PET: Probabilistic estimating tree for large-scale RFID estimation

Yuanqing Zheng, Mo Li, Chen Qian

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

49 Citations (Scopus)

Abstract

Estimating the number of RFID tags in the region of interest is an important task in many RFID applications. In this paper we propose a novel approach for efficiently estimating the approximate number of RFID tags. Compared with existing approaches, the proposed Probabilistic Estimating Tree (PET) protocol achieves O(loglogn) estimation efficiency, which remarkably reduces the estimation time while meeting the accuracy requirement. PET also largely reduces the computation and memory overhead at RFID tags. As a result, we are able to apply PET with passive RFID tags and provide scalable and inexpensive solutions for large-scale RFID systems. We validate the efficacy and effectiveness of PET through theoretical analysis as well as extensive simulations. Our results suggest that PET outperforms existing approaches in terms of estimation accuracy, efficiency, and overhead.
Original languageEnglish
Title of host publicationProceedings - 31st International Conference on Distributed Computing Systems, ICDCS 2011
Pages37-46
Number of pages10
DOIs
Publication statusPublished - 25 Aug 2011
Externally publishedYes
Event31st International Conference on Distributed Computing Systems, ICDCS 2011 - Minneapolis, MN, United States
Duration: 20 Jun 201124 Jul 2011

Conference

Conference31st International Conference on Distributed Computing Systems, ICDCS 2011
Country/TerritoryUnited States
CityMinneapolis, MN
Period20/06/1124/07/11

Keywords

  • Probabilistic algorithm
  • Probabilistic estimating tree
  • RFID counting system

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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