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Abstract-Estimating the RFID cardinality with accuracy guarantee is an important task in large-scale RFID systems. This paper proposes a fast RFID cardinality estimation scheme. The proposed Zero-One Estimator (ZOE) protocol rapidly converges to optimal parameter settings and achieves high estimation efficiency. ZOE significantly improves the cardinality estimation efficiency, achieving 3x performance gain compared with existing protocols. Meanwhile, ZOE guarantees arbitrary accuracy requirement without imposing computation and memory overhead at RFID tags. Due to the simplicity and robustness, the ZOE protocol provides reliable cardinality estimation even over noisy channel. We implement a prototype system using the USRP software defined radio and Intel WISP RFID tags. We also evaluate the performance of ZOE with extensive simulations. The evaluation of ZOE shows encouraging results in terms of estimation accuracy, time efficiency, as well as robustness.

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Abstract-Fast searching a particular subset in a large number of products attached with RFID tags is of practical importance for a variety of applications but not yet thoroughly investigated. Since the cardinality of the products can be extremely large, collecting the tag information directly from each of those tags could be highly inefficient. To address the tag searching efficiency in large-scale RFID systems, this paper proposes several algorithms to meet the stringent delay requirement in developing fast tag searching protocols. We formally formulate the tag searching problem in large-scale RFID systems. We propose utilizing compact approximators to efficiently aggregate a large volume of RFID tag information and exchange such information with a two-phase approximation protocol. By estimating the intersection of two compact approximators, the proposed twophase compact approximator based tag searching protocol significantly reduces the searching time compared with all possible solutions we can directly borrow from existing studies. We further introduce a scalable cardinality range estimation method which provides inexpensive input for our tag searching protocol. We conduct comprehensive simulations to validate our design. The results demonstrate that the proposed tag searching protocol is highly efficient in terms of both time-efficiency and transmission overhead, leading to good applicability and scalability for largescale RFID systems.

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Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.

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 Oðlog log nÞ 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.