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Автор: Grafiati
Опубліковано: 14 березня 2023

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1

Kim, Jongwan. "Hybrid Dynamic-Binary ALOHA Anti-Collision Protocol in RFID Systems." International Journal of Engineering & Technology 7, no. 2.15 (April 6, 2018): 171. http://dx.doi.org/10.14419/ijet.v7i2.15.12566.

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Анотація:
The main techniques for identifying objects in an Internet of things environment are based on radio frequency identification, in which a specific object is identified by the reader through the tag mounted on the object. When there are multiple tags in the reader’s interrogation zone, they respond simultaneously to the reader’s request, thus causing a collision between the signals sent simultaneously to the reader from those tags. Such collisions reduce the data accuracy and prolong the identification time, thus making it difficult to provide a rapid service. This paper explores a hybrid anti-collision protocol, namely, the hybrid dynamic-binary ALOHA anti-collision protocol, which is designed to prevent tag collision and to enable more stable information transmission by improving the existing tag anti-collision protocols. The proposed protocol has achieved performance enhancement by shortening the tag identification process when tag collision occurs by combining the ALOHA and binary search protocols. In contrast to the existing protocols, whereby the reader’s request is repeated after detecting a collision, the proposed protocol shortens the tag identification time by requesting only the collision bits. This contributes to a substantial reduction in the object identification time in an IoT environment.
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2

Chu, Ming, Zhihong Qian, and Xue Wang. "A multi-tag anti-collision protocol based on 8-ary adaptive pruning query tree." International Journal of Distributed Sensor Networks 14, no. 11 (November 2018): 155014771881182. http://dx.doi.org/10.1177/1550147718811823.

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Анотація:
In order to solve the problem of multi-tag anti-collision in radio frequency identification systems, a multi-tag anti-collision protocol based on 8-ary adaptive pruning query tree is proposed in this article. According to Manchester code, the highest collision bit can be detected. In the process of tag identification, the protocol only locks the target on the three consecutive bits which start from the highest collision bit. The protocol has two tag identification mechanisms, and which one is chosen is determined by the value of collision coefficient. Using the collision information characteristics of the three bits, idle timeslots are evitable, and some collision timeslots are eliminated at the same time. The 8-ary adaptive pruning query tree protocol fully takes into account several important performance indicators such as the number of query timeslots, communication complexity, transmission delay, and throughput. Both theoretical analysis and simulation results show that this protocol performs better than other tree-based anti-collision protocols. The throughput of 8-ary adaptive pruning query tree protocol remains approximately 0.625.
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3

Arjona, Laura, Hugo Simon, and Asier Ruiz. "Energy-Aware RFID Anti-Collision Protocol." Sensors 18, no. 6 (June 11, 2018): 1904. http://dx.doi.org/10.3390/s18061904.

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4

Yu, Song Sen, Yun Peng, and Jia Jing Zhang. "A Lightweight RFID Mechanism Design." Advanced Materials Research 216 (March 2011): 120–23. http://dx.doi.org/10.4028/www.scientific.net/amr.216.120.

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Анотація:
Based on the study of existing RFID security protocols and RFID anti-collision algorithms, this paper proposes a processing mechanism integrating lightweight random key double-authentication and dynamic slot-ALOHA protocol. The mechanism is simple, practical, and compatible with EPC Gen2 standards. Research shows that comparing with the other security protocols and anti-collision protocols, the new mechanism has a little complexity and tag-cost.
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5

Cmiljanic, Nikola, Hugo Landaluce, and Asier Perallos. "A Comparison of RFID Anti-Collision Protocols for Tag Identification." Applied Sciences 8, no. 8 (August 1, 2018): 1282. http://dx.doi.org/10.3390/app8081282.

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Анотація:
Radio Frequency Identification (RFID) is a technology that uses radio frequency signals to identify objects. RFID is one of the key technologies used by the Internet of Things (IoT). This technology enables communication between the main devices used in RFID, the reader and the tags. The tags share a communication channel. Therefore, if several tags attempt to send information at the same time, the reader will be unable to distinguish these signals. This is called the tag collision problem. This results in an increased time necessary for system identification and energy consumption. To minimize tag collisions, RFID readers must use an anti-collision protocol. Different types of anti-collision protocols have been proposed in the literature in order to solve this problem. This paper provides an update including some of the most relevant anti-collision protocols.
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6

Liu, Xiao Hui, Zhi Hong Qian, Gui Qin Wang, and Xu Zhang. "An Improved RFID Anti-Collision Algorithm." Advanced Materials Research 791-793 (September 2013): 1243–46. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1243.

