status of multiple tags simultaneously,and greatly improve II.RELATED WORKS the time-efficiency.Specifically,we propose a two-phase tag Collision Recovery Many works focus on how to extract monitoring scheme including the tag inventory and continuous tag cardinality [13]or recover the tag signal [8,9]from polling.In the tag inventory phase,the RFID reader constructs the collision signals based on the specialized instruments like a physical fingerprint for each tag individually via traditional USRP.Since Buettner et al.propose a Software Defined Ra- tag inventory.In the continuous polling phase,the RFID reader dio based UHF-RFID reader [14],several researchers further continuously issues multiple query cycles to interrogate the leverage this platform to deal with the collision problems tags.For each polling cycle,the RFID reader measures a new [7-9,15].Wang et al.[7]implement a new scheme which distribution of physical-layer features via both the singleton enables rateless code transmitting.[8,9]use the time-domain and collision slots.By matching the updated distributions to separation to recovery the data from the collision signals.Hou the original distributions,our solution is able to efficiently et al.[13]present a physical-layer cardinality estimator from detect the moving tags.The above two phases are executed the collision signals for large scale RFID system. alternately,and the time overhead of the tag inventory phase Physical Layer Identification Previous studies [16,17]focus can be amortized by the following multiple polling cycles, on physical-layer identification by leveraging the hardware im- such that the overall time-efficiency is achieved. perfection in tag manufacturing.Davide et al.[16]distinguish There are three key technical challenges.The first challenge from different tags based on the frequency difference orE is to achieve real-time time efficiency in large scale RFID caused by manufacturing imperfection of tags.Han et al.[18] systems.In a large scale RFID system,it is rather difficult to leverage the internal similarity among pulses of tags'RN16 continuously update the monitoring results within limited time preamble signals as the fingerprint for distinguishing.Zheng intervals.To address this challenge,we propose a two-phase et al.[10]employ a method to detect the missing tags based monitoring scheme including a normal tag inventory phase on physical-layer signals. and multiple fast tag polling phases,we significantly improve Different from previous work,in this paper,we focus on the time efficiency in extracting the physical-layer features via how to design a real-time tag monitoring scheme to efficiently decoding the tag collisions.The second challenge is to detect detect the motion status of all tags,so as to further support the motion status of all tags via the physical-layer features. tracking the movement of all tags.We aim to improve both To address this challenge,we exploit the relationship between the time efficiency in tag inventory and the accuracy in detect the physical-layer features and the motion status of tags.We the motion status of all tags. find that the phase value from the tag's response changes even if the specified tag is moved with a small distance, III.SYSTEM OVERVIEW while the backscatter link frequency of the tag's response A.Design Goals has high degree of distinction among different tags.We thus In this paper,we propose a real-time approach to detect leverage these physical-layer features to detect the motion the motion status of all tags in the monitoring area,so as to status of specified tags.The third challenge is to extract the further support tracking the movement of all tags.Because above physical-layer features from the collisions of multiple the tags may change their motion status any time,we need tag responses.To address this challenge,we recover each tag to continuously update the motion status within a limited response according to the geometrical characteristic of the time interval.Therefore,our objective in designing a moving collision signals in I-Q plane,and extract the phase profile tag detection scheme is to improve both the time efficiency of each tag response.Further,we refer to special patterns to in tag inventory and the accuracy in detecting the motion identify the starting and ending parts of recovered RF signals status of all tags.1)The average time for each cycle of tag based on cross-correlation,and extract the backscatter link inventory should be sufficiently reduced to achieve the real- frequency from the signal length of each tag. time requirement for large scale RFID systems.2)There are We make three contributions in this paper.First,to the two kinds of errors in the problem:a)False positive errors:the best of our knowledge,we are the first to propose a moving stationary tags are identified as moving tags.b)False negative tag detection scheme for tag monitoring by leveraging the errors:the moving tags are identified as stationary tags.Both physical-layer features,which is a fundamental premise for of the two errors should be effectively reduced in detecting tracking the movement of RFID tags in large-scale RFID the motion status. systems.Second,our solution is able to accurately detect the motion status of all tags,by referring to the physical-layer B.System Framework features,including the phase profile and backscatter link fre- In order to effectively detect the motion status of all tags, quency.Moreover,we extract these physical-layer features of we exploit the relationship between the physical-layer features multiple tags from collision slots,which significantly improves and the motion status of tags.The