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  4. Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable Channels - 2017
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Category: MTech Java Projects
By MTech Projects
MTech Projects
14.Mar
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Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable Channels - 2017

PROJECT TITLE :

Efficient Unknown Tag Detection in Large-Scale RFID Systems With Unreliable Channels - 2017

ABSTRACT:

One among the foremost important applications of radio frequency identification (RFID) technology is to detect unknown tags brought by new tagged items, misplacement, or counterfeit tags. While unknown tag identification is ready to pinpoint all the unknown tags, probabilistic unknown tag detection is most well-liked in massive-scale RFID systems that require to be frequently checked up, e.g., real-time inventory monitoring. Nevertheless, most of the previous solutions are neither efficient nor reliable. The communication efficiency of former schemes is not well optimized because of the transmission of unhelpful information. Furthermore, they do not take into account characteristics of unreliable wireless channels in RFID systems. During this paper, we propose a quick and reliable method for probabilistic unknown tag detection, white paper (WP) protocol. The key novelty of WP is to make a new information structure of composite message that consists of all the informative information from several independent detection synopses; thus it excludes useless information from communication. Furthermore, we tend to use packet loss differentiation and adaptive channel hopping techniques to combat unreliable backscatter channels. We have a tendency to implement a prototype system using USRP software-outlined radio and WISP tags to indicate the feasibility of this style. We have a tendency to also conduct intensive simulations and comparisons to indicate that WP outperforms previous ways. Compared with the state-of-the-art protocols, WP achieves additional than a pair of× performance gain in terms of time-potency when all the channels are assumed freed from errors and the quantity of tags is ten thousand, and achieves up to 12× success chance gain when the burstiness is more than eightyp.c.

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