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Анотація:
The dynamic bit-locking backoff (DBLBO) anti-collision algorithm was proposed on the basis of bit-locking backoff (BLBO) anti-collision algorithm . If there is only one collision bit When the reader is searching the locked collision bit , the reader can identify directly without queries.The proposed algorithm gives full consideration to the number of queries and throughput of the system.The analysis on simulation result indicates that DBLBO performs significantly better than the existing BLBO anti-collision algorithms. It is suitable for the RFID anti-collision protocol in a greater deal.
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7

Loganathan, Murukesan, Thennarasan Sabapathy, Mohamed Elshaikh, Mohamed Nasrun Osman, and Rosemizi Abd Rahim. "Energy efficient anti-collision algorithm for the RFID networks." Bulletin of Electrical Engineering and Informatics 8, no. 2 (June 1, 2019): 622–29. http://dx.doi.org/10.11591/eei.v8i2.1427.

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Анотація:
Energy efficiency is crucial for radio frequency identification (RFID) systems as the readers are often battery operated. The main source of the energy wastage is the collision which happens when tags access the communication medium at the same time. Thus, an efficient anti-collision protocol could minimize the energy wastage and prolong the lifetime of the RFID systems. In this regard, EPCGlobal-Class1-Generation2 (EPC-C1G2) protocol is currently being used in the commercial RFID readers to provide fast tag identification through efficient collision arbitration using the Q algorithm. However, this protocol requires a lot of control message overheads for its operation. Thus, a reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to provide better time system efficiency while being energy efficient through the minimization of control message overheads. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-Class 1 Generation 2 algorithms that are currently being used in the commercial readers. The results show conclusively that the proposed RL-DFSA performs identically to the very efficient EPC-C1G2 protocol in terms of time system efficiency but readily outperforms the compared protocol in the number of control message overhead required for the operation.
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8

Arjona, Laura, Hugo Landaluce, Asier Perallos, and Enrique Onieva. "Dynamic Frame Update Policy for UHF RFID Sensor Tag Collisions." Sensors 20, no. 9 (May 9, 2020): 2696. http://dx.doi.org/10.3390/s20092696.

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Анотація:
The current growing demand for low-cost edge devices to bridge the physical–digital divide has triggered the growing scope of Radio Frequency Identification (RFID) technology research. Besides object identification, researchers have also examined the possibility of using RFID tags for low-power wireless sensing, localisation and activity inference. This paper focuses on passive UHF RFID sensing. An RFID system consists of a reader and various numbers of tags, which can incorporate different kinds of sensors. These sensor tags require fast anti-collision protocols to minimise the number of collisions with the other tags sharing the reader’s interrogation zone. Therefore, RFID application developers must be mindful of anti-collision protocols. Dynamic Frame Slotted Aloha (DFSA) anti-collision protocols have been used extensively in the literature because EPCglobal Class 1 Generation 2 (EPC C1G2), which is the current communication protocol standard in RFID, employs this strategy. Protocols under this category are distinguished by their policy for updating the transmission frame size. This paper analyses the frame size update policy of DFSA strategies to survey and classify the main state-of-the-art of DFSA protocols according to their policy. Consequently, this paper proposes a novel policy to lower the time to read one sensor data packet compared to existing strategies. Next, the novel anti-collision protocol Fuzzy Frame Slotted Aloha (FFSA) is presented, which applies this novel DFSA policy. The results of our simulation confirm that FFSA significantly decreases the sensor tag read time for a wide range of tag populations when compared to earlier DFSA protocols thanks to the proposed frame size update policy.
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9

Hussein, Isam, Basil Jasim, and Ramzy Ali. "A Modified BA Anti-Collision Protocol for Coping with Capture Effect and Interference in RFID Systems." Future Internet 10, no. 10 (October 1, 2018): 96. http://dx.doi.org/10.3390/fi10100096.

