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Telecommunication Systems

, Volume 67, Issue 1, pp 63–71 | Cite as

CDMA-based anti-collision algorithm for EPC global C1 Gen2 systems

  • Wei Wei
  • Jian Su
  • Houbing Song
  • Huihui Wang
  • Xiumei Fan
Article
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Abstract

Frame slotted ALOHA protocol as a key technology to improve system throughput has been widely applied to modern radio frequency identification systems. In this paper, a novel frame slotted ALOHA collision arbitration protocol based on code division multiple access has been proposed. The main aim of the proposed algorithm is to avoid collisions between multiple tags. In the scheme, an orthogonal sequence is used as the means to distinguish the transmitted data from different tags within the same time slot and frequency band. The theoretical analysis and simulation results proved that the performance of our proposed algorithm outperforms the existing ALOHA-based protocols.

Keywords

RFID Anti-collision Aloha CDMA System throughput 

1 Introduction

Recently, there has been an increasing demand in the development of communications systems for the automatic identification of objects [1, 2, 3, 4, 5, 6, 7, 8, 9]. Radio frequency identification technology makes it possible and has attracted extensive attention. One of the major challenges facing RFID systems is the tag collision problem resulting from sharing of the common wireless channel by all devices in the system [2]. Tag collision occurs when multiple tags simultaneously respond to the reader with their signals. This collision degrades the identification performance of the RFID system. Hence, the primary goal of anti-collision protocols is to minimise the total identification time, i.e., the time required to identify all tags, or equivalently to maximise the system throughput.
Fig. 1

Comparison of TDMA and CDMA communication channel access techniques for an RFID system

Generally, most existing anti-collision protocols are based on time division multiple access (TDMA) methods including ALOHA-based algorithms [3, 4, 5] and tree-based algorithms [6, 7]. An ALOHA-based protocol is an intuitive solution which has been adopted by some UHF RFID standards such as ISO/IEC 18000 6C and EPC global C1 Gen2, but it does not appear to be scalable [8]. That is to say, the efficiency of the ALOHA-based protocol is affected by the cardinality (tag backlog). To enhance the performance and stability of the ALOHA-based protocol, researchers have proposed a series of improved frame slotted ALOHA protocols [9, 10]. Although these protocols could maintain a stable throughput value of nearly \(36\%\), they cannot breakthrough the maximum theoretical value of \(36.8\%\) achieved by a slotted ALOHA protocol. Compared to the ALOHA-based protocol, a tree-based protocol is cardinality insensitive because when the cardinality increases, the system throughput remains stable. However, the main disadvantage of this protocol is that every two slots should be triggered by a query command transmitted by the reader. Too many reader query commands lower the system throughput.

Aimed at the defects inherent in TDMA, there have also been many attempts to apply more efficient transmission schemes to RFID systems to reduce the collision rate. Previous attempts [11, 12, 13, 14, 15, 16] have shown that code division multiple access (CDMA) transmission can be an attractive option for RFID systems. In CDMA-RFID systems, tag separation is achieved through a set of spreading sequences used to spread the tag backscattering data. However, the performance of CDMA-RFID systems is mainly limited by the number of spreading sequences: using overly long spreading sequences will result in a significant increase in complexity.

We propose an FSA-CDMA anti-collision algorithm in which the tags can be adaptively divided into groups and identified group-by-group. For the purpose of exceeding the limited throughput of the ALOHA-based protocol, we introduce the CDMA mechanism into FSA. The reader can simultaneously identify multiple tags through orthogonal spreading sequences. The simulation results show that the proposed scheme outperforms current protocols by improving system throughput and decreasing identification delay. The remainder of the paper is organized as follows: the proposed FSA-CDMA is described in Sects. 2, 3 analyses the performance of the proposed scheme, conclusions are drawn in Sect. 4, and possible future research is presented.

