9.4718e+03 1.4875e+03 (8.9163e+03, 1.0027+04)RA-AIS 3.5367e+04 1.3337e+04 2.8251e+04 4.3861e+03 (2.6613e+04, 2.9889e+04)AINet-MTD 4.5572e+04 1.9778e+04 4.1385e+04 5.1861e+03 (3.9448e+04, 4.3321e+04)

NReader = 15 RA-GA 1.5288e+04 8.8759e+03 1.2249e+04 1.6991e+03 (1.1614e+04, 1.2883e+04)PSO 8.9557e+03 4.6631e+03 6.3962e+03 1.0157e+03 (6.0169e+03, 6.7754e+03)opt-aiNet 6.2555e+03 4.4246e+03 5.3464e+03 4.8271e+02 (5.1662e+03, 5.52670e+03)RA-AIS 2.5763e+04 1.6599e+04 2.1021e+04 2.3982e+03 (2.0126e+04, 2.1917e+04)AINet-MTD 4.3418e+04 2.7191e+04 3.6791e+04 3.4870e+03 (3.5489e+04, 3.8093e+04)

NReader = 20 RA-GA 9.9497e+03 5.4662e+03 7.8315e+03 1.3022e+03 (7.3452e+03, 8.3177e+03)PSO 6.9719e+03 2.8989e+03 4.8265e+03 1.1344e+03 (4.4029e+03, 5.2501e+03)opt-aiNet 4.8413e+03 3.2363e+03 3.7165e+03 3.4612e+02 (3.5872e+03, 3.8457e+03)RA-AIS 2.3777e+04 1.4043e+04 1.8266e+04 2.1183e+03 (1.7475e+04, 1.9057e+04)AINet-MTD 3.9210e+04 2.3732e+04 3.2305e+04 4.1494e+03 (3.0756e+04, 3.3854e+04)

NReader = 25 RA-GA 8.3206e+03 3.9722e+03 5.7013e+03 9.4375e+02 (5.3489e+03, 6.0537e+03)PSO 4.8870e+03 2.1134e+03 3.7895e+03 7.5512e+02 (3.5076e+03, 4.0715e+03)opt-aiNet 3.3322e+03 2.3286e+03 2.6777e+03 2.0710e+02 (2.6003e+03, 2.7550e+03)RA-AIS 1.7376e+04 1.2497e+04 1.5045e+04 1.3150e+03 (1.4554e+04, 1.5536e+04)AINet-MTD 3.4251e+04 2.0330e+04 2.6069e+04 2.9409e+03 (2.4971e+04, 2.7167e+04)

NReader = 30 RA-GA 6.4907e+03 2.3903e+03 4.0047e+03 8.6409e+02 (3.6821e+03, 4.3274e+03)PSO 4.6635e+03 2.1042e+03 3.4725e+03 6.5364e+02 (3.2285e+03, 3.7166e+03)opt-aiNet 2.5688e+03 1.7682e+03 2.1045e+03 2.0600e+02 (2.0275e+03, 2.1814e+03)RA-AIS 1.7107e+04 1.1743e+04 1.3827e+04 1.2249e+03 (1.3369e+04, 1.4284e+04)AINet-MTD 2.6193e+04 1.8698e+04 2.1800e+04 2.0406e+03 (2.1038e+04, 2.2562e+04)

Table 7The total number of queryable tags in case D (the best is in bold).