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Анотація:
Radio frequency identification (RFID) technology has widely been used in the last few years. Its applications focus on auto identification, tracking, and data capturing issues. However, RFID suffers from the main problem of tags collision when multiple tags simultaneously respond to the reader request. Many protocols were proposed to solve the collision problems with good identification efficiency and an acceptable time delay, such as the blocking anti-collision protocol (BA). Nevertheless, most of these protocols assumed that the RFID reader could decode the tag’s signal only when there was one tag responding to the reader request once each time. Hence, they ignored the phenomenon of the capture effect, which results in identifying the tag with the stronger signal as the multiple tags simultaneously respond. As a result, many tags will not be identified under the capture effect. Therefore, the purpose of this paper is to take the capture effect phenomenon into consideration in order to modify the blocking BA protocol to ensure a full read rate, i.e., identifying all the tags in the frame without losing any tag. Moreover, the modifications include distinguishing between collision and interference responses (for the period of staying tags) in the noisy environments, for the purpose of enhancing the efficiency of the identification. Finally, the simulation and analytical results show that our modifications and MBA protocol outperform the previous protocols in the same field, such as generalized query tree protocols (GQT1 and GQT2), general binary tree (GBT), and tweaked binary tree (TBT).
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10

XU, Yuan-yuan, Jun-fang ZENG, Lin CHEN, and Yu LIU. "Research and improvement of EPC Gen2 anti-collision protocol." Journal of Computer Applications 28, no. 12 (February 26, 2009): 3271–73. http://dx.doi.org/10.3724/sp.j.1087.2008.03271.

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11

Jia, Xiaolin, Quanyuan Feng, and Chengzhen Ma. "An Efficient Anti-Collision Protocol for RFID Tag Identification." IEEE Communications Letters 14, no. 11 (November 2010): 1014–16. http://dx.doi.org/10.1109/lcomm.2010.091710.100793.

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12

Zhao, Wen Ge. "RFID Anti-Collision Method Based on Particle Swarm Optimization and Support Vector Machine." Applied Mechanics and Materials 170-173 (May 2012): 3398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3398.

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Анотація:
RFID anti-collision method based on particle swarm optimization and support vector machine is presented in the paper. Support vector machine is a new detection technology,which is applied to RFID anti-collision detection. Particle swarm optimization algorithm is applied to choose the appropriate parameters of support vector machine. Particle swarm optimization algorithm can make the particle move toward the optimal resolution based on the history best experiences of each particle and global best position in swarm.The proposed RFID anti-collision structure is mainly composed of protocol processing module, interface module, RFID anti-collision method and serial-parallel conversion.The testing results show that RFID collision detection accuracy of particle swarm optimization and support vector machine than that of traditional support vector machine and BP neural network.
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13

Felemban, Emad. "Performance Analysis of RFID Framed Slotted Aloha Anti-Collision Protocol." Journal of Computer and Communications 02, no. 01 (2014): 13–18. http://dx.doi.org/10.4236/jcc.2014.21003.

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14

Lee, Seong Ro, Yeonwoo Lee, and Yang-Ick Joo. "An RFID Tag Anti-Collision Protocol for Port Logistics Systems." Journal of Korea Information and Communications Society 38C, no. 2 (February 28, 2013): 202–7. http://dx.doi.org/10.7840/kics.2013.38c.2.202.

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15

Liu, Xiaohui, Zhihong Qian, Yanhang Zhao, and Yuqi Guo. "An adaptive tag anti-collision protocol in RFID wireless systems." China Communications 11, no. 7 (July 2014): 117–27. http://dx.doi.org/10.1109/cc.2014.6895391.

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16

Gandino, Filippo, Renato Ferrero, Bartolomeo Montrucchio, and Maurizio Rebaudengo. "Probabilistic DCS: An RFID reader-to-reader anti-collision protocol." Journal of Network and Computer Applications 34, no. 3 (May 2011): 821–32. http://dx.doi.org/10.1016/j.jnca.2010.04.007.

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17

Jayadi, Riyanto, Yuan-Cheng Lai, and Chih-Chung Lin. "Efficient time-oriented anti-collision protocol for RFID tag identification." Computer Communications 112 (November 2017): 141–53. http://dx.doi.org/10.1016/j.comcom.2017.08.016.

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18

Cho, Yang-Hyun, and Joong-Gak Kook. "Energy Effective Tag Anti-collision Protocol for Mobile RFID System." Journal of the Korea Society of Computer and Information 17, no. 2 (February 29, 2012): 207–14. http://dx.doi.org/10.9708/jksci.2012.17.2.207.

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19

Jia, XiaoLin, and QuanYuan Feng. "An improved anti-collision protocol for radio frequency identification tag." International Journal of Communication Systems 28, no. 3 (August 10, 2013): 401–13. http://dx.doi.org/10.1002/dac.2629.

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20

Liang, Xinyu, and Yajun Guo. "A Probability-Based Anti-Collision Protocol for RFID Tag Identification." Wireless Personal Communications 107, no. 1 (March 11, 2019): 57–79. http://dx.doi.org/10.1007/s11277-019-06240-1.

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21

Qian, Haizhong. "An Improved Approach for Reader Anti-Collision in Industrial Internet of Things." Security and Communication Networks 2022 (May 27, 2022): 1–6. http://dx.doi.org/10.1155/2022/1268577.

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Анотація:
Radio-frequency identification (RFID) technology has been used in numerous applications, e.g., supply chain management and inventory control. This paper focuses on the practically important problem of reader-to-reader collision in large-scale RFID systems. There are many technical challenges during the existing work such as high computational complexity and low identification efficiency. To tackle the above challenges, this paper proposes a novel reader anti-collision protocol optimized by minimal reverse weight (NRA-MRW), which includes two phases. Extensive simulation results show that our proposed NRA-MRW can improve the identification efficiency and system throughput over the existing protocols by 25.2% and 12.3%, respectively.
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22

Mafamane, Rachid, Mourad Ouadou, Hajar Sahbani, Nisrine Ibadah, and Khalid Minaoui. "DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things." Computers 11, no. 7 (June 30, 2022): 107. http://dx.doi.org/10.3390/computers11070107.

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Анотація:
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation.
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23

Dong, Guozhong, Sichang Xuan, Feng Qin, Haowen Tan, and Jiuru Wang. "An Improved Binary Search Anti-collision Protocol for RFID Tag Identification." Computers, Materials & Continua 65, no. 2 (2020): 1855–68. http://dx.doi.org/10.32604/cmc.2020.09919.

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24

Ahn, Ji Hyoung, Se Houn Lee, and Tae-Jin Lee. "Anti-Collision Protocol for Coexistence of RFID and NFC P2P Communications." IEEE Communications Letters 20, no. 11 (November 2016): 2185–88. http://dx.doi.org/10.1109/lcomm.2016.2602865.

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25

Jia, Xiaolin, Miodrag Bolic, Yuhao Feng, and Yajun Gu. "An Efficient Dynamic Anti-Collision Protocol for Mobile RFID Tags Identification." IEEE Communications Letters 23, no. 4 (April 2019): 620–23. http://dx.doi.org/10.1109/lcomm.2019.2895819.

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26

Kim, Sung, and Poogyeon Park. "An Efficient Tree-Based Tag Anti-Collision Protocol for RFID Systems." IEEE Communications Letters 11, no. 5 (May 2007): 449–51. http://dx.doi.org/10.1109/lcomm.2007.070027.

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27

Joo, Yang-Ick, Dong-Hoan Seo, and Jae-Wan Kim. "An Efficient Anti-collision Protocol for Fast Identification of RFID Tags." Wireless Personal Communications 77, no. 1 (November 27, 2013): 767–75. http://dx.doi.org/10.1007/s11277-013-1535-8.

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28

Golsorkhtabaramiri, Mehdi, Mehdi Hosseinzadeh, Midia Reshadi, and Amir Masoud Rahmani. "A Reader Anti-collision Protocol for RFID-Enhanced Wireless Sensor Networks." Wireless Personal Communications 81, no. 2 (November 6, 2014): 893–905. http://dx.doi.org/10.1007/s11277-014-2163-7.

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29

Zhang, Lijuan, Wei Xiang, and Xiaohu Tang. "An Adaptive Anti-Collision Protocol for Large-Scale RFID Tag Identification." IEEE Wireless Communications Letters 3, no. 6 (December 2014): 601–4. http://dx.doi.org/10.1109/lwc.2014.2359461.

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30

Li, Gang, Haoyang Sun, Zhenbing Li, Peiqi Wu, Daniele Inserra, Jian Su, Xiaochuan Fang, and Guangjun Wen. "A Dynamic Multi-ary Query Tree Protocol for Passive RFID Anti-collision." Computers, Materials & Continua 72, no. 3 (2022): 4931–44. http://dx.doi.org/10.32604/cmc.2022.026654.

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31

Abd Allah, Mohamed Mostafa. "Modified Anti-Binary Collision Protocol (MABS) of Radio Frequency Identification (RFID) System." Journal of Wireless Networking and Communications 1, no. 1 (August 31, 2012): 16–22. http://dx.doi.org/10.5923/j.jwnc.20110101.03.

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32

KANG, You Sung, Dong-Jo PARK, Daniel W. ENGELS, and Dooho CHOI. "KeyQ: A Dynamic Key Establishment Method Using an RFID Anti-Collision Protocol." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 12 (2014): 2662–66. http://dx.doi.org/10.1587/transfun.e97.a.2662.

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33

Le, Nam-Tuan, Sun-Woong Choi, and Yeong-Min Jang. "Enhanced Anti-Collision Protocol for Identification Systems: Binary Slotted Query Tree Algorithm." Journal of Korean Institute of Communications and Information Sciences 36, no. 9B (September 30, 2011): 1092–97. http://dx.doi.org/10.7840/kics.2011.36b.9.1092.

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34

Arjona, L., H. Landaluce, A. Perallos, and E. Onieva. "Fast fuzzy anti‐collision protocol for the RFID standard EPC Gen‐2." Electronics Letters 52, no. 8 (April 2016): 663–65. http://dx.doi.org/10.1049/el.2015.3258.

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35

Jia, Xiaolin, Quanyuan Feng, and Lishan Yu. "Stability Analysis of an Efficient Anti-Collision Protocol for RFID Tag Identification." IEEE Transactions on Communications 60, no. 8 (August 2012): 2285–94. http://dx.doi.org/10.1109/tcomm.2012.051512.110448.

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36

Arjona, Laura, Hugo Landaluce, Asier Perallos, and Enrique Onieva. "Timing-Aware RFID Anti-Collision Protocol to Increase the Tag Identification Rate." IEEE Access 6 (2018): 33529–41. http://dx.doi.org/10.1109/access.2018.2849223.

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37

Leng, Xuefei, Yuan Hung Lien, Keith Mayes, and Konstantinos Markantonakis. "An RFID grouping proof protocol exploiting anti-collision algorithm for subgroup dividing." International Journal of Security and Networks 5, no. 2/3 (2010): 79. http://dx.doi.org/10.1504/ijsn.2010.032206.

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38

Fu, Yu, Xue Wang, Enshu Wang, and Zhihong Qian. "A bit arbitration tree anti-collision protocol in radio frequency identification systems." International Journal of Distributed Sensor Networks 13, no. 11 (November 2017): 155014771774157. http://dx.doi.org/10.1177/1550147717741571.

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39

Qu, Zhijian, Xubing Sun, Xinqiang Chen, and Shengao Yuan. "A novel RFID multi-tag anti-collision protocol for dynamic vehicle identification." PLOS ONE 14, no. 7 (July 5, 2019): e0219344. http://dx.doi.org/10.1371/journal.pone.0219344.

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40

Li, Zhonghua, Guannan He, and Shanjin Wang. "NFRA-AIC: A RFID Reader Anti-Collision Protocol With Adaptive Interrogation Capacity." IEEE Access 7 (2019): 86493–509. http://dx.doi.org/10.1109/access.2019.2924316.

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41

Bueno-Delgado, M. Victoria, Renato Ferrero, Filippo Gandino, Pablo Pavon-Marino, and Maurizio Rebaudengo. "A Geometric Distribution Reader Anti-Collision Protocol for RFID Dense Reader Environments." IEEE Transactions on Automation Science and Engineering 10, no. 2 (April 2013): 296–306. http://dx.doi.org/10.1109/tase.2012.2218101.

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42

Guo, Peng, Hai Yan Zhang, Zhen Yong Liu, and Yong Wei Li. "A Laboratory Equipment Management System Design Based on RFID." Applied Mechanics and Materials 602-605 (August 2014): 2526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2526.

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Анотація:
For the needs of laboratory equipment management, combined with development of RFID, a laboratory equipment management system is designed based on RFID. System design is briefly described. UHF RFID reader design is described in detail. The anti-collision algorithm of ISO 18000-6C protocol is partially improved, so that the reader is able to make real-time adaptations of the anti-collision algorithm using IAP according to the quantity of laboratory equipments, thus raising the efficiency of algorithm recognition. Experimental results prove that the system design has reliability, stability and certain practical value.
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43

SUNG, Jongwoo, Daeyoung KIM, Taehong KIM, and Jinhyuk CHOI. "Heuristic Query Tree Protocol: Use of Known Tags for RFID Tag Anti-Collision." IEICE Transactions on Communications E95-B, no. 2 (2012): 603–6. http://dx.doi.org/10.1587/transcom.e95.b.603.

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44

Wong, C. P., and Quanyuan Feng. "Grouping based bit-slot ALOHA protocol for tag anti-collision in RFID systems." IEEE Communications Letters 11, no. 12 (December 2007): 946–48. http://dx.doi.org/10.1109/lcomm.2007.071252.

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45

Jeong Geun Kim. "A Divide-and-Conquer Technique for Throughput Enhancement of RFID Anti-collision Protocol." IEEE Communications Letters 12, no. 6 (June 2008): 474–76. http://dx.doi.org/10.1109/lcomm.2008.080277.

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46

Assarian, Ali, Ahmad Khademzadeh, Mehdi HosseinZadeh, and Saeed Setayeshi. "A beacon analysis-based RFID reader anti-collision protocol for dense reader environments." Computer Communications 128 (September 2018): 18–34. http://dx.doi.org/10.1016/j.comcom.2018.06.006.

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47

Kim, Soo-Cheol, Jung-Sik Cho, and Sung-Kwon Kim. "Performance improvement of hybrid tag anti-collision protocol for radio frequency identification systems." International Journal of Communication Systems 26, no. 6 (June 25, 2012): 705–19. http://dx.doi.org/10.1002/dac.2389.

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48

Harikrishnan, Harshini, Aarjish Sarkar, Shams Al Ajrawi, Albena Mihovska, and Christopher Paolini. "Medium Access Control Protocols for Brain Computer Interface Applications." International Journal of Interdisciplinary Telecommunications and Networking 13, no. 2 (April 2021): 24–41. http://dx.doi.org/10.4018/ijitn.2021040103.

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Анотація:
Noteworthy advancements have occurred in the field of brain computer interfaces (BCI) in the last decade. The transmission of the collected signals to the corresponding organ through a wireless link is still in a nascent stage. This paper investigates the feasibility of having multiple UHF-RFID transmitters inside the skull which communicate to the receiver outside. The purpose of this paper is to design a wireless communication channel and enhance the communication using a MAC protocol. The existing MAC protocols were analyzed to be used in a BCI application. Though the combination of existing protocols seems like a possibility to use in a BCI system, the complexity of such a protocol is high. This drawback is overcome by proposing a novel MAC protocol. The performance of the proposed MAC algorithm is related with the widely used Q algorithm for anti-collision in the RFID tags. Parameters like delay and collisions are analyzed in detail which are essential to build a consistent, energy efficient, and a low power BCI system.
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49

Joo, Jin-Hoon, and Sang-Hwa Chung. "Implementation of an Efficient Slotted CSMA/CA Anti-collision Protocol for Active RFID System." Journal of Korean Institute of Communications and Information Sciences 37A, no. 12 (December 28, 2012): 1013–22. http://dx.doi.org/10.7840/kics.2012.37a.12.1013.

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50

Xu, Shouzhi, Bo Xu, Pengfei Guo, and Qing Wang. "A Fast and Reliable VANET Routing Protocol for Cooperative Anti-collision Warning on Highway." Intelligent Automation & Soft Computing 19, no. 3 (August 2013): 275–84. http://dx.doi.org/10.1080/10798587.2013.824135.

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