Approximate Range Emptiness in Constant Time for IoT Data Streams over Sliding Windows

Wang, Xiujun; Liu, Zhi; Yang, Yangzhao; Shao, Xun; Gu, Yu; Ishihara, Susumu

doi:10.1109/icccn.2019.8847153

Cited by 1 publication

(1 citation statement)

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“…Then, it is easy to see that at most 192 × n log 2 (N ) comparisons are needed for finding the filter-string that is shared by the largest number of tags of P in Step-11. 7 Obviously, at most n filter-strings are needed for picking P from S. Therefore, the second stage of TagSP needs at most 192 × n log 2 (N ) × n = O(n 2 log 2 (N )) comparisons.…”

Section: Solving the Tag-selection Problem By Using Multiple Selecmentioning

confidence: 99%

EPC-Based Efficient Tag Selection in RFID Systems

Zhu

Wang

Yang³

et al. 2020

IEEE Access

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Tag-selection problem, which selects a set of wanted tags from a tag population, is vital for boosting efficiencies of the real-time applications in RFID systems. However, prior arts for the problem can not be applied to RFID systems directly, given that they either require additional computing functions implemented in tag's chips or require a time-consuming pre-process with a large communication cost. This paper studies the tag-selection problem and propose an efficient Electronic Product Code (EPC)-based tag selection method with theoretical analysis. In particular, firstly, we prove a nontrivial lower bound of communication overhead for a protocol which is capable of solving the tag-selection problem. Secondly, we propose an efficient protocol, denoted by TagSP, which only uses the ''select'' command (a mandatory command that all RFID tags shall support) and EPC. The proposed TagSP can be applied directly into offthe-shelf RFID systems with a communication overhead close to the lower bound. Extensive simulations are conducted and the simulation results show TagSP's superiority compared with existing protocols. INDEX TERMS RFID systems, tag selection, lower bound of communication overhead, EPC.

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Section: Solving the Tag-selection Problem By Using Multiple Selecmentioning

confidence: 99%