Modeling and analyzing RFID Generation-2 under unreliable channels
Introduction
The RFID Generation-2 (shortly, Generation-2) system has emerged as a promising important technique for provisioning of the automatic identification to support Internet of Things such as the delivery system (Purio et al., 2016) and the logistics (Jakkhupan et al., 2011), (Wang et al., 2020), the indoor locations (Papapostolou and Chaouchi, 2011), etc. The wireless channel between the reader (namely, interrogator) and tags is a key for the Generation-2 system to achieve high performance. Therefore, for obtaining the more optimal tag identification and designing the effective RFID hardware (Shi et al., 2018), the design of the communication algorithm between reader and tags should be based on the fact that the realistic channels are unreliable (Zhang and Soong, 2005). Fig. 1 depicts the link timing between reader and tags in the Generation-2 (EPC, 2008), and the timing is based on the unreliable channels. There are four slots (the Single Tag Reply, the Collided Reply, the No Reply, and the Invalid Reply), instead of the three slots (the success slot, the collision slot, and the empty slot) in the existing literatures mainly regarding the ideal channels (Zhu and Yum, 2010a).
In the Internet of Things, there exist some factors affecting the reader-tag communication under the unreliable channel, for example, the multi-path fading effect, the spectrum constraint, the energy restriction, the mobility, the noise, and the difference distance between the reader and tags (Molisch, 2011). These factors of the RFID Generation-2 will cause some bad results such as the packet loss (Goyal and Kumar, 2015), the error code (Shi et al., 2018), (Altamirano and Almeida, 2013), and the capture effect (Jun et al., 2016), like ones of other wireless communications. These caused bad results can lead to performance degradation, for example, the multi-path effect causes more time spent than the ideal environment (Jeon and Cho, 2009a). It is noteworthy that these bad results should be used to research the RFID generation-2 under unreliable channel for reflecting the real situations to obtain the identification performance better than the one without considering usage of them.
In terms of the tag identification, although the current DFSA protocols can simply drop and re-transmit when the packets are missing or collided, the determination of the optimal frame length affecting the tag identification efficiency should fully consider those bad results (the packet loss, the error code, and the capture effect). In the unknown tag detection of large-scale RFID systems with unreliable channels (LiuLi and Min, 2014), it is very important to minimize the detection and recognition time of unknown tag. As a result, under certain detection accuracy, the choice of the optimal frame length of the unknown tag detection protocol also requires those bad results to be fully considered. When those bad results (the packet loss, the error code, and the capture effect) are adopted in the models, they can be considered as parameters of the models. Of course, the parameters are also important for other aspects, such as the miss tag detection and recognition (Xie et al., 2014), the blocked RFID tags identification (Liu et al., 2018). Although some commercial RFID devices (e,g., Impinj R420,Alien) use the re-transmission, the spread spectrum frequency hopping and other modes to improve the reliability of the communication, the identification efficiency of anti-collision algorithm can not be optimized without the consideration of these parameters (the packet loss, the error code, and the capture effect). So, it is very important to model the RFID Generation-2 using those parameters for obtaining the best performance, for example, the tag identification efficiency, the time minimization of the unknown tag detection and recognition, etc. Moreover, the influences of these parameters on performance can be different, and the costs of changing these parameters by circuit design also can be variant. Thus, the high performance price ratio can be obtained by analyzing the sensitivities influence of those parameters on the performance using the builded models.
In summary, the performance modeling and analysis of the RFID Generation-2 considering those parameters in unreliable channel are the basis of the related research. Based on the performance modeling and analysis, we can further study high-performance tag inventory algorithms (such as tag anti-collision algorithm, miss tag detection algorithm, etc.) and effective RFID hardware design. For the applications of the inventory algorithms, the algorithms can be implemented in the readers with no tag change or less tag change. In the next section, we will analyze the situation of modeling the RFID Generation-2 using these parameters.
The rest of this paper is organized as follows: Section II presents related works, Section III lists our contributions, Section IV gives the analysis of tag identification that helps to understand subsequent sections. Section V derives models. The effectivenesses of the proposed models are verified by comparing the simulation results and the numerical ones in Section VI. Section VII gives performance analyses of the Generation-2 using these models. Finally, Section VIII concludes the research.
Section snippets
Related works
E. Vahedi et al. analyze the performance of the Generation-2 system using the proposed Markov chain in the assumption of the reliable channels (Vahedi et al., 2014). L. Zhu et al. model the reading process as a Markov Chain and derive the optimal reading strategy through first-passage-time analysis under ideal channels (Zhu and Yum, 2010b). A. Ferdous et al. analyze the gains in efficiency when the tag estimation algorithms are used in conjunction with the Generation-2 at different points in
Our contributions
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Comprehensively considering these key parameters of the unreliable channel, we get the detailed and realistic analyses of the tag identification in the Generation-2 system, which lays a solid foundation for the performance modeling and analysis of the system.