No. of readers Algorithm Best Worst Mean Std Confidence interval

NReader = 5 RA-GA 4.0613e+04 1.5858e+04 2.7290e+04 6.6731e+03 (2.4798e+04, 2.9781e+04)PSO 3.3877e+04 7.4459e+03 1.7658e+04 5.5532e+03 (1.5584e+04, 1.9731e+04)opt-aiNet 2.6481e+04 9.1660e+03 1.7507e+04 4.4406e+03 (1.5848e+04, 1.9165e+04)RA-AIS 4.2229e+04 1.5452e+04 2.7555e+04 6.9262e+03 (2.4969e+04, 3.0141e+04)AINet-MTD 4.2229e+04 2.2533e+04 3.2817e+04 5.3617e+03 (3.0815e+04, 3.4819e+04)

NReader = 10 RA-GA 3.0825e+04 1.3341e+04 2.0727e+04 4.3376e+03 (1.9107e+04, 2.2347e+04)PSO 1.4255e+04 6.0368e+03 1.0180e+04 1.9317e+03 (9.4584e+03, 1.0901e+04)opt-aiNet 1.2319e+04 6.1392e+03 9.0505e+03 1.6164e+03 (8.4469e+03, 9.6540e+03)RA-AIS 4.6606e+04 1.9823e+04 2.9367e+04 5.9769e+03 (2.7135e+04, 3.1599e+04)AINet-MTD 5.4393e+04 3.0215e+04 4.0311e+04 5.8926e+03 (3.8111e+04, 4.2511e+04)

NReader = 15 RA-GA 1.8181e+04 7.6905e+03 1.2775e+04 2.2680e+03 (1.1928e+04, 1.3622e+04)PSO 1.1669e+04 4.6022e+03 6.6426e+003 1.3514e+03 (6.1379e+03, 7.1472e+03)opt-aiNet 6.7384e+03 4.3670e+03 5.5028e+03 5.7466e+02 (5.2882e+03, 5.7173e+03)RA-AIS 2.7446e+04 1.7900e+04 2.1642e+04 2.7001e+03 (2.0633e+04, 2.2650e+04)AINet-MTD 5.1652e+04 3.3025e+04 4.1256e+04 4.6526e+03 (3.9518e+04, 4.2993e+04)

NReader = 20 RA-GA 1.0856e+04 5.9959e+03 8.4623e+03 1.2877e+03 (7.9815e+03, 8.9432e+03)PSO 6.9815e+03 3.1705e+03 4.9510e+03 9.7512e+02 (4.5869e+03, 5.3151e+03)opt-aiNet 4.4058e+03 3.0485e+03 3.7084e+03 3.3783e+02 (3.5822e+03, 3.8345e+03)RA-AIS 2.2767e+04 1.4552e+04 1.8259e+04 1.9646e+03 (1.7526e+04, 1.8993e+04)AINet-MTD 4.5563e+04 2.9083e+04 3.6583e+04 4.6817e+03 (3.4835e+04, 3.8331e+04)

NReader = 25 RA-GA 7.0567e+03 3.6153e+03 5.3412e+03 8.2210e+02 (5.0342e+03, 5.6482e+03)PSO 5.6622e+03 2.3765e+03 3.8434e+03 8.1296e+02 (3.5398e+03, 4.1469e+03)opt-aiNet 3.8236e+03 2.2823e+03 2.7171e+03 3.0146e+02 (2.6046e+03, 2.8297e+03)RA-AIS 1.6614e+04 1.1960e+04 1.4797e+04 9.9863e+02 (1.4424e+04, 1.5170e+04)AINet-MTD 3.9402e+04 2.1396e+04 2.8046e+04 3.8401e+03 (2.6612e+04, 2.9480e+04)

NReader = 30 RA-GA 6.5668e+03 2.9533e+03 4.1264e+03 7.4078e+02 (3.8498e+03, 4.4030e+03)PSO 4.6758e+03 1.6636e+03 3.3446e+03 6.4996e+02 (3.1019e+03, 3.5873e+03)opt-aiNet 2.4012e+03 1.8545e+03 2.1362e+03 1.5560e+02 (2.0781e+03, 2.1943e+03)RA-AIS 1.5393e+04 1.1097e+04 1.3180e+04 1.1851e+03 (1.2738e+04, 1.3623e+04)AINet-MTD 3.0262e+04 1.8299e+04 2.3215e+04 2.8349e+03 (2.2156e+04, 2.4273e+04)

