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A novel-Q DFSA algorithm for passive RFID system
Accepted Manuscript A novel-Q DFSA algorithm for passive RFID system Tanvi Agrawal, P.K. Biswas, Rohit Sharma
PII: DOI: Reference:
S1574-1192(17)30265-1 http://dx.doi.org/10.1016/j.pmcj.2017.05.004 PMCJ 836
To appear in:
Pervasive and Mobile Computing
Received date : 21 November 2014 Revised date : 18 February 2017 Accepted date : 17 May 2017 Please cite this article as: T. Agrawal, P.K. Biswas, R. Sharma, A novel-Q DFSA algorithm for passive RFID system, Pervasive and Mobile Computing (2017), http://dx.doi.org/10.1016/j.pmcj.2017.05.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A Novel-Q DFSA Algorithm for Passive RFID Systems Tanvi Agrawal, P. K. Biswas, and Rohit Sharma# Department of Information & Technology, NITIE, Mumbai #
Department of Electrical Engineering, IIT Ropar, Rupnagar
[email protected]; [email protected]; [email protected]
Abstract
The tag detection ability of Passive Radio Frequency Identification (RFID) systems are critically challenged by the collision occurrence due to simultaneous responding tags during the identification process.The dynamic scheduling of the frame size governed by Dynamic Frame Size ALOHA(DFSA) process, by adjusting the frame lengths according to the size of tag population can avoid the collisions during the identification.However,the performance of DFSA majorly depends on the frame size selection policy which in previous studies was adopted to achieve the target of throughput maximization during a frame. This condition is obtained at the cost of equating the frame size up to the number of estimated tags responding during the time frame. This approximation enhances the throughput value but contributes to massive energy wastages as the frame lengths approach to a very large value in large tag population size. Therefore, it is essential to develop the new frame size estimation policy for DFSA achieving the aim of optimization between throughput and energy for improved time and energy performance. In this paper, we have proposed an EPC C1G2 standards based Novel-Q DFSA algorithm which optimizes the frame size accounting both the energy and the throughput. The combined throughput and energy trade-offs are measured through Energy Time-Delay (ET) cost which is minimum for our proposed algorithm compared to the existing solutions. Furthermore, the Throughput-Time Delay product approves the stability in large population size making it suitable for numerous identification applications. Keywords: Dynamic Frame Slotted ALOHA, Enhanced Dynamic Frame Slotted ALOHA, Electronic Product Code - Class 1, Generation 2, Frame Slotted ALOHA, and Radio Frequency Identification (RFID).
1 Introduction RFID technology is currently being deployed in large-scale identification applications i.e. Wal-Mart, Sam’s Club, US Department of Defence for automated inventory identification [1]. Identification through RFID provides the benefit of enhanced product visibility, and availability without the line of sight requirement, with minimal human efforts compared to the existing barcode technology [1]. The RFID system consists of reader, tags, and the server, where the reader capture information from the tags affixed to the items,using radio frequency waves and transmits it to the server [2]. The RFID systems are broadly classified into Active and Passive RFID systems based on the tags classification being utilized. The active RFID systems exploit the active tags which have the on-chip energy sources, while the passive RFID systems use passive tags which do not bear the self-containing energy source [3].The Electronic Produc Code(EPC) Class-1,Generation-2(C1G2) based passive RFID tags are gaining the popularity in large scale identification applications due to the associated lower cost compared to its counterpart[1]. However, these passive tags are more susceptible to collisions compared to the actve tags due to limited computing and memory capability making them incapable of sensing the neighbouring tags responses on the channel [4].These collisions can reduce the reading efficieny creating a major concern about the accuracy achieved during the identification process.The ALOHA algorithms attempts to reduce the simultaneous tag responses through time-based scheduling [5]. These ALOHA based anti-collision algorithms are highly suitable in passive RFID environment case due to lower bandwidth, and memory requirement with simpler reader designs [6].ALOHA algorithms are subdivided into Frame Slotted ALOHA (FSA) and the Dynamic Frame Slotted ALOHA (DFSA) based on the frame size selection policy.
The reader transmit the numbr of time slots based on the frame size selection decision, and the tags in turn respond during any time slot of that frame through random selection.Based on the tags answer, the slot can be filled with one tag response,multiple tags response, and no tag response resulting in the success slot (s), collision slot(c), and the emplty slot(e). In FSA, the time frame value transmitted by the reader to the tags is fixed.This however, can degrade the throughput achieved if the number of contending tags increase or decrease beyond the fixed time frame value limit during a frame [7]. In DFSA the frame size is dynamically modified in accordance with the number of tags responding during the time frame.This further, maximizes the throughput achieved during a frame by equating the frame size up to the number of responding tags estimation ( Nest ) based on the s , c and e statistics. However, the energy consumed by the DFSA is higher compared to the FSA as the frame size is equated to the Nest ,which in turn increases the energy consumption value during the frame due to direct proportional relation between the energy and the frame size[8]. In fig. 1 we have shown the dynamic scheduling of DFSA for frame size estimation based on the s, c, and e values utilized for tags estimation. Frame 1 1
2
Frame 2 3
1
2
Frame 1 3
4
1
2
Tag 1 Tag 2
s s
. . . . . . .
c
e
Tag N
Fig.1 DFSA Operation
The performance of DFSA is challenged by the two factors, the Tag Population Estimations, and the Frame size Approximations.In previous studies of DFSA development in [4], [6-7], and [9-24] the performance of DFSA was improved by adopting various tag estimation methods. However, the frame size approximations were commonly adopted to satisfy the n N est offering maximum optimal throughput up to 36.8% during a frame [25]. This further, gives rise to energy consumption in large tags environment as the number of collisions also increase with frame sizes approaching to a very high value [26]. These collisions waste the number of time slots during a frame and thus, the energy by not giving the
useful information in the form of
identity.Also,the energy during a time frame is directly proportional to the number of time slots or the time delay consumed during a frame [27]. Therefore, increasing the frame size n up to the number of estimated tags may achieve the maximal throughput values but also affect the energy budget in denser tag population. During the time frame of length n and N tag contenders, the dependence of both the energy E ( n, N ) and the throughput Th ( n, N ) on n necessitates the selection of new optimization goals for frame size estimations. The frame size optimization attemps proposed earlier in [24], and [28].In [24] the author suggested that irrespective of increasing the frame size indefinitely up to the number of tags the number of responding tags must be restricted to achieve the maximal throughput condition. Therefore, the author fixed the maximum frame length up to 256 and allowed the tags to answer in groups until all identified. This, however, requires additional reader and tags computation creating implementation concerns for low-cost passive RFID systems with limited functionality[4]. In [28] an improved DFSA algorithm was proposed which utilizes new frame size estimator satisfying the energy and throughput trade-off criteria. The algorithm achieves better energy savings and improved throughput conditioned by allowing the tags to respond in a limited number of slots irrespective of restricting them to groups as in [23]. This again needs additional computational efforts by the reader and the tags causing implementation concerns for low-cost passive RFID systems. Therefore, the need is to develop the new frame size estimator with minimal computational efforts for improved throughput and energy performance for EPC C1G2 standards based passive RFID systems. In this paper we have proposed a Novel Q-DFSA algorithm which proposes a new optimal frame size estimator by minimizing the Energy and Time Delay product. As
time delay minimization is essentially equivalent to the throughput maximization serving the purpose of maximum possible throughput outcome [4]. The algorithm achieves the minimum energy consumption and the substantial throughput value obtaining minimum Energy-Time-Delay (ET) cost. Further, the analytical results are tested on Network Simulator NS-3 for performance comparisons in real time simulation test bed. According to [29] slotted ALOHA algorithms are subjected to instability if the number of unidentified tags exceeds the number of available slots. The number of retransmissions increase if the frame length is not selected appropriately increasing the time delay [19].Henceforth it is essential to analyze the stability of the Novel-Q DFSA employing new optimal frame size estimator. The Throughput Time Delay product for the proposed algorithm confirms the stability in considerable tag population value. The remainder of the paper is organized as follows: Section 2 is the EPC C1G2 standards; Section 3 is the Background Work;Section 4 reports the Novel-Q Algorithm; Section 5 is Performance Evaluation followed by the Conclusion in Section 6.
2 EPC C1G2 Standards The ALOHA process of tag collisions arbitration during identification for passive tags are governed by the EPC C1G2 standards[21].The EPC Global industry defined the set of standards for the UHF passive RFID systems operating between 860-960 MHZ bands. These standards support for both the Fixed Frame and the dynamic frame structure by the Q parameter selection during the tag identification process. The EPC C1G2 standards based identification procedure is given below.
The reader initiates the Identification Frame with Query command. The Query consists of Q Parameter indicating the number of time slots contained in a frame ranging between [0,15]. The tags in the vicinity of the reader generate random numbers between 0, 2Q 1 .