2 The FSA-CDMA algorithm

In CDMA, diverse spreading codes are assigned to different users by using the characteristic of orthogonality or quasi-orthogonality of spreading sequences, multiple users simultaneously on the same frequency communicate with each other with a freedom from interference. In this case, each user may coexist in the domains of frequency, time, and space. As can be imagined, the appropriate application of CDMA to RFID systems ensures that the reader can identify multiple tags in the same slot. Figure 1 shows the comparison of TDMA and CDMA communication channel access techniques for an RFID system.
Fig. 2

Comparison of various throughputs for slotted ALOHA and CDMA

Fig. 3

Slot status in the proposed algorithm

From Fig. 1 the advantage of a CDMA technique in an RFID system may be inferred. To further emphasise the advantages of the CDMA technique, we compare the throughput of a TDMA-based, and CDMA-based, RFID systems. In an ideal CDMA-based RFID system, we assume perfect reception provided that the number of packets present in the channel is less than the spread factor. If the arrival processes of the return data from a tag obey a Poisson distribution in slotted ALOHA, the throughput of a CDMA-based RFID system can be given by:
$$\begin{aligned} S = G{e^{ - SF \times G}}\sum \limits _{k = 0}^{SF - 1} {\frac{{{{(SF \times G)}^k}}}{{k!}}} \end{aligned}$$
(1)
where S denotes the expected system throughput, G denotes the number of attempted transmissions per slot, and SF is the spread factor. Meanwhile, the throughput of the slotted ALOHA is defined by:
$$\begin{aligned} S = G{e^{ - G}} \end{aligned}$$
(2)
Figure 2 shows various throughputs S over the traffic rate G under slotted ALOHA and CDMA systems.
Obviously, for SF \(=\) 1, the throughput of CDMA degrades to that of slotted ALOHA. As the value of SF increases, the throughput improves but is limited. It provides the possibility of adopting a CDMA technique in an FSA RFID system. In an FSA-CDMA RFID system, if each tag encodes its data using a spreading sequence which has been designed to be orthogonal or quasi-orthogonal to the spreading sequence of other tags, it allows the successful identification of tags simultaneously. However, for RFID systems, to maintain synchronization is difficult. Therefore, it has been better to use PN sequences as a substitute for orthogonal codes. How to choose the most suitable PN sequence is out of the scope of this paper and is a subject for future work. In our proposed FSA-CDMA algorithm, we consider that a set of spreading sequences are stored in the reader. The tag randomly chooses a spreading sequence to encode its ID and send its ID to the reader. Hence, for a time slot in our scheme, there are three cases as follows:
  1. Case 1:

    Empty slot: no tags select the slot. Accordingly, the reader cannot receive any data from a tag.

     
  2. Case 2:

    Successful slot: it contains two cases. One case is only one tag’s selected current slot and responds with its ID. Another is that multiple tags select the slot and respond with their IDs encoded by different spreading sequences. In either case, the tags can be successfully identified by the reader.

     
  3. Case 3:

    Collision slot: it also contains two cases. One case is complete collision including all collided tags selecting the same spreading sequence, and each active (currently selected) spreading sequence selected by at least two tags. Another is partial collision: some active spreading sequences are selected by one tag, the other selected by more than one tag.