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 261

of tag

PBima

Fig. 9. Graphical representative

SO, opt-aiNet and RA-AIS, whichever is considered among cases

, C and D. By taking case C as an example, known from Table 6,

f NReader equals 5, AINet-MTD harvests improvements in theean total number of queryable tags by 23.01%, 102.09%, 111.06%

nd 33.02%, respectively, when it is compared with RA-GA, PSO,

s identified by readers in case D.

opt-aiNet and RA-AIS. Similarly, if NReader equals 30, it obtains

improvements by 444.36%, 527.79%, 935.91% and 57.67%, respec-tively. By taking case D as another example, seen from Table 7,if NReader equals 10, AINet-MTD has improvements in the averagetotal number of queryable tags by 94.49%, 296.00%, 345.40% and

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62 Z. Li et al. / Applied Soft C

7.27%, respectively, by comparison with RA-GA, PSO, opt-aiNetnd RA-AIS. Similarly, if NReader equals 20, it has improvementsy 332.30%, 638.90%, 886.49% and 100.35%, respectively. Theseesults demonstrate that the proposed AINet-MTD algorithm canffectively capture better allocation scheme than RA-GA, PSO,pt-aiNet and RA-AIS.

Alternatively, for the sake of clearer observations, these 95% con-dence intervals shown in Tables 4–7 are represented graphicallys supplementary figures in Appendix B.

.3.2. Graphical explanations of performance comparisonsTo further describe the optimal allocation with respect to time

lots and frequency channels, it is necessary to graphically displayhe operating readers and the identified tags. On the basis of resultsn Section 4.3.1, the most complex tag distribution of case D (9locks) is considered for graphical explanation, whose tag distribu-ion matrix is shown in Eq. (19). For the sake of visual observation,ig. 9(a)–(e) depicts the graphical representations of tags identifiedy 15 readers at all time slots for RA-GA, PSO, opt-aiNet, RA-AISnd the proposed AINet-MTD algorithm, respectively. Note that inig. 9(a)–(e), a small black square box denotes a reader, i.e., solidor the operating reader and hollow for the idle reader, and a smalllack circle denotes a tag, i.e., solid for the tag that readers can querynd hollow for the tag out of interrogation range. On the other hand,ll dashed-line circles are used to represent the effective interroga-ion range of readers, and colors are used to differentiate which timelot is considered. In detail, for example in Fig. 9(a)–(e), red dashed-ine circles are used for the first time slot, blue for the second timelot, black for the third time slot, green for the fourth time slot andink for the fifth time slot. For Nreader = 15 in case D, the number ofueryable tags (marked as small black solid circles) by using AINet-TD is 14535 while these figures are 11232 by RA-GA, 7184 by

SO, 2917 by opt-aiNet and 6806 by RA-AIS, respectively. By scal-ng down by a ratio of 1/25, the queryable tags and the unqueryableags are displayed, respectively, as shown in Fig. 9. For the sake oflear illustration, the interrogation ranges of readers at each timelot obtained by RA-GA, PSO, opt-aiNet, RA-AIS and AINet-MTDre given as supplementary figures in Appendix B, respectively.bviously, it can be further concluded that the proposed AINet-TD algorithm has better performance in solving R2RCAM-MTD

han RA-GA, PSO, opt-aiNet and RA-AIS.