following two physical- the time efficiency.Third,we implemented a prototype system layer features are investigated:1)Phase profile:it is the phase and evaluated its performance in realistic settings.Experiment value of an RF signal.The phase value from the tag's response result shows that our solution can accurately detect the moving changes even if the tag is moved with a small distance.2) tags while reducing 80%of inventory time compared with state Backscatter link frequency (BLF):it is the frequency of the of arts solutions. tag-to-reader link,which determines the tag's data rate instatus of multiple tags simultaneously, and greatly improve the time-efficiency. Specifically, we propose a two-phase tag monitoring scheme including the tag inventory and continuous polling. In the tag inventory phase, the RFID reader constructs a physical fingerprint for each tag individually via traditional tag inventory. In the continuous polling phase, the RFID reader continuously issues multiple query cycles to interrogate the tags. For each polling cycle, the RFID reader measures a new distribution of physical-layer features via both the singleton and collision slots. By matching the updated distributions to the original distributions, our solution is able to efficiently detect the moving tags. The above two phases are executed alternately, and the time overhead of the tag inventory phase can be amortized by the following multiple polling cycles, such that the overall time-efficiency is achieved. There are three key technical challenges. The first challenge is to achieve real-time time efficiency in large scale RFID systems. In a large scale RFID system, it is rather difficult to continuously update the monitoring results within limited time intervals. To address this challenge, we propose a two-phase monitoring scheme including a normal tag inventory phase and multiple fast tag polling phases, we significantly improve the time efficiency in extracting the physical-layer features via decoding the tag collisions. The second challenge is to detect the motion status of all tags via the physical-layer features. To address this challenge, we exploit the relationship between the physical-layer features and the motion status of tags. We find that the phase value from the tag’s response changes even if the specified tag is moved with a small distance, while the backscatter link frequency of the tag’s response has high degree of distinction among different tags. We thus leverage these physical-layer features to detect the motion status of specified tags. The third challenge is to extract the above physical-layer features from the collisions of multiple tag responses. To address this challenge, we recover each tag response according to the geometrical characteristic of the collision signals in I-Q plane, and extract the phase profile of each tag response. Further, we refer to special patterns to identify the starting and ending parts of recovered RF signals based on cross-correlation, and extract the backscatter link frequency from the signal length of each tag. We make three contributions in this paper. First, to the best of our knowledge, we are the first to propose a moving tag detection scheme for tag monitoring by leveraging the physical-layer features, which is a fundamental premise for tracking the movement of RFID tags in large-scale RFID systems. Second, our solution is able to accurately detect the motion status of all tags, by referring to the physical-layer features, including the phase profile and backscatter link fre￾quency. Moreover, we extract these physical-layer features of multiple tags from collision slots, which significantly improves the time efficiency. Third, we implemented a prototype system and evaluated its performance in realistic settings. Experiment result shows that our solution can accurately detect the moving tags while reducing 80% of inventory time compared with state of arts solutions. II. RELATED WORKS Collision Recovery Many works focus on how to extract tag cardinality [13] or recover the tag signal [8, 9] from the collision signals based on the specialized instruments like USRP. Since Buettner et al. propose a Software Defined Ra￾dio based UHF-RFID reader [14], several researchers further leverage this platform to deal with the collision problems [7–9, 15]. Wang et al. [7] implement a new scheme which enables rateless code transmitting. [8, 9] use the time-domain separation to recovery the data from the collision signals. Hou et al. [13] present a physical-layer cardinality estimator from the collision signals for large scale RFID system. Physical Layer Identification Previous studies [16, 17] focus on physical-layer identification by leveraging the hardware im￾perfection in tag manufacturing. Davide et al.[16] distinguish from different tags based on the frequency difference ∂TIE caused by manufacturing imperfection of tags. Han et al. [18] leverage the internal similarity among pulses of tags’ RN16 preamble signals as the fingerprint for distinguishing. Zheng et al. [10] employ a method to detect the missing tags based on physical-layer signals. Different from previous work, in this paper, we focus on how to design a real-time tag monitoring scheme to efficiently detect the motion status of all tags, so as to further support tracking the movement of all tags. We aim to improve both the time efficiency in tag inventory and the accuracy in detect the motion status of all tags. III. SYSTEM OVERVIEW A. Design Goals In this paper, we propose a real-time approach to detect the motion status of all tags in the monitoring area, so as to further support tracking the movement of all tags. