Computer Science ›› 2019, Vol. 46 ›› Issue (6): 124-127.doi: 10.11896/j.issn.1002-137X.2019.06.018

Previous Articles     Next Articles

Anti-collision Algorithm Based on Q-learning for RFID Multiple Readers

YUAN Yuan, ZHENG Jia-li, SHI Jing, WANG Zhe, LI Li   

  1. (School of Computer and Electronics Information,Guangxi University,Nanning 530004,China)
    (Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China)
  • Received:2018-05-14 Published:2019-06-24

Abstract: Due to the collision problem between multiple readers and tags communication in RFID system,this paper modeled the problem as a Markov decision process,and proposed an anti-collision algorithm based on Q-learning.By continuously interacting with the environment,the Q-value function is generated,as well as the optimal channel resources allocation.The complex hierarchical structure in HiQ algorithm is eliminated for simplifying the system model.The algorithm not only imports the concept of ε-greedy strategy to obtain the global optimal solution,but also improves the reward function to get the best state.Simulation results show that compared with HiQ and EHiQ,this intelligent algorithm can adaptively assign different channels to the reader for data transmission,therefore reduces the collision rate and improves the channel utilization and throughput rate.

Key words: Q-learning, Q-value, Radio frequency identification, Reader anti-collision

CLC Number: 

  • TP301.6
[1]WEI D X,ZHENG J L,LI L L,et al.Study of Novel Adaptive Multi-tree Anti-collision Search Algorithm[J].Computer Scien-ce,2013,40(10):52-55.(in Chinese)
[2]XIE L,YIN Y F,CHEN X,et al.RFID Data Management:Algorithms,Protocols and Performance Evaluation[J].Chinese Journal of Computers,2013,36(3):457-470.(in Chinese)
[3]WALDROP J,ENGELS D W,SARMA S E.Colorwave:An Anticollision Algorithm for the Reader Collision Problem[C]∥IEEE International Conference on Communications.IEEE,2003:1206-1210.
[4]BIRARI S M,IYER S.PULSE:A MAC Protocol for RFID Net-works[C]∥International Conference on Embedded and Ubiquitous Computing.Springer-Verlag,2005:1036-1046.
[5]SEO H,LEE C.A New GA-Based Resource Allocation Scheme for a Reader-to-Reader Interference Problem in RFID Systems[C]∥IEEE International Conference on Communications.IEEE,2010:1-5.
[6]TIAN J,FAN Y,ZHU Y,et al.RFID Reader Anti-collision Using Chaos Neural network Based on Annealing Strategy[C]∥World Congress on Intelligent Control and Automation,2008(WCICA 2008).IEEE,2008:6128-6132.
[7]HO J,ENGELS D W,SARMA S E.HiQ:a Hierarchical Q-learning Algorithm to Solve the Reader Collision Problem[C]∥2006 International Symposium on Applications and the Internet Workshops.IEEE,2006:88-91.
[8]GOLSORKHTABARAMIRI M,ISSAZADEHKOJIDI N.A Distance Based RFID Reader Collision Avoidance Protocol for Dense Reader Environments[J].Wireless Personal Communications,2017,95(2):1-18.
[9]SAADI H,TOUHAMI R,YAGOUB M C E,et al.TDMA-SDMA based RFID algorithm for fast detection and efficient collision avoidance[J].International Journal of Communication Systems,2018,31(3).
[10]YANG J,WANG Y H,CAI Q L,et al.EHiQ:A RFID Reader MAC Protocol Based on Enhanced HiQ[J].Computer Science,2011,38(7):85-87.(in Chinese)
[11]LIU Q,ZHAI J W,ZHANG Z Z,et al.A Survey on Deep Reinforcement Learning[J].Chinese Journal of Computers,2017,40(1):1-28.(in Chinese)
[12]GU J Y,ZHANG G A,BAO Z H.Joint multi-path routing and channel assignment strategy for cognitive wireless mesh networks[J].Computer Science,2011,38(5):45-48.(in Chinese)
[13]AVALLONE S,BANCHS A.A Channel Assignment and Routing Algorithm for Energy Harvesting Multiradio Wireless Mesh Networks[J].IEEE Journal on Selected Areas in Communications,2016,34(5):1463-1476.
[1] FAN Jing-yu, LIU Quan. Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [J]. Computer Science, 2022, 49(6): 335-341.
[2] ZHOU Qin, LUO Fei, DING Wei-chao, GU Chun-hua, ZHENG Shuai. Double Speedy Q-Learning Based on Successive Over Relaxation [J]. Computer Science, 2022, 49(3): 239-245.
[3] LIU Jia-chen, QIN Xiao-lin, ZHU Run-ze. Prediction of RFID Mobile Object Location Based on LSTM-Attention [J]. Computer Science, 2021, 48(3): 188-195.
[4] LIU Ling-yun, QIAN Hui, XING Hong-jie, DONG Chun-ru, ZHANG Feng. Incremental Classification Model Based on Q-learning Algorithm [J]. Computer Science, 2020, 47(8): 171-177.
[5] ZHENG Shuai, LUO Fei, GU Chun-hua, DING Wei-chao, LU Hai-feng. Improved Speedy Q-learning Algorithm Based on Double Estimator [J]. Computer Science, 2020, 47(7): 179-185.
[6] LI Long-fei,ZHANG Jing-zhou,WANG Peng-de,GUO Peng-jun. P2P Network Search Mechanism Based on Node Interest and Q-learning [J]. Computer Science, 2020, 47(2): 221-226.
[7] LU Hai-feng, GU Chun-hua, LUO Fei, DING Wei-chao, YUAN Ye, REN Qiang. Virtual Machine Placement Strategy with Energy Consumption Optimization under Reinforcement Learning [J]. Computer Science, 2019, 46(9): 291-297.
[8] LIANG Yuan, YUAN Jing-ling, CHEN Min-cheng. Prefetching Algorithm of Sarsa Learning Based on Space Optimization [J]. Computer Science, 2019, 46(3): 327-331.
[9] WANG Zhe, ZHENG Jia-li, LI Li, YUAN Yuan, SHI Jing. RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine [J]. Computer Science, 2019, 46(12): 120-125.
[10] SHI Jing, ZHENG Jia-li, YUAN Yuan, WANG Zhe, LI Li. RFID Multi-reader Channel Resources Allocation Algorithm Based on Whittle Index [J]. Computer Science, 2019, 46(10): 122-127.
[11] GAN Yong, WANG Kai, HE Lei. New Ownership Transfer Protocol of RFID Tag [J]. Computer Science, 2018, 45(11A): 369-372.
[12] LI Min-shuo, YAO Ming-hai. Q-learning with Feature-based Approximation for Traffic Light Control [J]. Computer Science, 2018, 45(11A): 143-145.
[13] GUAN Yang, YAN Guo-yu, WANG Ying and JIANG Sui-ping. Data Filtration Method for RFID Based Indoor RTLS [J]. Computer Science, 2017, 44(Z11): 293-296.
[14] HUANG Qi and LING Jie. Ultra-lightweight Mutual Authentication Protocol for Mobile Radio Frequency Identification [J]. Computer Science, 2017, 44(7): 111-115.
[15] ZHANG Ya-li, GUO Ya-jun, CUI Jian-qun and ZENG Qing-jang. New Ultra-lightweight RFID Authentication Protocol [J]. Computer Science, 2017, 44(1): 183-187.
Full text



No Suggested Reading articles found!