.3.3. DiscussionsThe purpose of this paper was to investigate how readers in an

FID system would be escaped from mutual collision between thems far as the multiple-density tag distribution is concerned. Thendings clearly suggest that the optimal resource allocation withespect to time slots and frequency channels is helpful to alleviater avoid reader-to-reader collision. The RFID system to which theroposed AINet-MTD algorithm is applied could interrogate moreags when compared with those systems that other algorithms aresed. At a given time slot, it is found that proper frequency separa-ion among neighboring readers helps readers to own the desirednterrogation ranges without collision. Another reason for moreueryable tags may be that the proposed AINet-MTD algorithm isble to search more accurate global optimal solutions than otherlgorithms. This supports and expands the findings in [23], whereimilar results are shown for an RFID system in an example of sec-ionalized warehouse management. This paper has taken a step inhe direction of studying the reader-to-reader collision problemn RFID systems with non-uniformly distributed tags. It did how-ver look at a limited number of tag density levels (i.e., 1, 2, 4 and

) located in a single storey warehouse. It may be the case thatFID systems located in other warehouses – for example, stereo-copic or multi-storey warehouse – would not benefit in the sameay. The model and algorithm in this paper should be replicated

ting 30 (2015) 249–264

with RFID systems with unlimited tag density levels, as well as inother types of warehouses in order to be able to recommend theuse of R2RCAM-MTD and AINet-MTD for all practical warehousemanagement applications.

On the other hand, the findings also suggest that the pro-posed AINet-MTD algorithm is effective and efficient for solvingR2RCAM-MTD and its extended models. To be specific, the pro-posed AINet-MTD algorithm performs well in dealing with suchkind of problems that can be transformed as optimization prob-lems. By means of such operators in the immune network as clone,mutation and suppression, the proposed algorithm is able to beextended to apply in other practical optimization problems.

5. Conclusions

This paper established a reader-to-reader collision avoidancemodel with multiple-density tag distribution (R2RCAM-MTD) fromthe viewpoint of resource scheduling, and proposed an efficientartificial immune network (AINet-MTD) to solve R2RCAM-MTD. Inthe simulation experiments, the effects of such parameters as timeslots and frequency channels are investigated, and the algorith-mic performance comparisons are conducted among the proposedAINet-MTD, RA-GA, PSO, opt-aiNet and RA-AIS. Based on the the-oretical analyses and simulation results, some conclusions canbe made. (a) The established reader-to-reader collision avoidancemodel with the multiple-density tag distribution (R2RCAM-MTD)is actually an optimization problem with respect to resource allo-cation. It is compatible with the existing reader collision avoidancemodels that the single-density tag distribution is considered. (b)The total number of tags that readers can query increases almostlinearly as more available time slots are provided. Meanwhile, thetotal number of tags that readers can query grows up nonlinearlyas more available frequency channels are provided. (c) The totalnumber of queryable tags firstly increases and then decreases asthe number of reader increases. Accordingly, there always exists apeak for a proper number of readers. (d) The RFID system in whichAINet-MTD is used to solve R2RCAM-MTD can query more tagsthan those by using other algorithms (i.e., RA-GA, PSO, opt-aiNetand RA-AIS). In other words, the reader-to-reader collision can beeffectively avoided or alleviated.

However, some potential research issues on RFID reader col-lision problem in the future are possibly focused on. (a) How toreduce the time consumption caused by evolutionary algorithms.(b) How to mitigate the reader collision problem with fixed and/ormobile readers. (c) How to allocate again the communicationresources to readers when there are additional or abnormal read-ers. (d) How to extend the multi-density tag distribution towardbroader RFID applications.

Acknowledgements

This work is partially supported by the National Natural ScienceFoundation of China under Grants Nos. 61201087 and 61471122,the Guangdong Natural Science Foundation under Grant No.S201301001182, the Science and Technology Program of Huizhouunder Grant No. 2013B020015006, the Science and Technology Pro-gram of Zhaoqing under Grants Nos. 2012G027 and 2013C003, andthe Science and Technology Program of Guangzhou under Grant No.2014J4100001. We would like to thank five anonymous (unknown)reviewers and the editors for their comments.

Appendix A.

For the sake of clarity, the symbols/variables used in R2RCAM-MTD are listed and their descriptions are given in Table A1.

Z. Li et al. / Applied Soft Computing 30 (2015) 249–264 263

Table A1Symbols and their descriptions in R2RCAM-MTD.