     
These three cases mentioned above are shown in Fig. 3. Considering that there are n tags in an RFID system, the frame length is F and the number of spreading sequence is k. To make the implementation convenient, we just write the encoded ID with a randomly selected spreading sequence into tag memory during production. It does not need the tags to be encoded with their IDs in real-time. The benefit is a saving in the energy consumption of the tags and a reduction of their architectural complexity. In the reader, all spreading sequences are stored in its memory and examined one-by-one to decode the information sent by those tags that replied. To formulate the proposed scheme, the probability of successful, collision, and idle tag states arising needs to be calculated.
Let \(P_e\) denote the probability that no tag selects a slot, we have:
$$\begin{aligned} {P_e} = \left( \begin{array}{l} n\\ 0 \end{array} \right) {\left( {\frac{1}{F}} \right) ^0}{\left( {1 - \frac{1}{F}} \right) ^n} = {\left( {1 - \frac{1}{F}} \right) ^n} \end{aligned}$$
(3)
In the next step, the probability of successful identification should be discussed. This probability is denoted by \(P_s\) which contains two parts as shown in Fig. 3.
$$\begin{aligned} {P_s} = {P_{one}} + {P_{S/C}} \end{aligned}$$
(4)
where \(P_{one}\) is the probability that only one tag selects the current slot, \(P_{s/c}\) denotes that all collided tags respond with their current slot but with different spreading sequences. Let \(P_{r/k}\) denote the probability that r tags select different r spreading sequences from k sequences. \(P_{r/k}\) can be computed as:
$$\begin{aligned} {P_{r/k}} = \frac{{k(k - 1)\ldots (k - r + 1)}}{{{k^r}}} \end{aligned}$$
(5)
For simplification, let
$$\begin{aligned} {P_{sub{-}c}} = {\sum \limits _{r = 2}^n {\left( \begin{array}{l} n\\ r \end{array} \right) \left( {\frac{1}{F}} \right) } ^r}{\left( {1 - \frac{1}{F}} \right) ^{n-r}}. \end{aligned}$$
Then, Eq. (4) can be expressed as:
$$\begin{aligned} \begin{array}{l} {P_s} = \left( \begin{array}{l} n\\ 1 \end{array} \right) {\left( {\frac{1}{F}} \right) ^1}{\left( {1 - \frac{1}{F}} \right) ^{n - 1}} + {P_{sub{-}c}}{P_{r/k}}\\ \;\;\;\; = \frac{n}{F}{\left( {1 - \frac{1}{F}} \right) ^{n - 1}} + {P_{sub{-}c}}\frac{{k(k - 1)\ldots (k - r + 1)}}{{{k^r}}} \end{array} \end{aligned}$$
(6)
Now, let \(P_c\) denote the probability in the proposed scheme, according to the analysis above, we have:
$$\begin{aligned} {P_c} = {P_{sub{-}c}}\left( {1 - \frac{{k(k - 1)\ldots (k - r + 1)}}{{{k^r}}}} \right) \end{aligned}$$
(7)
We need to calculate the probability of the partial-collision state which is a part of the probability of collision because the reader always identifies a part of the tags in any partly-collided slot. \(P_{full{-}col}\) denotes the probability of full collision which corresponds to the probability that all collided tags select the same spreading sequence plus that of each active spreading sequence selected by at least two tags. The former is simple to compute:
$$\begin{aligned}&{P_{s{-}col}} = {P_{sub{-}c}}\frac{1}{{{k^{r - 1}}}} \end{aligned}$$
(8)
$$\begin{aligned}&{g_k}(r,2) = \sum \limits _{i = 0}^r {{{\left( { - 1} \right) }^i}\left( \begin{array}{l} r\\ i \end{array} \right) \frac{{k!}}{{\left( {k - i} \right) !}}{g_{k - i}}\left( {r - i,1} \right) } \end{aligned}$$
(9)
$$\begin{aligned}&{g_{k - i}}\left( {r - i,1} \right) = P\left( {k - i,\;r - i} \right) {\left( {r - i} \right) ^{k - i}} \end{aligned}$$
(10)
In which \(P (k - i, r - i)\) is the probability that we have \((k - i)\) balls and \((r - i)\) boxes and all the boxes contain at least one ball. The mathematical expression for \(P (k - i, r - i)\) can be given by:
$$\begin{aligned} P\left( {k - i,\;r - i} \right) = \sum \limits _{j = 0}^{r - i} {{{\left( { - 1} \right) }^j}\left( \begin{array}{l} r - i\\ j \end{array} \right) {{\left( {1 - \frac{j}{{r - i}}} \right) }^{\left( {k - i} \right) }}} \end{aligned}$$
(11)
According to (9), (10), and (11), \(P_{d-col}\) can be written as
$$\begin{aligned} \left\{ \begin{array}{l} {P_{d - col}} = \sum \limits _{i = 0}^r {\sum \limits _{j = 0}^{r - i} {{{\left( { - 1} \right) }^{i + j}}\left( \begin{array}{l} r\\ i \end{array} \right) \left( \begin{array}{l} r - i\\ j \end{array} \right) } } \\ \qquad \quad \qquad \times \frac{{k!}}{{\left( {k - i} \right) !}}{\left( {r - i - j} \right) ^{k - i}}{P_{sub{-}c}}\\ {P_{sub{-}c}} = {\sum \limits _{r = 2}^n {\left( \begin{array}{l} n\\ r \end{array} \right) \left( {\frac{1}{F}} \right) } ^r}{\left( {1 - \frac{1}{F}} \right) ^{n - r}} \end{array} \right. \nonumber \end{aligned}$$
(12)
Hence, the probability of full collision and part-collision are:
$$\begin{aligned} {P_{full{-}col}}= & {} {P_{s - col}} + {P_{d{-}col}} \end{aligned}$$
(13)
$$\begin{aligned} {P_{part{-}col}}= & {} {P_c} - {P_{full{-}col}} \end{aligned}$$
(14)
Fig. 4