The tags generating a random number as zero will answer with a 16-bit random number (RN-16) to the reader. Further slots may result in success (s), collision(c), and empty (e) based on the RN-16 responses generated by either the single, multiple or no tags during a frame.
The reader acknowledge the tags identified in success slots through ACK command,and the tags in response to ACK transmit full tag identifier or EPC (Electronic Product Code) to the reader.
The reader may initiate the consecutive time slots either with QueryRep or QyeryAdjust commands. The QueryRep repeats the same Q parameter transmitting the same time frame length while the QueryAdjust changes the Q parameter indicating a change in frame length.
In EPC C1G2 standards based DFSA the frame size is adjusted dynamically either slot by slot, or frame by frame by adjusting Q value known as Q-slot, and Q -frame DFSA operation. The Q value is changed by adding or subtracting the C parameter based on the collision (c) and empty slot (e) information available to the reader during a frame [21]. In case the slot is in a collision the C value is added to the Q while, in the case of empty slot the C value is subtracted from the Q value from previous slot. The range of C lies between 0.1, 0.5 and the modified Q value is rounded up to the nearest integer for frame adjustments. Fig.2 illustrates the working principle of EPC C1G2 standards based DFSA algorithm for 3 tags. During the first slot only tag 1 transmits the RN-16 value resulting in successful slot (s) and the reader, in turn, sends back the ACK in response to RN-16. In second slot tag-2 and tag-3 simultaneously send the RN-16s resulting in a collision (c). In response to this, the reader may send the QueryAdjust or the QueryRep commands in order to inform the change or no change in frame based on Q adjustments. During the third slot, no tag send back the responses to the reader leading to an empty slot (e). Here T1 is a reader to tag response time, T2 is a tag to reader response time, and T3 is the time required by the reader to issue another command after waiting time of T1 [21].CW represents the continuous wave energy which is transmitted by the reader to energise the passive tags in between the idle time.CRC-16+OID is the combination of object identification number along with 16 bit Cyclic Redundecy Checksum (CRC-16) known as the EPC of the tag.
Fig.2 EPC C1G2 standards based Q-DFSA example
3 Background Work The performance of DFSA is governed by the two factors one is the tag estimation ( N est ) and another is the frame size n approximation. The tag estimation methods have widely been explored by the authors in [4], [6-7], and [9-24]. The tag estimation methods considered by the previous authors are categorized as Error Minimization, Heuristic, and Maximum Likelihood approach. Error minimization methods were deployed in [14], [15], and [24] which minimize the squared error between expected c e s and actual values of success collision and empty slots. The Vogt [14]
approach of tag estimation is computationally feasible henceforth widely utilized for EPC C1G2 standards based passive systems. In [16], [18], and [19] the authors maximize the likelihood of conditional success probability during a frame. In recently proposed Multiframe Maximum Likelihood DFSA (MFML-DFSA) algorithm in [19] the maximum likelihood estimations were computed based on the multiple frame
statistics. This further achieves better accuracy compared to the other existing maximum likelihood estimations. The algorithms developed in [4], and [9-13] counts on a formula based estimations known as a Heuristic method and adjust the frame length to the nearest power of two [17]. However, the performance of the heuristic approach is poor compared to the existing error minimization estimators [30]. The algorithms developed in [6-7], [20-23], and [28] belong to EPC C1G2 standards based Q-DFSA family in which the frame size is optimized by updating the Q parameter without utilizing tag estimation techniques. In basic DFSA (increase) method proposed in [7] reader initiates the interrogation round with minimum frame size as 4 and increase frame size until the single tag is identified. In the case of single tag identification the reader stops the cycle and starts to read with initial minimum frame size. In [22] much-improved Q algorithm was proposed which optimize the frame size utilizing two different constants for collision and empty events as Cc , and Ci unlike Q-DFSA which use only parameter C . If the slot is in a collision Cc is added while in the empty slot event Ci is subtracted from original Q value this further improved the throughput achieved during the frame compared to the existing Q-DFSA algorithms. Table-1 illustrates the previously developed DFSA algorithms with detailed frame size optimization and tag estimation ( N est ) methods adopted in previous studies. Conclusively, it can be said that the tag estimation methods may differ however, the frame size optimization method commonly applied by the authors in [4], [6-7], and [924] were either the throughput maximization or the delay reduction resulting in However, the frame size approaches to a very high value for
n N est
n N est
.
in large number of
tags environment [26]. The optimization of frame size based on the energy consumption perspective has not been paid attention in previous studies which play a vital role in large tags size environment.In [23], and [27] the authors attempt to solve the indefinite frame size problem with existing frame size estimator leading to energy reductions.Although the reductions in frame size is achieved by by allowing the tags to answer in the group. This, however, requires complex reader and tags computations not compatible with resource constrained passive RFID systems.The table -1 shows the DFSA algorithm and the background work based on the frame size optimization and the tags estimation methods.
Table-1 DFSA algorithms Background Work Algorithm
Frame Size Estimation (n)
PS[4] Schoute[9]
Optimization Method Throughput Maximization Throughput Maximization
Cha and Kim[10]
Throughput Maximization
n Nest
Heuristic
Optimization Outcome Frame size is fixed
n Nest
Tag Estimation(Nest) Method Heuristic Heuristic
Wang [11]
Throughput Maximization
n Nest
Heuristic
Kodialam NandaGopal [12]
Throughput Maximization
n Nest
Heuristic
ASAP [13]
Throughput Maximization
n Nest
Heuristic
Vogt[14]
Throughput Maximization
n Nest
Error Minimization
SbS[15]
Throughput Maximization
n Nest
Error Minimization
EDFSA[24]
Throughput Maximization
n Nest
Error Minimization
Floerkemiere[16]
Efficiency Maximization
n Nest
Maximum Likelihood
Chen and Hung [18]
Efficiency Maximization
n Nest
Maximum Likelihood
MFML-DFSA[19]
Throughput Maximization
n Nest
Maximum Likelihood
Optimal DFSA[20]
Time-Delay Minimization
n Nest
-
Q-DFSA [21]
s,c,e statistics
n Nest
-
Joe and Lee[6]
s,c,e statistics
n Nest
-
s,c,e statistics
n Nest
-
Slot Count [23]
s,c,e statistics
n Nest
-
DFSA (increase)[24]
s,c,e statistics
n Nest
-
Q+ -[22]
Agrtawal et al.[28]
Energy-Throughput Optimization
n
N 1 0.3010
-
4 Novel Q-DFSA Algorithm In this section, we describe our Novel Q-DFSA algorithm which follows the EPC C1G2 standards based tag identification process known as Q-DFSA algorithm.The Novel QDFSA works in Q-slot manner, by changing the Q value slot by slot, dynamically for frame lengths adjustment.The Novel Q-DFSA employ a new frame size estimator achieved through energy and time delay product optimization. The frame size estimator proposed for Novel Q-DFSA will help the reader to analyze the estimated value of n according to the tag population value, and then the integer value to the nearest power of two will be identified for 2Q approximation.The reader then communicate the Q value to the tags for estimated frame size and the tags based on Q value can generate the random numbers between 0 and 2Q-1 during the time slots of the frame.
4.1 Optimal Frame Size Formulation Let
PRTX , PRRX , PTTX , and PTRX
be the reader transmission, reader reception, tag
transmission and the tag reception power respectively.Further, TQUERY be the time consumed in Query transmission, and L be the number of bits transmission during a frame of length n in N tag population value.The value of E ( n, N ) [33], throughput
Th(n, N ) [4], and time delay T (n, N ) [4] are given by the equations 1, 2, and 3.
