DocumentCode :
3308238
Title :
Using neighbor and tag estimations for redundant reader eliminations in RFID networks
Author :
Ali, Kashif ; Hassanein, Hossam S. ; Alsalih, Waleed
Author_Institution :
Sch. of Comput., Queen´´s Univ., Kingston, ON, Canada
fYear :
2011
fDate :
28-31 March 2011
Firstpage :
832
Lastpage :
837
Abstract :
Deployments of Radio Frequency Identification (RFID) networks are anticipated to be dense and ad-hoc. These deployments usually involve redundant readers having overlapping interrogation zones and, hence, causing immense reader collisions. Elimination of redundant readers, from the network, is of utmost importance as otherwise they affect the lifetime and the operational capacity of the overall RFID network. In this paper, we propose a light-weight greedy algorithm that detects and eliminates redundant readers from the network. Our algorithm uses the ratio of tag counts to the number of neighboring readers of each reader to estimate the likelihood for that reader to be redundant. The proposed algorithm is highly scalable and poses a minimal communication overhead as compared with existing schemes in the literature.
Keywords :
ad hoc networks; greedy algorithms; maximum likelihood estimation; radiofrequency identification; telecommunication network reliability; RFID network; ad-hoc network; immense reader collision; light-weight greedy algorithm; likelihood estimation; neighbor estimation; network lifetime; overlapping interrogation zone; radio frequency identification network; redundant reader elimination; tag estimation; Ad hoc networks; Complexity theory; Estimation; Greedy algorithms; Low earth orbit satellites; Radiofrequency identification; Redundancy; RFID tag estimation; greedy algorithm; redundant reader elimination;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications and Networking Conference (WCNC), 2011 IEEE
Conference_Location :
Cancun, Quintana Roo
ISSN :
1525-3511
Print_ISBN :
978-1-61284-255-4
Type :
conf
DOI :
10.1109/WCNC.2011.5779240
Filename :
5779240
Link To Document :
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