Symbol/variable Description

�b� The normalized spectrum power� The path-loss exponentˇmask(•) The function of the spectrum maskωj(k) A two-valued function indicating whether Rj is

operating at the kth time slot�i(k) The wavelength of Ri at the kth time slot�CH The channel difference� The distribution density of tags�0 The reference tag distribution density�A The tag distribution density matrix for case A�B The tag distribution density matrix for case B�C The tag distribution density matrix for case C�D The tag distribution density matrix for case D�(p,q) The tag density in the block located at block

coordinate (p,q)BPi(k,x) The backscatter signal power received by Ri in the

kth time slot at the distance of x from the target tagCH The set of ID numbers of frequency channelsCHi(k) The channel number selected by Ri at the kth time

slotCHj(k) The channel number selected by Rj at the kth time

slotd2,1 The distance from desired reader R1 to interfering

reader R2

dj,i The distance from desired reader Ri to interferingreader Rj

dmin The lower bound distance in the constraintcondition

ETag The effective power reflection coefficient of the tagGT The reader antenna transmitting gainGR The reader antenna receiving gainh The fading coefficientIi(k) The total interference power received by Ri at the

kth time slotIj,i(k) The interference power received by Ri at the kth

time slot from Rj

NT The total number of queryable tags of the RFIDsystem

Md The modulation depthNTi(k) The number of queryable tags obtained by Ri at the

kth time slotNReader The total number of readers in an RFID systemNFreq The number of available frequency channelsNslot The number of available time slotsNS The set of ID numbers of time slotsNR The set of ID numbers of readersNoisei The noise power measured at Ri

P × Q The number of blocksPLoss The reference path loss at the distance of 1 mPi The signal power of Ri

Pj The signal power of Rj

Pmin The minimum power required for a tag to operateR1 The desired readerR2 The interfering readerRi The ith desired readerRj The jth interfering readerr1 The effective interrogation radius of the desired

reader R1

r2 The effective interrogation radius of the interferingreader R2

rinterfer The interference radiusrmax,i(k) The maximum interrogation radius of Ri at the kth

time slotrmax,1 The maximum interrogation radius of R1

ri(k) The effective interrogation radius of Ri at the kthtime slot

SINRmin The desired minimumSignal-to-Interference-plus-Noise Ratio (SINR)

SINRi(k, x) The backscatter signal power received by Ri at thekth time slot at the distance of x

Si(k,p,q) The effective interrogation area of Ri in the block of�(p,q) at the kth time slot

x The distance from the desired reader to the targettag

Table A2Symbols and their descriptions in the proposed AINet-MTD algorithm.

Symbol/variable Description

� The percentage of removed antibodiesThs The dynamic suppression thresholdAB The antibody populationAb The antibody stringABRemove The removed antibodyABRemian The remain antibodyABu The uth antibodyABclone(C) The Cth cloned antibody of the uth antibody ABu

ABv The vth antibodyAffinity(•) The affinity functionAff old The mean affinity value of the previous generationAff new The mean affinity value of the current generationb The percentage of recruitment antibodiesCHw(k) The selected bit which is denoted the reader Rw at

the kth time slotCH’w(k) The mutated result of CHw(k)Du,v The hybrid distance measure between Abu and Abv

Euclidean(Abu , Abv) The function value of the Euclidean distancebetween Abu and Abv

NIni The initialized number of antibodiesNClone The prefixed cloned multiplierNmax The upper limit of the candidate antibody

populationSim(Abu ,Abv) The similarity between the uth and the vth

antibodySR The reader set with a distance less than dmin

outside MRt The tth iteration

[

[

[

[

tmax The maximum number of iterationsWR The waiting reader setMR The mutated reader set

Likewise, the symbols/variables in the proposed AINet-MTD algo-rithm and their descriptions are provided in Table A2.

Appendix B. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.asoc.2015.01.056.

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