E (n, N ) TQuery ( PRTX PTRX ) n{L( PTTX PRRX ) PRTX PTRX }
Th(n, N )
N 1 (1 ) N 1 n n
(2)
T ( n, N )
n 1 (1 ) N 1 n
(3)
(1)
In equation (1), TQUERY , PRTX , PRRX , PTTX , and PTRX are the predefined and fixed while,
L is variable and is inversely proportional to Th(n, N ) as higher throughput results in more success and lesser number of transmissions during the frame. Therefore, reframing the energy equation by replacing fixed values with a proportionality constant k is given as followed in (4). E (n, N ) kn *
1 kn 2 1 Th(n, N ) N (1 ) N 1 n
(4)
The throughput maximization is equivalent to delay reduction according to [4] therefore, our new optimization goal F minimizes the T (n, N ) , and E ( n, N ) multiplicative for optimized n ( n * )formulation given by (5). n* arg min [ F ] arg min [ E (n, N )*T (n, N )] n(1................215 )
(5)
n(1................215 )
Solving (5) for n * by keeping values from (3), and (4) we get the F as given by (6). F min[k ][( n
kn *2 n* )]( )] 1 N 1 1 N 1 N (1 ) (1 ) n n
(6)
Computing
n*
dF and equating it to zero we get the n * value as given by (7). dn
2N 1 3
(7)
4.2 The Comparative Analysis of Frame Size Estimators The previous attempts of DFSA development have employed the frame size estimator as n N for frame lengths adjustments.However, this leads to excessive collisions and energy consumption when the tags population becomes large. In [28] we have proposed a new frame size estimator as n
N 1 satisfying throughput and energy trade-off 0.3010
criteria. The estimator yields reduced energy values however, throughput values were limited creating stability concerns in huge tags environment. As in the case of lower throughput values increased number of retransmissions threaten the stability during a frame [29].In table-2 we have compared the two frame size estimators with our proposed one for n value comparisions. According to [19] there is a certain range of N corresponding to each n for which maximum stable throughput value is achieved and after this value the throughput starts to degrade during a frame as shown in fig. (3). Table 2: n vs. N Range
nN
N
n
4
2
4
3
8
6
16
11
32
22
64
43
128
86
256
171
512
342
1024
683
2048
1366
1-3 3-6 6-11 11-22 22-44 44-89 89-177 177-355 355-710 710-1420 1420-2839
2N 1 3
n
8 8 8 16 32 32 64 64 128 128 256
N 1 0.3010
Fig.3 Number of tags vs. Throughput
4.3 Stability analysis From fig. 3 it is clear that the maximum achievable throughput value is up to 36.8% for n N estimator and lower for smaller n value estimations. The condition worsens in
high tag population scenario as lower throughput values result in higher collision and retransmissions leading to instability conditions. Therefore, it is essential to analyze the stability for lower values of n proposed by our frame size estimator. In this section, we have established a condition of stability with the aim of achieving maximum throughput condition with lower n value estimator. Let mi denote the number of backlogged packets during the ith frame then mi (i 0) can be modeled through Markovian Chain and will only be stable if it is
aperiodic and irreducible [33]. Let λ be the expected number of arrivals in a slot and
be the backlogs ratio defined as
n m m mi n with i 1 condition, then the two sufficient
condition for mi (i 0) to be aperiodic and irreducible is given as follows according to [34] by (8) and (9). (i)
E[(mi 1 mi mi ]
(8)
(ii)
lim n * e ) 0
m
(9)
Here E is the expectation value of backlogs during a slot in a frame. In fig. 4 we have shown the number of unidentified tags and backlogged packets at t and t t timeslot represented with ( N t , mt ) and ( N t t , mt t ) pairs during a frame of length n .
N t , mt
-
e
s
N t t , mt t
c
s
e
c
-
n Fig. 4 Time Frame
During the frame, every collision slot generates at least two backlogs as minimum two tags would have answered in that slot and every success slot leads to the reduction of one backlog due to one identified tag [14]. Therefore at t t time number of tags
N t t N t s while mt t mt 2 * c .Henceforth it is essential, to control the backlogs as it increases at much faster rate compared to the success rate leading to the reduction in the number of arrivals. Lemma-1 The backlog increase with lower frame size values n m or n m and can only be decreased by increasing time frame maximum up to the backlogs approaching n m . The stability of a frame is challenged with n m condition giving rise to backlogs as shown by (10) according to [35]. m
m ) lim ( m
limO(m) lim m e
m
m
m
m e m ) ) 0 lim (O(m) m e m ) ) 0 m m )
m m )
limO(m) lim m e m
m
m m )
limO(m) lim m e m
m
(10)
m m )
(11)
From equation (11) it is clear that the stability condition is not fulfilled with n O m
as m e
m m )
value increases exponentially for O m or n m condition. Further
expanding the stability equation for n m we get the result as follows.
m
m
m m m) lim[( ) e m ) ] 0 lim[( ) e m ) ] 0 m m m) m)
(12)
The eq. (12) shows that the inequality holds true for n m balancing the stability equation while not for the n O m as shown by (10). Let r be the new arrived packets, f be the number of success packets, m be the backlogged packets. Further, the probability of f+r arrivals is represented as f r and .
the probability of exactly f success out of m backlogged packets during a frame is represented as
m , f
. Then, the transition probability of backlogs increase from m to m+r
in total f+r arrivals is represented by Pm, m+r given by (13) according to [35].
Pm,mr
min(m,n)
f 0
f r m, f r 1
(13)
The
Pm ,m r
m , f
can be increased more, compared to
can only be decreased if for increased number of backlogs from m to m+r,
success. The value of
m , f
( nm )(m!) nm 0 0
Here G Q, h Q h
The
m , f
resulting in more backlogs converting in
is given as follows for different arrival conditions.
( nf )( mf ) f!G(n f, m f) m , f
f r
h
x 1
x 1
j 0
0
(1) x [(h j)(Q j)](Q x)(h x)
(14)
1 x!
(15)
0
decreases to zero when
f min m, n
, and f n m .Therefore, when the number of
arrivals exceed the backlogs the success rate degrades and approaches to zero. Nevertheless,
m , f
is higher for n m compared to n m as the value of
( nm )(m!) ( )( )f!G(n f,m f) , and upsurges for this condition. Henceforth decreasing nm n f
m f
success rate for n m condition leads to increased
Pm ,m r
value and further approaches
to zero in case the number of arrivals becomes greater or equal to m . Therefore, whenever the stability condition is deviated it is essential to increase
n
maximum up to
m in order to minimize the number of arrivals compared to backlogs.
Lemma-2 The condition of stability is satisfied only when
s cs
during a frame.
Let ps be the probability of success during frame size of n slots then according to [34] number of arrivals
t
at any time instant t must be lesser than
lim n nps
during a frame for a system to be stable as given by (16).
t lim n nps
(16) t
The value of
s
ps
during a frame at t time is defined as the success ratio as (
i 1
n
)
therefore, keeping the value in (16) the condition reduces to as follows given by (17). t
t lim n n
s i 1
n
t
s
lim
(17)
n i 1
Further, the value of t at
t
time instant is proportional to the difference of collision t
t
i 1
i 1
and success slots thus, t c s during a frame at time t . As the difference represent the reduction of backlogs to the number of arrivals, therefore, replacing the t value and rewriting the rewriting the stability condition as given by(18). t
t
t
i 1
i 1
i 1
c s s
(18)
Combining results from lemma-1 and lemma-2, conclusively it can be said that whenever
t
t
t
i 1
i 1
i 1
c s s condition
arrives stability condition is deviated as the
success rate is reduced. This, however, can only be corrected by increasing the frame size maximum up to the order of backlogs during a frame.
4.4 Implementation of Novel-Q DFSA The Novel Q-DFSA is based on the EPC C1G2 standards based Q-DFSA algorithm and works in Q-slot manner as the frame size is changed dynamically slot by slot. In QDFSA the Q-slot frame size modification is based on collision slot ( c ), and empty slot ( e )information. It is increased if the slot is in the collision and decreased in case of the empty slot by adding and subtracting the C parameter. However the value of C is not clearly defined in the standards. In Novel-Q DFSA the frame size is updated slot by slot by analyzing the stability condition check established by lemma-2 of s c s unlike QDFSA .If it is not matched then the frame size is changed based on the difference of s , and c s i.e. n is increased by c 2s value. This further minimizes the gap between backlogs and the arrival rate to meet the stability criteria as s represents the arrival rate while c s represents the backlogs during a frame. The frame size is increased by this difference until the stability condition is satisfied with maximum value set by the proposed frame size estimator n
2 N est 1 .Henceforth Novel-Q DFSA starts with 3
the minimum frame size and increases maximum up to n
2 N est 1 estimator. This 3
further achieves the goal of maximum energy savings along with the optimal throughput outcome to maintain the stability during a frame. In Novel Q-DFSA N est at kth frame k
k
k
j 1
j 1
j 1
( N ( est )k ) is computed as N ( est ) k 2 c j s j , here c j is the total number of k
collision slots and
s j 1
j
is the total number of successes up to the kth frame. According k
to [15] minimum, two tags would have replied during collision slots, therefore, 2
c j 1
j
is the minimum bound on the total number of collision slots up to the kth frame. Out of these collision slots, some of the slots turned successful due to the number of tags k
k
j 1
j 1
identified during those slots. Therefore, 2 c j s j yields the minimum bound on tags estimation during a frame k . The flow chart of Novel-Q DFSA is shown in fig. 5.
Fig. 5 Flow Chart Novel-Q DFSA Algorithm
5 Performance Evaluation The Novel Q-DFSA performance primarily depends on the proposed frame size selection method which optimizes the throughput and energy values both with minimal computational overheads. As Novel Q-DFSA increase the frame size by 2c s in every slot whenever the stability condition is violated to maximum value up to
k
k
j 1
j 1
2(2 c j s j ) 1 3
for n
2 N est 1 Henceforth, the maximum possible number of 3 . k
k
j 1
j 1
2(2 c j s j ) 1 iterations during a frame k
k
k
2 c j s j N ( est )k therefore j 1
can be
3
k
k
j 1
j 1
2(2 c j s j ) 1
j 1
3
n( c 2 s ) .Further as
n( c 2 s ) O ( N est ) k until the
condition of stability is achieved. Therefore, the maximum number of computations required for Novel Q-DFSA during a frame is O( N est ) . The performance of Novel Q-DFSA has been evaluated on MATLAB 8.0 and is tested on NS-3 discrete network simulator. Further the results are obtained by averaging the data over 100 simulations for better accuracy. The simulation environment comprises of 100-1000 tags with the single reader and is in compliance with EPC C1G2 standards .The tags have 96 bits long tag ID and are assumed to be stationary until identified. The energy consumption values are computed utilizing the power consumption values given in table-3 based on the transceiver and transponder power specifications given in [36] and [37].The time specifications are given in table-4 based on the EPC C1G2 standards based parameters [21]. For performance comparisons we have considered DFSA (increase) [8], Vogt [15], MFML-DFSA [20], Q+-[24], and EDFSA [29] algorithms.
Table-3 Power Consumption Values
Reader
Tag
PRtx
PRrx
PTtx
PTrx
501 mW
104 mW
4.2 µW
12.5 µW
Table 4: EPC C1G2 Specifications
SYMBOL READER TO TAG DATA RATE TAG TO READER DATA RATE RISE TIME/FALL TIME
SIGNIFICANCE TRANSMISSION RATE TRANSMISSION RATE READER POWER UP WAVEFORM RISE TIME/ READER POWER DOWN WAVEFORM FALL TIME DIVIDE RATIO
VALUE 40 KBPS 40 KBPS 500 µS
TARI
REFERENCE TIME INTERVAL OF DATA 0 FROM READER TO
25µS
DATA 0
TIME INTERVAL OF DATA 0
1 TARI
DATA 1
TIME INTERVAL OF DATA 1
1.5 TARI
RTCAL
READER TO TAG
DR
8
TAG DATA SIGNALLING
TRCAL RTPREAMBLE T1 T2 T3
62.5 µS CALIBRATION PARAMETER TAG TO READER 75 µS CALIBRATION PARAMETER READER TO TAG PREAMBLE 175 µS READER TRANSMISSION TO 175 µS (MIN) 225 µS (MAX) TAG RESPONSE TIME TAG RESPONSE TO READER 87.5 µS TRANSMISSION TIME TIME READER WAITS AFTER 87.5 µS T1 INTERVAL TO ISSUE ANOTHER COMMAND
T4 TRPREAMBLE Select
MINIMUM TIME BETWEEN READER COMMANDS TAG TO READER PREAMBLE Time Consumed in Select
137.5 µS 162.5 µS 6.3 ms
Transmission Query
Time Consumed in Query
0.8375 ms
Transmission Queryrep
Time Consumed in
0.2125 ms
QueryRep Transmission QueryAdjust
Time Consumed in
0.375 ms
QueryAdjust Transmission ACK
Time Consumed in ACK
0.6625 ms
Transmission NAK
Time Consumed in NAK
.325 ms
Transmission RN-16
Time Consumed in RN-16
0.6625 ms
Transmission EPC
Time Consumed in EPC Transmission
10.325 ms
We have compared the performance majorly in the light of two new parameters Energy Time delay (ET) cost and the Energy Throughput ratio. According to [38] throughput may be improved due to frame size optimization, however, different time values for collision, success, and the empty slots may lead to excessive time delays. Therefore, it is essential to analyze the energy performances during the consumed time delay measured by the energy-time-delay (ET) metric. Furthermore, the Energy Throughput ratio gives the measure of throughput and energy trade-offs achieved during identification. The performance metrics utilized in performance evaluation are given as follows in (19), (20), and (21) respectively.
Throughput
N 1 (1 ) N 1 n n
(19)
Energy Timedelay Cost ( ET ) [(
Energy Throughput
kn 3 )] 1 (2 N 2) N (1 * ) n
Energy Throughput
(20)
(21)
In fig. 6 we have compared the throughput with existing DFSA algorithms. The Novel-Q DFSA achieves the maximum throughput value up to 36.2% which is almost the same as MFML-DFSA, and EDFSA with throughput values reaching up to 36.5%. In fig.7, 8, 9, and 10 we have compared the collision, idle, reader, and the tags energy performances. From fig. 7 and 8 it is clear that the collisions and the idle slot energy consumption values are reduced to a greater extent leading to minimum reader and the tag energy consumption values for Novel Q-DFSA as shown in 9, and 10.Fig 11 shows Energy Throughput ratio assessment which is minimized for Novel-Q DFSA compared to the existing algorithms proving the best energy and throughput trade-offs achieved. In fig. 12 we have compared the Energy-Time Delay Cost values assuming proportionality constant k 1 for ease of comparisons. The ET cost for Novel-Q DFSA is the least making it the perfect choice for passive RFID implementation with improved energy and time performance.
Fig. 6 Tag Population vs. Throughput
Fig.7 TagPopulation vs. Collision Energy[mJ]
Fig.8 TagPopulation vs. Idle Energy[mJ]
Fig.9 Tag Population vs. Reader Energy [mJ]
Fig. 10 Tag Population vs. Tag Energy [µJ]
Fig. 11 Tag Population vs. Energy Throughput Ratio[mJ]
Fig. 12 Tag Population vs. Energy-Time Delay Cost
In fig. 13 we have shown the throughput and time delay performance variation with the increasing tag population size. For a stable channel, the throughput first increases, attains the maximum value, and become constant compared to delay with increasing N value as shown in fig. 13. According to [29] the Throughput-Delay product gives the knowledge of stability achieved during the identification. Henceforth in fig. 14 Throughput-Time Delay product is analyzed for 100-5000 tags range. The result from fig. 14 shows that with increasing N the throughput Delay product reduces but are maintained to a non-zero value for considerable tag population up to 5000.
Fig. 13 Tag Population vs. Throughput-Time Delay
Fig. 14 Throughput-Time Delay vs.N
We have validated the theoretical results obtained through MATLAB 8.0 in NS-3 for performance evaluation in real time event driven network simulator. In fig. 15 we have compared the identification time difference achieved in MATLAB 8.0 and NS-3 separately. In NS-3 we have simulated the tags by randomly distributing on a 50x50 grid. The Table 5 shows that the percentage time difference between MATLAB and NS3 results is lesser than 4% confirming the suitability of the proposed algorithm in real time testbed. Table 5 MATLAB vs. NS-3 Data Comparison
N
Identification
Identification
time ( seconds )
time (seconds)
using MATLAB
using NS-3
8.0 100
1.52
1.38
200
3.039
3.17
300
4.53
3.42
400
6.06
6.23
500
7.37
6.98
600
8.97
8.67
700
10.69
12.35
800
12.02
10.39
900
13.43
12.25
1000
14.92
15.38
. Fig. 15 Identification Time Comparison between MATLAB vs. NS-3
Conclusion The Novel Q-DFSA algorithm achieves substantial througput maximum up to 36.2% along with enormous energy savings compared to the existing DFSA algorithms. The proposed algorithm justifies the minimum implementation and computational cost constraints required for EPC C1G2 standards based passive RFID applications. The Novel-Q DFSA achieves minimal Energy-Delay cost and minimum Energy-Throughput ratios confirming improved time and energy performance collectively. Our proposed algorithm maintains the stability in considerably high tag population size proving the applicability in denser tags environment for multiple applications.
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Evaluation", IEEE Trans. of Communications.,vol. 23,no.4,(1975)410-423. [30] M. V. B. Delgado, J. V. Alonso, and F. J. González-Castaño, “Analysis of DFSA anti-collision protocols in passive RFID environments,” in Proc. 35th Int. Conf. IEEE Ind. Electron. Soc., Porto, Portugal, Nov., (2009)2630–2637. [31] D.K. Klair, and C. Kwan-Wu, “A Novel Anti-Collision Protocol for Energy-Efficient Identification and Monitoring in RFID-Enhanced WSNs,” in Proc.17th IEEE Int.Conf.Computer Communications and Networks,(2008)1-8. [32] Y. Xinqing, L.Xuemei., “Evaluating the Energy Consumption of RFID Tag Collision Resolution Protocols”, Proc. 6th IEEE Int. Conf. Mobile Ad-hoc and Sensor Networks, (2010)215-221. [33] S. Ghez,S. Verdu, and S.C.Shwartz , “Stability Properties of Slotted Aloha with multipacket reception capability”, IEEE Trans.of Automatic Control, vol.33,no. 7,(1988)640-649. [34] G.Feyolle,E.Gelenbe , and G. Labetoulle ,“Stability and Optimal Control of the Packet Switching Broadcast Channel” Intl.Journal of the Assoc. for Comp. Mach.,vol.24,no. 3,(1977)375-386. [35] J.Yu, and L.Chen “Stability Analysis of Frame Slotted ALOHA Protocol”,Cornell University Library Press,(2014)1-14. [36] Y. Eo, H. Bang, K. Choi, S. Jeon, J.,S. Jung, D. Lee , and H. Lee, “A Single-Chip CMOS Transceiver for UHF Mobile RFID Reader,” IEEE Journal of Solid-State Circuits, vol. 43, no. 3, (2008)729-737. [37] U. Karthaus, and M.fischer, “Fully Integrated Passive UHF RFID Transponder IC With 16.7 µW Minimum RF Input Power,”IEEE Journal of Solid-State Circuits, vol. 38, no. 10,(2003)1602-1608. [38] Z. Lijuan, Z.Jin, and T. Xiahou,“Assigned Tree Slotted ALOHA,”IEEE Trans. on Wireless Comm.,vol. 12, no. 11,(2013)5493-5505.
PII: DOI: Reference:
S1574-1192(17)30265-1 http://dx.doi.org/10.1016/j.pmcj.2017.05.004 PMCJ 836
To appear in:
Pervasive and Mobile Computing
Received date : 21 November 2014 Revised date : 18 February 2017 Accepted date : 17 May 2017 Please cite this article as: T. Agrawal, P.K. Biswas, R. Sharma, A novel-Q DFSA algorithm for passive RFID system, Pervasive and Mobile Computing (2017), http://dx.doi.org/10.1016/j.pmcj.2017.05.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A Novel-Q DFSA Algorithm for Passive RFID Systems Tanvi Agrawal, P. K. Biswas, and Rohit Sharma# Department of Information & Technology, NITIE, Mumbai #
Department of Electrical Engineering, IIT Ropar, Rupnagar
[email protected]; [email protected]; [email protected]
Abstract
The tag detection ability of Passive Radio Frequency Identification (RFID) systems are critically challenged by the collision occurrence due to simultaneous responding tags during the identification process.The dynamic scheduling of the frame size governed by Dynamic Frame Size ALOHA(DFSA) process, by adjusting the frame lengths according to the size of tag population can avoid the collisions during the identification.However,the performance of DFSA majorly depends on the frame size selection policy which in previous studies was adopted to achieve the target of throughput maximization during a frame. This condition is obtained at the cost of equating the frame size up to the number of estimated tags responding during the time frame. This approximation enhances the throughput value but contributes to massive energy wastages as the frame lengths approach to a very large value in large tag population size. Therefore, it is essential to develop the new frame size estimation policy for DFSA achieving the aim of optimization between throughput and energy for improved time and energy performance. In this paper, we have proposed an EPC C1G2 standards based Novel-Q DFSA algorithm which optimizes the frame size accounting both the energy and the throughput. The combined throughput and energy trade-offs are measured through Energy Time-Delay (ET) cost which is minimum for our proposed algorithm compared to the existing solutions. Furthermore, the Throughput-Time Delay product approves the stability in large population size making it suitable for numerous identification applications. Keywords: Dynamic Frame Slotted ALOHA, Enhanced Dynamic Frame Slotted ALOHA, Electronic Product Code - Class 1, Generation 2, Frame Slotted ALOHA, and Radio Frequency Identification (RFID).
1 Introduction RFID technology is currently being deployed in large-scale identification applications i.e. Wal-Mart, Sam’s Club, US Department of Defence for automated inventory identification [1]. Identification through RFID provides the benefit of enhanced product visibility, and availability without the line of sight requirement, with minimal human efforts compared to the existing barcode technology [1]. The RFID system consists of reader, tags, and the server, where the reader capture information from the tags affixed to the items,using radio frequency waves and transmits it to the server [2]. The RFID systems are broadly classified into Active and Passive RFID systems based on the tags classification being utilized. The active RFID systems exploit the active tags which have the on-chip energy sources, while the passive RFID systems use passive tags which do not bear the self-containing energy source [3].The Electronic Produc Code(EPC) Class-1,Generation-2(C1G2) based passive RFID tags are gaining the popularity in large scale identification applications due to the associated lower cost compared to its counterpart[1]. However, these passive tags are more susceptible to collisions compared to the actve tags due to limited computing and memory capability making them incapable of sensing the neighbouring tags responses on the channel [4].These collisions can reduce the reading efficieny creating a major concern about the accuracy achieved during the identification process.The ALOHA algorithms attempts to reduce the simultaneous tag responses through time-based scheduling [5]. These ALOHA based anti-collision algorithms are highly suitable in passive RFID environment case due to lower bandwidth, and memory requirement with simpler reader designs [6].ALOHA algorithms are subdivided into Frame Slotted ALOHA (FSA) and the Dynamic Frame Slotted ALOHA (DFSA) based on the frame size selection policy.
The reader transmit the numbr of time slots based on the frame size selection decision, and the tags in turn respond during any time slot of that frame through random selection.Based on the tags answer, the slot can be filled with one tag response,multiple tags response, and no tag response resulting in the success slot (s), collision slot(c), and the emplty slot(e). In FSA, the time frame value transmitted by the reader to the tags is fixed.This however, can degrade the throughput achieved if the number of contending tags increase or decrease beyond the fixed time frame value limit during a frame [7]. In DFSA the frame size is dynamically modified in accordance with the number of tags responding during the time frame.This further, maximizes the throughput achieved during a frame by equating the frame size up to the number of responding tags estimation ( Nest ) based on the s , c and e statistics. However, the energy consumed by the DFSA is higher compared to the FSA as the frame size is equated to the Nest ,which in turn increases the energy consumption value during the frame due to direct proportional relation between the energy and the frame size[8]. In fig. 1 we have shown the dynamic scheduling of DFSA for frame size estimation based on the s, c, and e values utilized for tags estimation. Frame 1 1
2
Frame 2 3
1
2
Frame 1 3
4
1
2
Tag 1 Tag 2
s s
. . . . . . .
c
e
Tag N
Fig.1 DFSA Operation
The performance of DFSA is challenged by the two factors, the Tag Population Estimations, and the Frame size Approximations.In previous studies of DFSA development in [4], [6-7], and [9-24] the performance of DFSA was improved by adopting various tag estimation methods. However, the frame size approximations were commonly adopted to satisfy the n N est offering maximum optimal throughput up to 36.8% during a frame [25]. This further, gives rise to energy consumption in large tags environment as the number of collisions also increase with frame sizes approaching to a very high value [26]. These collisions waste the number of time slots during a frame and thus, the energy by not giving the
useful information in the form of
identity.Also,the energy during a time frame is directly proportional to the number of time slots or the time delay consumed during a frame [27]. Therefore, increasing the frame size n up to the number of estimated tags may achieve the maximal throughput values but also affect the energy budget in denser tag population. During the time frame of length n and N tag contenders, the dependence of both the energy E ( n, N ) and the throughput Th ( n, N ) on n necessitates the selection of new optimization goals for frame size estimations. The frame size optimization attemps proposed earlier in [24], and [28].In [24] the author suggested that irrespective of increasing the frame size indefinitely up to the number of tags the number of responding tags must be restricted to achieve the maximal throughput condition. Therefore, the author fixed the maximum frame length up to 256 and allowed the tags to answer in groups until all identified. This, however, requires additional reader and tags computation creating implementation concerns for low-cost passive RFID systems with limited functionality[4]. In [28] an improved DFSA algorithm was proposed which utilizes new frame size estimator satisfying the energy and throughput trade-off criteria. The algorithm achieves better energy savings and improved throughput conditioned by allowing the tags to respond in a limited number of slots irrespective of restricting them to groups as in [23]. This again needs additional computational efforts by the reader and the tags causing implementation concerns for low-cost passive RFID systems. Therefore, the need is to develop the new frame size estimator with minimal computational efforts for improved throughput and energy performance for EPC C1G2 standards based passive RFID systems. In this paper we have proposed a Novel Q-DFSA algorithm which proposes a new optimal frame size estimator by minimizing the Energy and Time Delay product. As
time delay minimization is essentially equivalent to the throughput maximization serving the purpose of maximum possible throughput outcome [4]. The algorithm achieves the minimum energy consumption and the substantial throughput value obtaining minimum Energy-Time-Delay (ET) cost. Further, the analytical results are tested on Network Simulator NS-3 for performance comparisons in real time simulation test bed. According to [29] slotted ALOHA algorithms are subjected to instability if the number of unidentified tags exceeds the number of available slots. The number of retransmissions increase if the frame length is not selected appropriately increasing the time delay [19].Henceforth it is essential to analyze the stability of the Novel-Q DFSA employing new optimal frame size estimator. The Throughput Time Delay product for the proposed algorithm confirms the stability in considerable tag population value. The remainder of the paper is organized as follows: Section 2 is the EPC C1G2 standards; Section 3 is the Background Work;Section 4 reports the Novel-Q Algorithm; Section 5 is Performance Evaluation followed by the Conclusion in Section 6.
2 EPC C1G2 Standards The ALOHA process of tag collisions arbitration during identification for passive tags are governed by the EPC C1G2 standards[21].The EPC Global industry defined the set of standards for the UHF passive RFID systems operating between 860-960 MHZ bands. These standards support for both the Fixed Frame and the dynamic frame structure by the Q parameter selection during the tag identification process. The EPC C1G2 standards based identification procedure is given below.
The reader initiates the Identification Frame with Query command. The Query consists of Q Parameter indicating the number of time slots contained in a frame ranging between [0,15]. The tags in the vicinity of the reader generate random numbers between 0, 2Q 1 .
The tags generating a random number as zero will answer with a 16-bit random number (RN-16) to the reader. Further slots may result in success (s), collision(c), and empty (e) based on the RN-16 responses generated by either the single, multiple or no tags during a frame.
The reader acknowledge the tags identified in success slots through ACK command,and the tags in response to ACK transmit full tag identifier or EPC (Electronic Product Code) to the reader.
The reader may initiate the consecutive time slots either with QueryRep or QyeryAdjust commands. The QueryRep repeats the same Q parameter transmitting the same time frame length while the QueryAdjust changes the Q parameter indicating a change in frame length.
In EPC C1G2 standards based DFSA the frame size is adjusted dynamically either slot by slot, or frame by frame by adjusting Q value known as Q-slot, and Q -frame DFSA operation. The Q value is changed by adding or subtracting the C parameter based on the collision (c) and empty slot (e) information available to the reader during a frame [21]. In case the slot is in a collision the C value is added to the Q while, in the case of empty slot the C value is subtracted from the Q value from previous slot. The range of C lies between 0.1, 0.5 and the modified Q value is rounded up to the nearest integer for frame adjustments. Fig.2 illustrates the working principle of EPC C1G2 standards based DFSA algorithm for 3 tags. During the first slot only tag 1 transmits the RN-16 value resulting in successful slot (s) and the reader, in turn, sends back the ACK in response to RN-16. In second slot tag-2 and tag-3 simultaneously send the RN-16s resulting in a collision (c). In response to this, the reader may send the QueryAdjust or the QueryRep commands in order to inform the change or no change in frame based on Q adjustments. During the third slot, no tag send back the responses to the reader leading to an empty slot (e). Here T1 is a reader to tag response time, T2 is a tag to reader response time, and T3 is the time required by the reader to issue another command after waiting time of T1 [21].CW represents the continuous wave energy which is transmitted by the reader to energise the passive tags in between the idle time.CRC-16+OID is the combination of object identification number along with 16 bit Cyclic Redundecy Checksum (CRC-16) known as the EPC of the tag.
Fig.2 EPC C1G2 standards based Q-DFSA example
3 Background Work The performance of DFSA is governed by the two factors one is the tag estimation ( N est ) and another is the frame size n approximation. The tag estimation methods have widely been explored by the authors in [4], [6-7], and [9-24]. The tag estimation methods considered by the previous authors are categorized as Error Minimization, Heuristic, and Maximum Likelihood approach. Error minimization methods were deployed in [14], [15], and [24] which minimize the squared error between expected c e s and actual values of success collision and empty slots. The Vogt [14]
approach of tag estimation is computationally feasible henceforth widely utilized for EPC C1G2 standards based passive systems. In [16], [18], and [19] the authors maximize the likelihood of conditional success probability during a frame. In recently proposed Multiframe Maximum Likelihood DFSA (MFML-DFSA) algorithm in [19] the maximum likelihood estimations were computed based on the multiple frame
statistics. This further achieves better accuracy compared to the other existing maximum likelihood estimations. The algorithms developed in [4], and [9-13] counts on a formula based estimations known as a Heuristic method and adjust the frame length to the nearest power of two [17]. However, the performance of the heuristic approach is poor compared to the existing error minimization estimators [30]. The algorithms developed in [6-7], [20-23], and [28] belong to EPC C1G2 standards based Q-DFSA family in which the frame size is optimized by updating the Q parameter without utilizing tag estimation techniques. In basic DFSA (increase) method proposed in [7] reader initiates the interrogation round with minimum frame size as 4 and increase frame size until the single tag is identified. In the case of single tag identification the reader stops the cycle and starts to read with initial minimum frame size. In [22] much-improved Q algorithm was proposed which optimize the frame size utilizing two different constants for collision and empty events as Cc , and Ci unlike Q-DFSA which use only parameter C . If the slot is in a collision Cc is added while in the empty slot event Ci is subtracted from original Q value this further improved the throughput achieved during the frame compared to the existing Q-DFSA algorithms. Table-1 illustrates the previously developed DFSA algorithms with detailed frame size optimization and tag estimation ( N est ) methods adopted in previous studies. Conclusively, it can be said that the tag estimation methods may differ however, the frame size optimization method commonly applied by the authors in [4], [6-7], and [924] were either the throughput maximization or the delay reduction resulting in However, the frame size approaches to a very high value for
n N est
n N est
.
in large number of
tags environment [26]. The optimization of frame size based on the energy consumption perspective has not been paid attention in previous studies which play a vital role in large tags size environment.In [23], and [27] the authors attempt to solve the indefinite frame size problem with existing frame size estimator leading to energy reductions.Although the reductions in frame size is achieved by by allowing the tags to answer in the group. This, however, requires complex reader and tags computations not compatible with resource constrained passive RFID systems.The table -1 shows the DFSA algorithm and the background work based on the frame size optimization and the tags estimation methods.
Table-1 DFSA algorithms Background Work Algorithm
Frame Size Estimation (n)
PS[4] Schoute[9]
Optimization Method Throughput Maximization Throughput Maximization
Cha and Kim[10]
Throughput Maximization
n Nest
Heuristic
Optimization Outcome Frame size is fixed
n Nest
Tag Estimation(Nest) Method Heuristic Heuristic
Wang [11]
Throughput Maximization
n Nest
Heuristic
Kodialam NandaGopal [12]
Throughput Maximization
n Nest
Heuristic
ASAP [13]
Throughput Maximization
n Nest
Heuristic
Vogt[14]
Throughput Maximization
n Nest
Error Minimization
SbS[15]
Throughput Maximization
n Nest
Error Minimization
EDFSA[24]
Throughput Maximization
n Nest
Error Minimization
Floerkemiere[16]
Efficiency Maximization
n Nest
Maximum Likelihood
Chen and Hung [18]
Efficiency Maximization
n Nest
Maximum Likelihood
MFML-DFSA[19]
Throughput Maximization
n Nest
Maximum Likelihood
Optimal DFSA[20]
Time-Delay Minimization
n Nest
-
Q-DFSA [21]
s,c,e statistics
n Nest
-
Joe and Lee[6]
s,c,e statistics
n Nest
-
s,c,e statistics
n Nest
-
Slot Count [23]
s,c,e statistics
n Nest
-
DFSA (increase)[24]
s,c,e statistics
n Nest
-
Q+ -[22]
Agrtawal et al.[28]
Energy-Throughput Optimization
n
N 1 0.3010
-
4 Novel Q-DFSA Algorithm In this section, we describe our Novel Q-DFSA algorithm which follows the EPC C1G2 standards based tag identification process known as Q-DFSA algorithm.The Novel QDFSA works in Q-slot manner, by changing the Q value slot by slot, dynamically for frame lengths adjustment.The Novel Q-DFSA employ a new frame size estimator achieved through energy and time delay product optimization. The frame size estimator proposed for Novel Q-DFSA will help the reader to analyze the estimated value of n according to the tag population value, and then the integer value to the nearest power of two will be identified for 2Q approximation.The reader then communicate the Q value to the tags for estimated frame size and the tags based on Q value can generate the random numbers between 0 and 2Q-1 during the time slots of the frame.
4.1 Optimal Frame Size Formulation Let
PRTX , PRRX , PTTX , and PTRX
be the reader transmission, reader reception, tag
transmission and the tag reception power respectively.Further, TQUERY be the time consumed in Query transmission, and L be the number of bits transmission during a frame of length n in N tag population value.The value of E ( n, N ) [33], throughput
Th(n, N ) [4], and time delay T (n, N ) [4] are given by the equations 1, 2, and 3.
E (n, N ) TQuery ( PRTX PTRX ) n{L( PTTX PRRX ) PRTX PTRX }
Th(n, N )
N 1 (1 ) N 1 n n
(2)
T ( n, N )
n 1 (1 ) N 1 n
(3)
(1)
In equation (1), TQUERY , PRTX , PRRX , PTTX , and PTRX are the predefined and fixed while,
L is variable and is inversely proportional to Th(n, N ) as higher throughput results in more success and lesser number of transmissions during the frame. Therefore, reframing the energy equation by replacing fixed values with a proportionality constant k is given as followed in (4). E (n, N ) kn *
1 kn 2 1 Th(n, N ) N (1 ) N 1 n
(4)
The throughput maximization is equivalent to delay reduction according to [4] therefore, our new optimization goal F minimizes the T (n, N ) , and E ( n, N ) multiplicative for optimized n ( n * )formulation given by (5). n* arg min [ F ] arg min [ E (n, N )*T (n, N )] n(1................215 )
(5)
n(1................215 )
Solving (5) for n * by keeping values from (3), and (4) we get the F as given by (6). F min[k ][( n
kn *2 n* )]( )] 1 N 1 1 N 1 N (1 ) (1 ) n n
(6)
Computing
n*
dF and equating it to zero we get the n * value as given by (7). dn
2N 1 3
(7)
4.2 The Comparative Analysis of Frame Size Estimators The previous attempts of DFSA development have employed the frame size estimator as n N for frame lengths adjustments.However, this leads to excessive collisions and energy consumption when the tags population becomes large. In [28] we have proposed a new frame size estimator as n
N 1 satisfying throughput and energy trade-off 0.3010
criteria. The estimator yields reduced energy values however, throughput values were limited creating stability concerns in huge tags environment. As in the case of lower throughput values increased number of retransmissions threaten the stability during a frame [29].In table-2 we have compared the two frame size estimators with our proposed one for n value comparisions. According to [19] there is a certain range of N corresponding to each n for which maximum stable throughput value is achieved and after this value the throughput starts to degrade during a frame as shown in fig. (3). Table 2: n vs. N Range
nN
N
n
4
2
4
3
8
6
16
11
32
22
64
43
128
86
256
171
512
342
1024
683
2048
1366
1-3 3-6 6-11 11-22 22-44 44-89 89-177 177-355 355-710 710-1420 1420-2839
2N 1 3
n
8 8 8 16 32 32 64 64 128 128 256
N 1 0.3010
Fig.3 Number of tags vs. Throughput
4.3 Stability analysis From fig. 3 it is clear that the maximum achievable throughput value is up to 36.8% for n N estimator and lower for smaller n value estimations. The condition worsens in
high tag population scenario as lower throughput values result in higher collision and retransmissions leading to instability conditions. Therefore, it is essential to analyze the stability for lower values of n proposed by our frame size estimator. In this section, we have established a condition of stability with the aim of achieving maximum throughput condition with lower n value estimator. Let mi denote the number of backlogged packets during the ith frame then mi (i 0) can be modeled through Markovian Chain and will only be stable if it is
aperiodic and irreducible [33]. Let λ be the expected number of arrivals in a slot and
be the backlogs ratio defined as
n m m mi n with i 1 condition, then the two sufficient
condition for mi (i 0) to be aperiodic and irreducible is given as follows according to [34] by (8) and (9). (i)
E[(mi 1 mi mi ]
(8)
(ii)
lim n * e ) 0
m
(9)
Here E is the expectation value of backlogs during a slot in a frame. In fig. 4 we have shown the number of unidentified tags and backlogged packets at t and t t timeslot represented with ( N t , mt ) and ( N t t , mt t ) pairs during a frame of length n .
N t , mt
-
e
s
N t t , mt t
c
s
e
c
-
n Fig. 4 Time Frame
During the frame, every collision slot generates at least two backlogs as minimum two tags would have answered in that slot and every success slot leads to the reduction of one backlog due to one identified tag [14]. Therefore at t t time number of tags
N t t N t s while mt t mt 2 * c .Henceforth it is essential, to control the backlogs as it increases at much faster rate compared to the success rate leading to the reduction in the number of arrivals. Lemma-1 The backlog increase with lower frame size values n m or n m and can only be decreased by increasing time frame maximum up to the backlogs approaching n m . The stability of a frame is challenged with n m condition giving rise to backlogs as shown by (10) according to [35]. m
m ) lim ( m
limO(m) lim m e
m
m
m
m e m ) ) 0 lim (O(m) m e m ) ) 0 m m )
m m )
limO(m) lim m e m
m
m m )
limO(m) lim m e m
m
(10)
m m )
(11)
From equation (11) it is clear that the stability condition is not fulfilled with n O m
as m e
m m )
value increases exponentially for O m or n m condition. Further
expanding the stability equation for n m we get the result as follows.
m
m
m m m) lim[( ) e m ) ] 0 lim[( ) e m ) ] 0 m m m) m)
(12)
The eq. (12) shows that the inequality holds true for n m balancing the stability equation while not for the n O m as shown by (10). Let r be the new arrived packets, f be the number of success packets, m be the backlogged packets. Further, the probability of f+r arrivals is represented as f r and .
the probability of exactly f success out of m backlogged packets during a frame is represented as
m , f
. Then, the transition probability of backlogs increase from m to m+r
in total f+r arrivals is represented by Pm, m+r given by (13) according to [35].
Pm,mr
min(m,n)
f 0
f r m, f r 1
(13)
The
Pm ,m r
m , f
can be increased more, compared to
can only be decreased if for increased number of backlogs from m to m+r,
success. The value of
m , f
( nm )(m!) nm 0 0
Here G Q, h Q h
The
m , f
resulting in more backlogs converting in
is given as follows for different arrival conditions.
( nf )( mf ) f!G(n f, m f) m , f
f r
h
x 1
x 1
j 0
0
(1) x [(h j)(Q j)](Q x)(h x)
(14)
1 x!
(15)
0
decreases to zero when
f min m, n
, and f n m .Therefore, when the number of
arrivals exceed the backlogs the success rate degrades and approaches to zero. Nevertheless,
m , f
is higher for n m compared to n m as the value of
( nm )(m!) ( )( )f!G(n f,m f) , and upsurges for this condition. Henceforth decreasing nm n f
m f
success rate for n m condition leads to increased
Pm ,m r
value and further approaches
to zero in case the number of arrivals becomes greater or equal to m . Therefore, whenever the stability condition is deviated it is essential to increase
n
maximum up to
m in order to minimize the number of arrivals compared to backlogs.
Lemma-2 The condition of stability is satisfied only when
s cs
during a frame.
Let ps be the probability of success during frame size of n slots then according to [34] number of arrivals
t
at any time instant t must be lesser than
lim n nps
during a frame for a system to be stable as given by (16).
t lim n nps
(16) t
The value of
s
ps
during a frame at t time is defined as the success ratio as (
i 1
n
)
therefore, keeping the value in (16) the condition reduces to as follows given by (17). t
t lim n n
s i 1
n
t
s
lim
(17)
n i 1
Further, the value of t at
t
time instant is proportional to the difference of collision t
t
i 1
i 1
and success slots thus, t c s during a frame at time t . As the difference represent the reduction of backlogs to the number of arrivals, therefore, replacing the t value and rewriting the rewriting the stability condition as given by(18). t
t
t
i 1
i 1
i 1
c s s
(18)
Combining results from lemma-1 and lemma-2, conclusively it can be said that whenever
t
t
t
i 1
i 1
i 1
c s s condition
arrives stability condition is deviated as the
success rate is reduced. This, however, can only be corrected by increasing the frame size maximum up to the order of backlogs during a frame.
4.4 Implementation of Novel-Q DFSA The Novel Q-DFSA is based on the EPC C1G2 standards based Q-DFSA algorithm and works in Q-slot manner as the frame size is changed dynamically slot by slot. In QDFSA the Q-slot frame size modification is based on collision slot ( c ), and empty slot ( e )information. It is increased if the slot is in the collision and decreased in case of the empty slot by adding and subtracting the C parameter. However the value of C is not clearly defined in the standards. In Novel-Q DFSA the frame size is updated slot by slot by analyzing the stability condition check established by lemma-2 of s c s unlike QDFSA .If it is not matched then the frame size is changed based on the difference of s , and c s i.e. n is increased by c 2s value. This further minimizes the gap between backlogs and the arrival rate to meet the stability criteria as s represents the arrival rate while c s represents the backlogs during a frame. The frame size is increased by this difference until the stability condition is satisfied with maximum value set by the proposed frame size estimator n
2 N est 1 .Henceforth Novel-Q DFSA starts with 3
the minimum frame size and increases maximum up to n
2 N est 1 estimator. This 3
further achieves the goal of maximum energy savings along with the optimal throughput outcome to maintain the stability during a frame. In Novel Q-DFSA N est at kth frame k
k
k
j 1
j 1
j 1
( N ( est )k ) is computed as N ( est ) k 2 c j s j , here c j is the total number of k
collision slots and
s j 1
j
is the total number of successes up to the kth frame. According k
to [15] minimum, two tags would have replied during collision slots, therefore, 2
c j 1
j
is the minimum bound on the total number of collision slots up to the kth frame. Out of these collision slots, some of the slots turned successful due to the number of tags k
k
j 1
j 1
identified during those slots. Therefore, 2 c j s j yields the minimum bound on tags estimation during a frame k . The flow chart of Novel-Q DFSA is shown in fig. 5.
Fig. 5 Flow Chart Novel-Q DFSA Algorithm
5 Performance Evaluation The Novel Q-DFSA performance primarily depends on the proposed frame size selection method which optimizes the throughput and energy values both with minimal computational overheads. As Novel Q-DFSA increase the frame size by 2c s in every slot whenever the stability condition is violated to maximum value up to
k
k
j 1
j 1
2(2 c j s j ) 1 3
for n
2 N est 1 Henceforth, the maximum possible number of 3 . k
k
j 1
j 1
2(2 c j s j ) 1 iterations during a frame k
k
k
2 c j s j N ( est )k therefore j 1
can be
3
k
k
j 1
j 1
2(2 c j s j ) 1
j 1
3
n( c 2 s ) .Further as
n( c 2 s ) O ( N est ) k until the
condition of stability is achieved. Therefore, the maximum number of computations required for Novel Q-DFSA during a frame is O( N est ) . The performance of Novel Q-DFSA has been evaluated on MATLAB 8.0 and is tested on NS-3 discrete network simulator. Further the results are obtained by averaging the data over 100 simulations for better accuracy. The simulation environment comprises of 100-1000 tags with the single reader and is in compliance with EPC C1G2 standards .The tags have 96 bits long tag ID and are assumed to be stationary until identified. The energy consumption values are computed utilizing the power consumption values given in table-3 based on the transceiver and transponder power specifications given in [36] and [37].The time specifications are given in table-4 based on the EPC C1G2 standards based parameters [21]. For performance comparisons we have considered DFSA (increase) [8], Vogt [15], MFML-DFSA [20], Q+-[24], and EDFSA [29] algorithms.
Table-3 Power Consumption Values
Reader
Tag
PRtx
PRrx
PTtx
PTrx
501 mW
104 mW
4.2 µW
12.5 µW
Table 4: EPC C1G2 Specifications
SYMBOL READER TO TAG DATA RATE TAG TO READER DATA RATE RISE TIME/FALL TIME
SIGNIFICANCE TRANSMISSION RATE TRANSMISSION RATE READER POWER UP WAVEFORM RISE TIME/ READER POWER DOWN WAVEFORM FALL TIME DIVIDE RATIO
VALUE 40 KBPS 40 KBPS 500 µS
TARI
REFERENCE TIME INTERVAL OF DATA 0 FROM READER TO
25µS
DATA 0
TIME INTERVAL OF DATA 0
1 TARI
DATA 1
TIME INTERVAL OF DATA 1
1.5 TARI
RTCAL
READER TO TAG
DR
8
TAG DATA SIGNALLING
TRCAL RTPREAMBLE T1 T2 T3
62.5 µS CALIBRATION PARAMETER TAG TO READER 75 µS CALIBRATION PARAMETER READER TO TAG PREAMBLE 175 µS READER TRANSMISSION TO 175 µS (MIN) 225 µS (MAX) TAG RESPONSE TIME TAG RESPONSE TO READER 87.5 µS TRANSMISSION TIME TIME READER WAITS AFTER 87.5 µS T1 INTERVAL TO ISSUE ANOTHER COMMAND
T4 TRPREAMBLE Select
MINIMUM TIME BETWEEN READER COMMANDS TAG TO READER PREAMBLE Time Consumed in Select
137.5 µS 162.5 µS 6.3 ms
Transmission Query
Time Consumed in Query
0.8375 ms
Transmission Queryrep
Time Consumed in
0.2125 ms
QueryRep Transmission QueryAdjust
Time Consumed in
0.375 ms
QueryAdjust Transmission ACK
Time Consumed in ACK
0.6625 ms
Transmission NAK
Time Consumed in NAK
.325 ms
Transmission RN-16
Time Consumed in RN-16
0.6625 ms
Transmission EPC
Time Consumed in EPC Transmission
10.325 ms
We have compared the performance majorly in the light of two new parameters Energy Time delay (ET) cost and the Energy Throughput ratio. According to [38] throughput may be improved due to frame size optimization, however, different time values for collision, success, and the empty slots may lead to excessive time delays. Therefore, it is essential to analyze the energy performances during the consumed time delay measured by the energy-time-delay (ET) metric. Furthermore, the Energy Throughput ratio gives the measure of throughput and energy trade-offs achieved during identification. The performance metrics utilized in performance evaluation are given as follows in (19), (20), and (21) respectively.
Throughput
N 1 (1 ) N 1 n n
(19)
Energy Timedelay Cost ( ET ) [(
Energy Throughput
kn 3 )] 1 (2 N 2) N (1 * ) n
Energy Throughput
(20)
(21)
In fig. 6 we have compared the throughput with existing DFSA algorithms. The Novel-Q DFSA achieves the maximum throughput value up to 36.2% which is almost the same as MFML-DFSA, and EDFSA with throughput values reaching up to 36.5%. In fig.7, 8, 9, and 10 we have compared the collision, idle, reader, and the tags energy performances. From fig. 7 and 8 it is clear that the collisions and the idle slot energy consumption values are reduced to a greater extent leading to minimum reader and the tag energy consumption values for Novel Q-DFSA as shown in 9, and 10.Fig 11 shows Energy Throughput ratio assessment which is minimized for Novel-Q DFSA compared to the existing algorithms proving the best energy and throughput trade-offs achieved. In fig. 12 we have compared the Energy-Time Delay Cost values assuming proportionality constant k 1 for ease of comparisons. The ET cost for Novel-Q DFSA is the least making it the perfect choice for passive RFID implementation with improved energy and time performance.
Fig. 6 Tag Population vs. Throughput
Fig.7 TagPopulation vs. Collision Energy[mJ]
Fig.8 TagPopulation vs. Idle Energy[mJ]
Fig.9 Tag Population vs. Reader Energy [mJ]
Fig. 10 Tag Population vs. Tag Energy [µJ]
Fig. 11 Tag Population vs. Energy Throughput Ratio[mJ]
Fig. 12 Tag Population vs. Energy-Time Delay Cost
In fig. 13 we have shown the throughput and time delay performance variation with the increasing tag population size. For a stable channel, the throughput first increases, attains the maximum value, and become constant compared to delay with increasing N value as shown in fig. 13. According to [29] the Throughput-Delay product gives the knowledge of stability achieved during the identification. Henceforth in fig. 14 Throughput-Time Delay product is analyzed for 100-5000 tags range. The result from fig. 14 shows that with increasing N the throughput Delay product reduces but are maintained to a non-zero value for considerable tag population up to 5000.
Fig. 13 Tag Population vs. Throughput-Time Delay
Fig. 14 Throughput-Time Delay vs.N
We have validated the theoretical results obtained through MATLAB 8.0 in NS-3 for performance evaluation in real time event driven network simulator. In fig. 15 we have compared the identification time difference achieved in MATLAB 8.0 and NS-3 separately. In NS-3 we have simulated the tags by randomly distributing on a 50x50 grid. The Table 5 shows that the percentage time difference between MATLAB and NS3 results is lesser than 4% confirming the suitability of the proposed algorithm in real time testbed. Table 5 MATLAB vs. NS-3 Data Comparison
N
Identification
Identification
time ( seconds )
time (seconds)
using MATLAB
using NS-3
8.0 100
1.52
1.38
200
3.039
3.17
300
4.53
3.42
400
6.06
6.23
500
7.37
6.98
600
8.97
8.67
700
10.69
12.35
800
12.02
10.39
900
13.43
12.25
1000
14.92
15.38
. Fig. 15 Identification Time Comparison between MATLAB vs. NS-3
Conclusion The Novel Q-DFSA algorithm achieves substantial througput maximum up to 36.2% along with enormous energy savings compared to the existing DFSA algorithms. The proposed algorithm justifies the minimum implementation and computational cost constraints required for EPC C1G2 standards based passive RFID applications. The Novel-Q DFSA achieves minimal Energy-Delay cost and minimum Energy-Throughput ratios confirming improved time and energy performance collectively. Our proposed algorithm maintains the stability in considerably high tag population size proving the applicability in denser tags environment for multiple applications.
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