Flowchart through the FSA-CDMA process

Considering the disparity between slot durations, the so-called slot-optimal algorithm may not be effective in terms of identification time. So, in this letter we evaluate the performance in terms of time efficiency rather than traditional throughput of ALOHA-based algorithms. The time efficiency can be defined by:
$$\begin{aligned} {\eta _{time\_effi}} = \frac{{S{T_s}}}{{E{T_E} + S{T_S} + C{T_C}}} \end{aligned}$$
(15)
Fig. 5

FSA system throughput

where E, S, and C represent the numbers of idle slots, successful slots and collision slots for the identification process; \(T_E\), \(T_S\), and \(T_C\) denote the corresponding times. The flowchart of our proposed FSA-CDMA algorithm can be seen in Fig. 4. For reducing the complexity of the tag circuit, the frame length L must be a power of two. To maximize system throughput of an RFID system, an appropriate frame length should be determined for a given tag backlog. According to the Fig. 5 and Eq. (16) we can find the relationship between frame length and tag backlog.
$$\begin{aligned} \frac{n}{{{F_{Q1}}}}{\left( {1 - \frac{1}{{{F_{Q1}}}}} \right) ^{n - 1}} = \frac{n}{{{F_{Q2}}}}{\left( {1 - \frac{1}{{{F_{Q2}}}}} \right) ^{n - 1}} \end{aligned}$$
(16)
Herein \(F_{Q1}\) and \(F_{Q2}\) are adjacent frame lengths as described in Fig. 5. Then for a given number of tags, the appropriate frame length can be obtained and the results are listed in Table 1. From analysis [10], we know that the tag backlog estimation error has a slight affect on system throughput. Therefore, we can use \(n_{est} = 2.39C_k\) to estimate the tag backlog and adjust the frame length at the end of the current inventory round according to Table 1. Considering the aforementioned deficiencies of existing algorithms, a Q-ary search anti-collision algorithm (QAS) was proposed based on a tag ID bit encoding mechanism: such a mechanism could effectively eliminate idle slots, reducing the number of collision slots, thereby increasing identification efficiency [17].
Table 1

Relationship between frame size and tag range

Q

Frame size (F = 2Q)

Appropriate tag range \(n_1\) to \(n_2\)

2

4

1–5

3

8

6–11

4

16

12–22

5

32

23–44

6

64

45–89

7

128

90–177

8

256

178–355

9

512

356–710

10

1024

711–1420

Table 2

Parameter values for numerical computations

Parameters

Values (\(\upmu \)s)

Parameters

Values (\(\upmu \)s)

Reader to tag preamble

112.5

PC \(+\) EPC \(+\) CRC

800

Tag to reader preamble

112.5

T1

62.5

Query command

412.5

T2

62.5

QueryRep command

75

T3

50

ACK command

337.5

T4

112.5

Additional load (SF)

\(SF\times 100\)

RN16

100

Tsucc (other schemes)

2012.5

\(T_{succ }\) (our scheme)

2112.5

Tidle (other schemes)

300

\(T_{idle}\) (our scheme)

300

Tcoll (other schemes)

750

\(T_{coll}\) (our scheme)

