Information-Gradient Based Decentralized Data Management over RFID Tag Clouds

Bapat, Tejas A.; Candan, K. Selçuk; Cherukuri, V. Snehith; Sundaram, Hari

doi:10.1109/mdm.2009.18

Cited by 2 publications

(1 citation statement)

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“…The rationale of the proposed approach is similar to those methods based on the F-RFID model and only considers the case of a single reader. The works [19,20] use the memory of passive tags to encode and store the information of moving objects. Then the proposed algorithms estimate the locations of moving objects through creating an information-gradience.…”

Section: Related Workmentioning

confidence: 99%

MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking

et al. 2020

Sensors

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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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Section: Related Workmentioning

confidence: 99%