scholarly journals Enhanced TDMA Based Anti-Collision Algorithm with a Dynamic Frame Size Adjustment Strategy for Mobile RFID Readers

Sensors ◽  
2009 ◽  
Vol 9 (2) ◽  
pp. 845-858 ◽  
Author(s):  
Kwang Cheol Shin ◽  
Seung Bo Park ◽  
Geun Sik Jo
2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pauline Ong

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.


2013 ◽  
Vol 4 (3-4) ◽  
pp. 163-180
Author(s):  
Chin-Ling Chen ◽  
Yong-Yuan Deng ◽  
Hsien-Wen Tseng

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1711 ◽  
Author(s):  
Qian Ma ◽  
Xia Li ◽  
Guanyu Li ◽  
Bo Ning ◽  
Mei Bai ◽  
...  

Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been proposed. However, tracking mobile RFID readers in real-time has been a daunting task, especially for achieving high localization accuracy. In this paper, we propose a new Mobile RFID (M-RFID)-based Localization approach for Indoor Human Tracking, named MRLIHT. Based on the M-RFID model where RFID readers are equipped on the moving objects (human beings) and RFID tags are fixed deployed in the monitoring area, MRLIHT implements the real-time indoor location tracking effectively and economically. First, based on the readings of multiple tags detected by an RFID reader simultaneously, MRLIHT generates the response regions of tags to the reader. Next, MRLIHT determines the potential location region of the reader where two algorithms are devised. Finally, MRLIHT estimates the location of the reader by dividing the potential location region of the reader into finer-grained grids. The experimental results demonstrate that the proposed MRLIHT performs well in both accuracy and scalability.


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