1750/2250

3 Simulation results

In this section, the performance of our proposed algorithm was examined by carrying out extensive simulations compared with FDFSA [10, 18, 19, 20, 21, 22, 23, 24], GDFSA [9, 25, 26, 27, 28], and DFSA [3, 29, 30, 31]. To ensure the fairness and validity of the results, we take into account three metrics in our experiments: time efficiency, average identification time for one tag, and the energy consumption of the tags. To obtain the time efficiency of algorithms, we need to calculate the time duration of every step and command used in the anti-collision process. The primary parameters used in the simulations are listed in Table 2. We first compare the time efficiency of various anti-collision algorithms. Figure 6 plots the simulation results for normalized time efficiency according to Eq. (15) where the tag backlog varied from 5 to 1500. The initial size is set to 128. The efficiency of four curves ranged from the highest to the lowest as follows: FSA-CDMA (\(SF = 10\)), FSA-CDMA (\(SF = 5\)), FDFSA, GDFSA, and DFSA. Even though our method uses a less accurate method for backlog estimation, FSA-CDMA maintains its good performance due to the introduction of the CDMA technique to the FSA algorithm and the appropriate frame length adjustment. In addition, we verify that the performance improvement is physically limited by the increase in SF. When the number of tags is \({>}\)1000 the initial frame size and SF are too small, many tags select the same spreading sequence, so the reader cannot identify the tag successfully. Figure 7 shows the simulation results for the average identification time for one tag within an initial frame length of 128 [32, 33, 34]. FSA-CDMA (\(SF = 5\)) took 2.92 ms, FSA-CDMA (\(SF = 10\)) took 2.82 ms, whereas GDFSA, FDFSA, and DFSA took 3.30, 3.28, and 4.89 ms, respectively. Although the FSA-CDMA incurs an additional time cost, the overall performance can be improved due to the CDMA mechanism. For one tag, the proposed scheme spends only 2.82 ms, i.e., the identification speed is about 355 tags/s. Meanwhile, that of FDFSA, GDFSA and DFSA are about 305, 303, and 205 tags/s, respectively. To evaluate the temporal energy consumption of tags under various algorithms, we defined the energy consumption as:
$$\begin{aligned} E_{cons} = \frac{1}{2}\frac{{G_{inv} }}{{T_{clk} }}V_{cc}^2 C_{load} T_{dura}. \end{aligned}$$
(17)
Fig. 6

Simulation results: time efficiency

Fig. 7

Simulation results: average identification time for one tag

Herein \(G_{inv}\) denotes the frequency of inversion of a given digital gate (\(0G_{inv}1\)), \(T_{clk}\) denotes the internal clock period of a tag, \(V_{cc }\) denotes the internal supply voltage, \(C_{load}\) is the loading capacitance, and \(T_{dura}\) is the time duration of a tag in the identification process. In our simulation experiments, we set \(G_{inv}\), \(T_{clk}\), \(V_{cc }\), and \(C_{load}\) as 0.5, 1/1.92 MHz, 1 V, and 11.5 pF, respectively. The comparison of energy consumption is shown in Fig. 8 where the proposed scheme is the most energy efficient, that is to say, under the same conditions, FSA-CDMA will consume less energy [17, 34, 35, 36].

For example, the FSA-CDMA (\(SF = 10\)) algorithm saved more energy than DFSA, GDFSA, and FDFSA methods by up to 40, 21.8, and \(22.2\%\) when the number of successfully identified tags is 1020.
Fig. 8

Simulation results: tag energy consumption

4 Conclusion

In this paper, an FSA-CDMA anti-collision algorithm has been proposed for tag identification; this was suitable for EPC global C1 Gen2 systems. Our algorithm is based on the mechanism of a CDMA technique of in-frame analysis and has a lower complexity but nevertheless, proved to be an efficient and accurate estimation method capable of handling tag backlog. The simulation results reveal that our proposed scheme outperforms existing ALOHA-based algorithms in terms of time efficiency, average identification time for one tag, and energy consumption of the tags. However, how to choose and design an appropriate PN sequence instead of the current orthogonal sequence can be considered as a valid future research extension of this work.

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments. This job is supported by Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 2013JK1139) and China Postdoctoral Science Foundation (No. 2013M542370) and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20136118120010). And this project is also supported by NSFC Grant (Program Nos. 11301414 and 11226173 and 51409213). This work is also supported by Shaanxi Province Hundred Talents Program, Natural Science Foundation of China under Grants 61272509, Research Fund for the Doctoral Program of Higher Education 20136118110002, Natural Science Foundation of Shaanxi Province 2016JM6058.

