The Bit Query for Labels in a Binary Tree-Based Anti-Collision Recognition Algorithm

Authors

  • Chenyao Sun Department of Computer Systems Engineering, University of Glasgow, United Kingdom

DOI:

https://doi.org/10.51983/ijiss-2023.13.2.3853

Keywords:

Radio Frequency Identification, Bit Blocking, Query Trees, Conflict Avoidance Algorithms, Predictive Controls

Abstract

With its benefits including recognition distance, penetration ability, and multi-object recognition, radio frequency identification technology is a non-contact automatic identification method that is currently being used extensively in a variety of industries, including document anti-counterfeiting, automation, transportation, control management, and product services. An efficient anti-collision mechanism, such as anti-collision algorithms or anti-collision protocols, must be established in order to coordinate communication between the tag and the reader in the event that an RFID system experiences signal interference in the wireless channel, conflict, or collision, which will result in tag recognition or data collection failure. In response to tag conflicts in radio frequency identification (RFID) systems, a novel hybrid blocking query tree algorithm is suggested that leverages blocking prediction and child node hedging techniques to overcome the issues of lengthy time intervals and high communication complexity. The technique uses quadruple and bifurcated query trees as the foundation for its conflict avoidance strategy. The approach minimizes the conflict time and prevents the construction of idle subunits by forcing the reader to create new query prefixes via prediction, which is accomplished by employing locking commands to extract and forecast information about the conflicting bits. According to simulation data, this strategy performs better in terms of lowering the overall number of time slots and communication complexity as well as enhancing identification and labelling abilities than the current adaptive multi-tree search (RLAMS) and improved hybrid query tree (IHQT) techniques. It greatly increases the effectiveness of label identification while lowering the overall amount of time slots and communication complexity.

References

Ai, Y., Bai, T., Xu, Y., & Zhang, W. (2022). Anti‐collision algorithm based on slotted random regressive‐style binary search tree in RFID technology. IET Communications, 16(10), 1200-1208.

Akavia, A., Leibovich, M., Resheff, Y. S., Ron, R., Shahar, M., & Vald, M. (2022). Privacy-preserving decision trees training and prediction. ACM Transactions on Privacy and Security, 25(3), 1-30.

Alimohammadi, H., & Ahmadi, M. (2019). Clustering-based many-field packet classification in software-defined networking. Journal of Network and Computer Applications, 147, 102428.

Baghdad, A. (2022). An improved RFID anti-collision protocol (IMRAP) with low energy consumption and high throughput. Scientific African, 16, e01209.

Besta, M., Gerstenberger, R., Peter, E., Fischer, M., Podstawski, M., Barthels, C., & Hoefler, T. (2023). Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. ACM Computing Surveys, 56(2), 1-40.

Gul, F., Mir, I., Alarabiat, D., Alabool, H. M., Abualigah, L., & Mir, S. (2022). Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning. Journal of Parallel and Distributed Computing, 169, 171-184.

Guo, K., Xie, X., Chen, S., Qi, H., & Li, K. (2022). Efficient collision-slot utilization for missing tags identification in RFID system. Computer Communications, 195, 61-72.

Hu, J. G., Mei, W. Z., Wu, J., Li, J. W., & Wang, D. M. (2023). A Fully Integrated RFID Reader SoC. Micromachines, 14(9), 1691.

Khalil, G., Doss, R., & Chowdhury, M. (2019). A comparison survey study on RFID-based anti-counterfeiting systems. Journal of Sensor and Actuator Networks, 8(3), 37.

Kumar, A., Aggarwal, A., & Gopal, K. (2021). A novel and efficient reader-to-reader and tag-to-tag anti-collision protocol. IETE Journal of Research, 67(3), 301-312.

Lai, Y. C., Chen, S. Y., Hailemariam, Z. L., & Lin, C. C. (2022). A bit-tracking knowledge-based query tree for RFID tag identification in IoT systems. Sensors, 22(9), 3323.