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The new reasonable mathematical models are proposed to study the performance of the Generation-2 system under the realistic unreliable channels. The packet loss and error code express in the models of the Single Tag Reply, Collided Reply,
Analyses of tag identification procedure under unreliable channels
This section will analyze the tag identification procedure of the Generation-2 under the unreliable channels, including steps and anti-collision algorithm of the tag identification. Although some literatures have described them, they do not consider those bad results of the unreliable channel to carry out a systematic analysis of the tag identification process. In order to provide the corresponding basis for the later performance modeling, we take into account the bad results for doing the
Mathematical models
On the basis of considering the unreliable channels, we analyze the various possible occurrence causes of the four slots (No Reply slot, Single Tag Reply slot, Invalid Reply slot and Collision Reply slot) in the Generation-2 system. Further, we comprehensively take occurrence causes into account in the model of the four slots.
Validation of models
It is very important whether these models effectively express the tag identification procedure of the Generation-2 system under the unreliable channels, because the effectiveness of the models determines their applicability to the practice. In this paper, the effectivenesses of the proposed models are verified by comparing the simulation results and the numerical ones and also by their fitness for a instance. Similar model validation method has been used to validate the model of the
Performance analyses
After validating the accuracy of these models, we use these models to investigate the impacts of their parameters on the tag identification performance. Especially, we use the parameter sensitivity analysis method to find the key factors that affect the tag identification performance. Because the four parameters can reflect the real situation of unreliable channel objectively, which is mentioned in the introduction, the performance analysis through these models considering the four parameters
Conclusions
Driven by the importance of evaluating and optimizing the generation-2 system using more realistic factors, this paper has presented new performance models with the four feasible calculation methods of parameters, and calculation of the time efficiency and the energy efficiency in the presence of the unreliable channels, which are captured by the packet loss, error code, and capture effect. The extensive simulation and the fitness for a instance have been conducted to validate the accuracy of
Credit author statement
Yihong Chen: Conceptualization, Methodolog, Writing-Original draft preparation. Quanyuan Feng: Writing-Reviewing and Editing. Xiaolin Jia: Validation, Investigation. Huayue Chen: Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China (61871330), in part by Key Project of the National Natural Science Foundation of China (62090012), in part by the Sichuan Provincial Science and Technology Important Projects (19ZDYF2904, 2018ZDZX0148, 2018GZDZX0001, 2020YFS0360), also in part by the Doctoral Foundation Project (16E008), the Innovation Team Project (CXTD2017-5), and the Meritocracy Research Funds (17YC148) of China West Normal University.
Yihong Chen, received his Ph.D. degree in computer science from Southwest Jiaotong University, China in 2015. He is working as a professor. He has been honored as “the candidate leader of science and technology” of Sichuan province because of his contribution. His research interest includes RFID, Internet of Things, and distributed machine learning. He has published more than 40 papers in journals such as IEEE Transactions series. He served as TPC member of some conferences such as IEEE
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Yihong Chen, received his Ph.D. degree in computer science from Southwest Jiaotong University, China in 2015. He is working as a professor. He has been honored as “the candidate leader of science and technology” of Sichuan province because of his contribution. His research interest includes RFID, Internet of Things, and distributed machine learning. He has published more than 40 papers in journals such as IEEE Transactions series. He served as TPC member of some conferences such as IEEE International Conference on Ubiquitous Intelligence and Computing (2019). He is also a senior member of the CCF, and a member of the CCF IoT Technology Committee. (corresponding author)
Quanyuan Feng (M′06-SM′08), received his Ph.D. degree from Southwest Jiaotong University, Chengdu, P.R.China in 2000, and he is currently a professor and an advisor of Ph.D. candidates in the School of Information Science and Technology, Southwest Jiaotong University, Chengdu, P.R.China. He has been honored as “the excellent expert” and “the leader of science and technology” of Sichuan province because of his outstanding contributions. In the last 5 years, he has published more than 240 papers. His current research interests include RF and microwave devices, integrated circuits design, RFID technolog, etc. (corresponding author)
Xiaolin Jia, received his Ph.D. degree in computer science from Southwest Jiaotong University, China in 2013. He is working as a professor. His research interest includes RFID, Internet of Things. He has published more than 30 papers in journals such as IEEE Transactions series. He has been honored as the Scientific and Technological Progress Award Winner of Sichuan Province, the High-level Overseas Talents of Sichuan Province, the Leading Talents of High-Tech Industry and Strategic Emerging Industry of Mianyang, the Excellent Teacher of Mianyang, the Top Ten Outstanding Youth of SWUST, etc. He has presided and participated in many R&D projects from the National Natural Science Foundation of China (NNSF), the National High-Tech Research & Development Program of China (863 Program).
Huayue Chen, received the B.S. degree in Computer Science and Technology, from Sichuan Normal College, Nanchong, China, in 2002. the M.S. degree in Computer Software and Theory from Chongqin University, Chongqin, China, in 2005. From 2015 to now, she is a PH.D degree candidate in Geodetection and Information Technology in Chengdu Technology University, Chengdu, China. From 2012 to now, she was an Associate Professor with China West Normal University, Nanchong, China. Her research interest includes artificial intelligence, optimization method.