References

  1. 1.
    Finkenzeller, K. (2003). RFID handbook: Fundamentals and applications in contactless smart cards and identification (2nd ed., pp. 1–9). John Wiley & Sons Inc.Google Scholar
  2. 2.
    Klair, D. K., Chin, K. W., & Raad, R. (2010). A survey and tutorial of RFID anti-collision protocols. IEEE Communications Surveys and Tutorials, 12(3), 400–421.CrossRefGoogle Scholar
  3. 3.
    Vogt, H. (2002). Efficient object identification with passive RFID tags. In International conference on pervasive computing (pp. 98–113) London.Google Scholar
  4. 4.
    Solic, P., Radic, J., & Rozic, N. (2014). Energy efficient tag estimation method for Aloha-based RFID systems. IEEE Sensors Journal, 14(10), 3637–3647.CrossRefGoogle Scholar
  5. 5.
    Su, J., Sheng, Z., Hong, D., & Wen, G. (2016). An effective frame breaking policy for dynamic framed slotted Aloha in RFID. IEEE Communications Letters, 20(4), 692–695.CrossRefGoogle Scholar
  6. 6.
    Su, J., Wen, G., & Hong, D. (2015). A new RFID anti-collision algorithm based on the Q-ary search scheme. Chinese Journal of Electronics, 24(4), 679–683.CrossRefGoogle Scholar
  7. 7.
    Jian, S. U., et al. (2016). An efficient anti-collision algorithm based on improved collision detection scheme. IEICE Transactions on Communications, 99(2), 465–470.Google Scholar
  8. 8.
    Qian, C., et al. (2013). ASAP: Scalable collision arbitration for large RFID systems. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1277–1288.CrossRefGoogle Scholar
  9. 9.
    Yu, P., et al. (2013). Reducing tag collision in radio frequency identification systems by using a grouped dynamic frame slotted ALOHA algorithm. Acta Physica Sinica, 62(14), 148401.Google Scholar
  10. 10.
    Chen, W.-T. (2014). A feasible and easy-to-implement anticollision algorithm for the EPCglobal UHF class-1 generation-2 RFID protocol. IEEE Transactions on Automation Science and Engineering, 11(2), 485–491.CrossRefGoogle Scholar
  11. 11.
    Mazurek, G. (2009). Active RFID system with spread-spectrum transmission. IEEE Transactions on Automation Science and Engineering, 6(1), 25–32.CrossRefGoogle Scholar
  12. 12.
    Loeffler, A., Schuh, F., & Gerhaeuser, H. (2010). Realization of a CDMA-based RFID system using a semi-active UHF transponder. In 2010 6th international conference on wireless and mobile communications (ICWMC). IEEE.Google Scholar
  13. 13.
    Demeechai, T., & Siwamogsatham, S. (2011). Using CDMA to enhance the MAC performance of ISO/IEC 18000-6 type C. IEEE Communications Letters, 15(10), 1129–1131.CrossRefGoogle Scholar
  14. 14.
    Riordan, J. (2012). Introduction to combinatorial analysis. North Chelmsford: Courier Corporation.Google Scholar
  15. 15.
    Jiang, Y., Zhang, H., Zhang, H., Liu, H., Song, X., Gu, M., et al. (2015). Design of mixed synchronous/asynchronous systems with multiple clocks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2220–2232.CrossRefGoogle Scholar
  16. 16.
    Jiang, Y., Zhang, H., Zhang, H., Liu, H., Song, X., Gu, M., et al. (2015). Design of mixed synchronous/asynchronous systems with multiple clocks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2220–2232.CrossRefGoogle Scholar
  17. 17.
    Bin, G., & Sheng, V. S. (2016). A robust regularization path algorithm for v-support vector classification. IEEE Transactions on Neural Networks and Learning Systems,. doi: 10.1109/TNNLS.2016.2527796.Google Scholar
  18. 18.
    Wei, W., & Qi, Y. (2011). Information potential fields navigation in wireless Ad-Hoc sensor networks. Sensors, 11(5), 4794–4807.CrossRefGoogle Scholar
  19. 19.
    Jiang, Y., Zhang, H., Li, Z., Deng, Y., Song, X., Gu, M., et al. (2015). Design and optimization of multiclocked embedded systems using formal techniques. IEEE Transactions on Industrial Electronics, 62(2), 1270–1278.CrossRefGoogle Scholar
  20. 20.