Mu, Y., Ni, R., Sun, Y., Zhang, T., Li, J., Hu, T., & Tyas, T. L. (2021). A novel hybrid tag identification protocol for large-scale RFID systems. Computers, Materials & Continua, 68, 2516-2526.

Munir, A., Laskar, M. T. R., Hossen, M. S., & Choudhury, S. (2019). A localized fault-tolerant load balancing algorithm for RFID systems. Journal of Ambient Intelligence and Humanized Computing, 10, 4305-4317.

Peng, J., Zhang, L., Fan, M., Zhao, N., Lei, L., He, Q., & Xia, J. (2023). An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification. Applied Sciences, 13(4), 2228.

Rasina Begum, B., & Chitra, P. (2023). SEEDDUP: a three-tier SEcurE data DedUPlication architecture-based storage and retrieval for cross-domains over cloud. IETE Journal of Research, 69(4), 2224-2241.

Seo, C., Noh, Y., Abebe, M., Kang, Y. J., Park, S., & Kwon, C. (2023). Ship collision avoidance route planning using CRI-based A* algorithm. International Journal of Naval Architecture and Ocean Engineering, 100551.

Su, J., Chen, Y., Sheng, Z., Huang, Z., & Liu, A. X. (2020). From M-ary query to bit query: a new strategy for efficient large-scale RFID identification. IEEE Transactions on Communications, 68(4), 2381-2393.

Sulaiman, S., & Sudheer, A. P. (2022). Modeling of a wheeled humanoid robot and hybrid algorithm-based path planning of wheelbase for dynamic obstacles avoidance. Industrial Robot: The International Journal of Robotics Research and Application, 49(6), 1058-1076.

Tang, Z. J., Guo, Y., & Liu, Q. (2019). Research on an improved fusion RFID collision avoidance algorithm. Journal of Communications Technology, Electronics and Computer Science, 22, 6-19.

Umelo, N. H., Noordin, N. K., Rasid, M. F. A., Geok, T. K., & Hashim, F. (2022). Grouping based radio frequency identification anti-collision protocols for dense internet of things application. International Journal of Electrical and Computer Engineering, 12(6), 5848.

Umelo, N. H., Noordin, N. K., Rasid, M. F. A., Tan, K. G., & Hashim, F. (2023). Efficient Tag Grouping RFID Anti-Collision Algorithm for Internet of Things Applications Based on Improved K-Means Clustering. IEEE Access, 11, 11102-11117.

Wang, L., Luo, Z., Guo, R., & Li, Y. (2023). A Review of Tags Anti-Collision Identification Methods Used in RFID Technology. Electronics, 12(17), 3644.

Wu, J., Chen, X., Bie, Y., & Zhou, W. (2023). A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification. Accident Analysis & Prevention, 180, 106907.

Yaacob, M., Daud, S. M., & Azizan, A. (2019). A review of deterministic anti-collision algorithm of passive RFID systems. Open International Journal of Informatics, 7(1), 8-25.

Zhang, H., Gao, L., Luo, H. G., & Zhai, Y. (2022). Research on the RFID anticollision strategy based on decision tree. Wireless Communications and Mobile Computing, 2022, 1-7.

Zhou, H., Feng, P., & Chou, W. (2023). A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment. Industrial Robot: The International Journal of Robotics Research and Application, 50(1), 94-106.

Zhou, J., Liu, Q., Jiang, Q., Ren, W., Lam, K.-M., & Zhang, W. (2023). Underwater image restoration via adaptive dark pixel prior and color correction. International Journal of Computer Vision. DOI: 10.1007/s11263-023-01853-3.

Zhou, J., Zhang, D., & Zhang, W. (2023). Cross-view enhancement network for underwater images. Engineering Applications of Artificial Intelligence, 121, 105952.

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Published

04-12-2023

How to Cite

Sun, C. (2023). The Bit Query for Labels in a Binary Tree-Based Anti-Collision Recognition Algorithm. Indian Journal of Information Sources and Services, 13(2), 68–75. https://doi.org/10.51983/ijiss-2023.13.2.3853