Abstract | ||
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Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer-to-peer communications among nearby tags, the emerging networked tags make a fundamental enhancement to today's RFID systems. This new capability supports a series of system-level functions in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper makes the first attempt to design a new communication model that is specifically tailored to efficient implementation of system-level functions in networked tag systems, in terms of energy cost and execution time. Instead of exploiting complex mechanisms for collision detection and resolution, we propose a collision-resistant communication model (CCM) that embraces the collision in tag communications and utilizes it to merge the data from different sources in a benign way. Two fundamental applications: RFID estimation and missing-tag detection, are presented to illustrate how CCM assists efficient system-level operations in networked tag systems. Simulation results show that the system-level applications through CCM are able to reduce the energy cost and execution time by one order of magnitude, compared with the ID-collection based solution. |
Year | DOI | Venue |
---|---|---|
2019 | 10.1109/ICDCS.2019.00071 | 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) |
Keywords | Field | DocType |
Networked tags,Communication model,RFID | Collision detection,Wireless,Computer science,Computer network,Models of communication,Collision,Execution time,Merge (version control),Wireless sensor network,Radio-frequency identification,Distributed computing | Conference |
ISSN | ISBN | Citations |
1063-6927 | 978-1-7281-2520-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jia Liu | 1 | 72 | 21.41 |
Youlin Zhang | 2 | 10 | 5.26 |
Shiping Chen | 3 | 190 | 25.84 |
Min Chen | 4 | 115 | 11.07 |
Lijun Chen | 5 | 181 | 20.57 |