    Jiang, Y., Zhang, H., Song, X., Jiao, X., William, N. N., Hung, W. N., et al. (2013). Bayesian-network-based reliability analysis of PLC systems. IEEE Transactions on Industrial Electronics, 60(11), 5325–5336.CrossRefGoogle Scholar
  21. 21.
    Wei, W., et al. (2014). GI/Geom/1 queue based on communication model for mesh networks. International Journal of Communication Systems, 27(11), 3013–3029.Google Scholar
  22. 22.
    Wei, W., Fan, X., Song, H., Fan, X., & Yang, J. (2016). Imperfect information dynamic Stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Transactions on Service Computing. doi: 10.1109/TSC.2016.2528246.
  23. 23.
    Zhang, H., Jiang, Y., Hung, W. N. N., Song, X., Gu, M., & Sun, J.-G. (2014). Symbolic analysis of programmable logic controllers. The IEEE Transactions on Computers, 63(10), 2563–2575.CrossRefGoogle Scholar
  24. 24.
    Wei, W., Qiang, Y., & Zhang, J. (2013). A bijection between lattice-valued filters and lattice-valued congruences in residuated lattices. Mathematical Problems in Engineering. doi: 10.1155/2013/908623.
  25. 25.
    Wei, W., Srivastava, H. M., Zhang, Y., Wang, L., Shen, P., & Zhang, J. (2014). A local fractional integral inequality on fractal space analogous to Andersons inequality. In Abstract and Applied Analysis (Vol. 2014). Hindawi Publishing Corporation.Google Scholar
  26. 26.
    Angerer, C., Langwieser, R., & Rupp, M. (2010). RFID reader receivers for physical layer collision recovery. IEEE Transcations on Communications, 58(12), 3526–3537.CrossRefGoogle Scholar
  27. 27.
    Su, W., Alchazidis, N. V., & Ha, T. T. (2010). Multiple RFID tags access algorithm. IEEE Transactions on Mobile Computing, 3(2), 174–187.Google Scholar
  28. 28.
    Li, J., Li, X., Yang, B., & Sun, X. (2015). Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security, 10(3), 507–518.CrossRefGoogle Scholar
  29. 29.
    Jiang, Y., Yang, Y., Liu, H., Kong, H., Gu, M., & Sun, J.-G., et al. (2016). From Stateflow simulation to verified implementation: A verification approach and a real-time train controller design. In RTAS (pp. 231–241).Google Scholar
  30. 30.
    Landaluce, H., Perallos, A., Bengtsson, L., & Carcia, I. J. (2014). Simplified computation in memoryless anti-collision RFID identification protocols. Electronics Letters, 50(17), 1250–1252.CrossRefGoogle Scholar
  31. 31.
    EPCglobal. (2013). EPC radio-frequency identify protocols class-1 generation-2 UHF RFID protocol for communications at 860 MHz–960 MHz ver. 2. 2. 0.Google Scholar
  32. 32.
    Pan, Zhaoqing, Zhang, Yun, & Kwong, Sam. (2015). Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Transactions on Broadcasting, 61(2), 166–176.CrossRefGoogle Scholar
  33. 33.
    Xia, Z., Wang, X., Sun, X., & Wang, Q. (2015). A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems, 27(2), 340–352.CrossRefGoogle Scholar
  34. 34.
    Zhangjie, F., Ren, K., Shu, J., Sun, X., & Huang, F. (2015). Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Transactions on Parallel and Distributed Systems,. doi: 10.1109/TPDS.2015.2506573.
  35. 35.
    Bin, G., Sheng, V. S., Tay, K. Y., Romano, W., & Li, S. (2015). Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems, 26(7), 1403–1416.Google Scholar
  36. 36.
    Wei, W., Yang, X.-L., Shen, P.-Y., & Zhou, B. (2012). Holes detection in anisotropic sensornets: Topological methods. International Journal of Distributed Sensor Networks, 2012, 1–9.Google Scholar

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© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer and EngineeringXi’an University of TechnologyXi’anPeople’s Republic of China
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  3. 3.Department of Electrical, Computer, Software, and Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachUSA
  4. 4.Department of EngineeringJacksonville UniversityJacksonvilleUSA
  5. 5.School of Automation and Information EngineeringXi’an University of TechnologyXi’anPeople’s Republic of China

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