Maximum A Posteriori (MAP)-based Tag Estimation Method for Dynamic Framed-Slotted ALOHA (DFSA) in RFID Systems

Radio frequency identification (RFID) is a non-contact technology that uses radio frequency electromagnetic fields to transfer data from a tag attached to an object, for the purposes of automatic identification and tracking. One of the common problems that arise in any RFID deployment is the collision between tags which reduces the efficiency of the RFID system. Dynamic framed-slotted ALOHA (DFSA) is one of the most popular approaches to resolve the tag collision problem. In DFSA, each tag randomly selects one of the time slots of a frame and transmits its data at the slot. Unless the tag successfully transmits its data to a reader, it will try again in the next frame. It is widely known that the optimal performance of framed-slotted ALOHA is achieved when the frame size (i.e., number of time slots) is equal to the number of tags to be identified. So, a reader dynamically adjusts the next frame size according to the number of tags. Thus, it is important to accurately estimate the number of tags. In this article, we propose an accurate maximum a posteriori (MAP)-based tag estimation method with low computational complexity. The idea behind our method is to more accurately determine the most potential number of tags which draws the observed results on the basis of both a posteriori probability and a priori probability. Simulation results show that our method improves the accuracy of tag estimation and the speed of tag identification. Introduction Radio frequency identification (RFID) systems that identify tagged objects via near/far-field wireless communications to realize ubiquitous computing are drawing much attention. The operation of RFID systems often involves a situation in which numerous tags are simultaneously placed in the interrogation zone of a single reader. The tags may collide with each other, leading to retransmission of tag data that brings about a waste of bandwidth and an increase in the total delay. To resolve the tag collision problem, a number of tag anti-collision algorithms have been proposed [1]. The primary concern in the algorithms is how to read multiple tags as fast and as reliably as possible. Tag anti-collision algorithms are mainly grouped into tree-based [2] and ALOHA-based [3] algorithms. Treebased algorithms work by repeatedly splitting the group of colliding tags into two disjoint subsets. The subsets *Correspondence: [email protected] Graduate School of Information and Communication, Ajou University, Suwon 443-749, South Korea become smaller and smaller until the number of tags within a subset reduces to one, in which case the tag would be uniquely identified. However, as the number of tags increases, the performance of tree-based algorithms decreases. This is because the colliding tags are successively grouped into two subsets, and each subset may still contain many tags resulting in collisions. The tree-based algorithms have been studied extensively in the literature [4-6]. ALOHA-based algorithms are mostly referred to as Framed-Slotted ALOHA (FSA) [7,8]. In FSA, time is divided into frames of multiple slots and the reader begins its interrogation round by announcing the frame size (i.e., the number of time slots) to the tags. Each tag selects one of the time slots at random and transmits its data at the slot. Unless the tag successfully transmits its data to the reader, it will try again in the next frame. According to [9], the expected throughputU of FSA withN tags and L slots in a frame is given by U(N , L) = N L ( 1− 1 L )L−1 . (1) © 2012 Choi and Lee; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Choi and Lee EURASIP Journal onWireless Communications and Networking 2012, 2012:268 Page 2 of 12 http://jwcn.eurasipjournals.com/content/2012/1/268 It is obvious from the above equation that the throughput depends on the appropriate choice of frame size L, given the number of tags N in the interrogation range. Figure 1 shows the well-known upper bound of the throughput of e−1 that is characteristic for slotted ALOHA and also applies to FSA. The maximum throughput occurs when the frame size equals to the number of tags, i.e., L = N . Thus, for high performance, it is desirable that the frame size is dynamically adjusted according to the number of unread tags; the mechanism is referred to as Dynamic Framed-Slotted ALOHA (DFSA) [3]. However, DFSA necessitates the reader accurately estimating the number of unread tags to decide an appropriate frame size. To deal with this, various methods to estimate the number of unread tags have been studied in the literature [9-20]. In this article, we propose an accurate and simple maximum a posteriori (MAP)-based tag estimation method for DFSA in RFID systems. In the proposed scheme, we derive a probability mass function (PMF) that describes the relative probability of detection results occurring at a given number of tags and then, based on the derived PMF and the prior tag distribution (if it is postulated), determine the most potential number of tags which draws the detection results observed in a read cycle as the optimal estimate. However, this method may result in a heavy computational load due to the wide search range of tag quantity. To deal with this problem, we propose a simple iterative algorithm based on Newton’s method. In our simulations, comparison with several conventional tag estimates shows that the proposed iterative algorithm has lower computational complexity and less error. The rest of the article is organized as follows. We analyze several important tag estimation methods in the following section. In Section “The proposed tag estimation mechanism”, we propose an MAP-based tag estimation with low computational complexity. Simulation results are shown in Section “Numerical results”. Finally, we give our concluding remarks in Section “Conclusion”. Related study In DFSA, the reader begins its interrogation by first announcing the frame size to all tags within its radio range. Then, each tag randomly selects one of the available time slots and transmits its information at the selected slot. For a given time slot, only three possible outcomes can happen: idle channel, successful transmission, or collision, as shown in Figure 2. The channel is idle if no tag transmits its information at the time slot. A successful transmission means that only one tag sends its information. If two ormore tags transmit at the same time slot, the reader suffers from collision and no tag can be read. Based on the detection results, the reader dynamically adjusts the frame size for next read cycle (frame). Asmentioned in Introduction section, since the system throughput, which is defined as the ratio between success slots and the frame size, can be maximized when the frame size equals to the number of unread tags, a number of studies have focused on accurate tag estimation [9-20]. The lower bound method [10] is obtained through the observation that a collision involves at least two different tags. Suppose that after carrying out an FSA in which the frame size is set to F , the reader can observe si idle slots, ss success (or singly occupied) slots, and sc collision slots, where si + ss + sc = F . Then, the lower bound method 20 40 60 80 100 120 140 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Frame size E xp ec te d T hr ou gh pu t 10 Tags