TY - GEN
T1 - Revisiting reading rate with mobility
T2 - 13th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2017
AU - Lin, Qiongzheng
AU - Yang, Lei
AU - Jia, Huanyu
AU - Duan, Chunhui
AU - Liu, Yunhao
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
PY - 2017/11/28
Y1 - 2017/11/28
N2 - Radio-frequency identification (RFID) systems, as major enablers of automatic identification, are currently supplemented with various interesting sensing functions, e.g., motion tracking. All these sensing applications forcedly require much higher reading rate (i.e., sampling rate) such that any fast movement of tagged objects can be accurately captured in a timely manner through tag readings. However, COTS RFID systems suffer from an extremely low individual reading rate when multiple tags are present, due to their intense channel contention in the link layer. In this work, we present a holistic system, called Tagwatch, a rate-adaptive reading system for COTS RFID devices. This work revisits the reading rate from a distinctive perspective: mobility. We observe that the reading demands of mobile tags are considerably more urgent than those of stationary tags because the states of the latter nearly remain unchanged; meanwhile, only a few tags (e.g., < 20%) are actually in motion despite the existence of a massive amount of tags in practice. Thus, Tagwatch adaptively improves the reading rates for mobile tags by cutting down the readings of stationary tags. Our main contribution is a two-phase reading design, wherein the mobile tags are discriminated in the Phase I and exclusively read in the Phase II. We built a prototype of Tagwatch with COTS RFID readers and tags. Results from our microbenchmark analysis demonstrate that the new design outperforms the reading rate by 3.2x when 5% of tags are moving.
AB - Radio-frequency identification (RFID) systems, as major enablers of automatic identification, are currently supplemented with various interesting sensing functions, e.g., motion tracking. All these sensing applications forcedly require much higher reading rate (i.e., sampling rate) such that any fast movement of tagged objects can be accurately captured in a timely manner through tag readings. However, COTS RFID systems suffer from an extremely low individual reading rate when multiple tags are present, due to their intense channel contention in the link layer. In this work, we present a holistic system, called Tagwatch, a rate-adaptive reading system for COTS RFID devices. This work revisits the reading rate from a distinctive perspective: mobility. We observe that the reading demands of mobile tags are considerably more urgent than those of stationary tags because the states of the latter nearly remain unchanged; meanwhile, only a few tags (e.g., < 20%) are actually in motion despite the existence of a massive amount of tags in practice. Thus, Tagwatch adaptively improves the reading rates for mobile tags by cutting down the readings of stationary tags. Our main contribution is a two-phase reading design, wherein the mobile tags are discriminated in the Phase I and exclusively read in the Phase II. We built a prototype of Tagwatch with COTS RFID readers and tags. Results from our microbenchmark analysis demonstrate that the new design outperforms the reading rate by 3.2x when 5% of tags are moving.
KW - Epcglobal gen2
KW - RFID
KW - Rate-adaptive reading
KW - Tagwatch
KW - Two-phase protocol
UR - http://www.scopus.com/inward/record.url?scp=85040226961&partnerID=8YFLogxK
U2 - 10.1145/3143361.3143387
DO - 10.1145/3143361.3143387
M3 - Conference contribution
AN - SCOPUS:85040226961
T3 - CoNEXT 2017 - Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies
SP - 199
EP - 211
BT - CoNEXT 2017 - Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies
PB - Association for Computing Machinery, Inc
Y2 - 12 December 2017 through 15 December 2017
ER -