# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 7 Conclusions  
In this paper, we established an identification model for mobile RFID systems and proposed an ACDQT algorithm for fast-moving tag identification. In ACDQT, a new multi-round admission control mechanism is designed to admit reasonable tags participating in each reading round. Next, a novel dynamic query tree strategy was developed to quickly identify tags in each round. Theoretical analysis was conducted to calculate the number of slots and average time needed to identify tags in a single round. We conducted numerous simulations to evaluate the identification performance in a single round and in multiple rounds in conveyor belt systems. The performance of ACDQT is also compared with the most related benchmark works. Simulation results demonstrate that ACDQT outperforms existing works in diverse situations. In our future work, we will consider using a machine learning method for tag flow estimation in the admission control process.  
Methodology, J.P. and N.Z.; conceptualization, L.Z. and L.L.; formal analysis, M.F.; validation, Q.H. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61902182), the Natural Science Foundation of Jiangsu Province of China (No. BK20190409), the Aeronautical Science Foundation of China (No. 2016 ZC52029), Qing Lan Project of Jiangsu Province of China, and China Postdoctoral Science Foundation (No. 2019 TQ0153) and the Future Network Scientific Research Fund Project (FNSRFP-2021-ZD-4).  
Not applicable.  
**Data Availability Statement:** No public data link are provided in this work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Single Round**} & \multicolumn{3}{c}{**Number of Slots**} & \multicolumn{2}{c}{**Average Number of Message Bits**} & \multicolumn{2}{c}{**Time Performance**} \\ \cline{2-7}  & **Collision** & **Idle** & **Reader Side** & **Tag Side** & \
\begin{tabular}{c} **Average** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ **Efficiency** \\ \end{tabular} \\ \hline \multirow{4}{*}{n = 500} & ACDQT & **219.5** & 525.1 & 44.3 & **136.6** & **1.19 ms** & **50.4\%** \\ \cline{2-7}  & MCT & 289.5 & 372.5 & **41.2** & 150.1 & 1.25 ms & 47.7\% \\ \cline{2-7}  & DPPS & 304.5 & **245.1** & 68 & 153.5 & 1.41 ms & 42.6\% \\ \cline{2-7}  & CwT & 498 & 351.1 & 178.1 & 136.7 & 1.95 ms & 30.8\% \\ \hline \multirow{4}{*}{n = 1500} & ACDQT & **694.7** & 1669.3 & 46.6 & 136.6 & **1.21 ms** & **49.6**\% \\ \cline{2-7}  & MCT & 870.4 & 1115.3 & **42.1** & 147.6 & 1.24 ms & 48.1\% \\ \cline{2-7}  & DPPS & 904.6 & **716.8** & 69.1 & 150.4 & 1.41 ms & 42.7\% \\ \cline{2-7}  & CwT & 1498 & 1059.1 & 182.8 & **134.3** & 1.97 ms & 30.5\% \\ \hline \multirow{4}{*}{fast moving} & \multicolumn{3}{c}{tag flow rate = 300} & \multicolumn{3}{c}{tag flow rate = 500} & \multicolumn{3}{c}{tag flow rate = 700} \\ \cline{2-7}  & throughput & tag lost ratio & throughput & tag lost ratio & throughput & tag lost ratio \\ \hline ACDQT & **291.99** & **0** & **470.54** & **0.01** & **509.68** & **0.22** \\ \hline DCT & 126.18 & 0.56 & 126.58 & 0.74 & 126.23 & 0.80 \\ \hline BFSA & 181.89 & 0.28 & 170.93 & 0.57 & 160.55 & 0.71 \\ \hline EBD & 279.85 & 0.01 & 274.71 & 0.33 & 271.58 & 0.51 \\ \hline \end{tabular}  
* The bold values are the best performance in each column.  
\end{table}
Table 7: Simulation results of the comparable algorithms in single round and fast-moving situations.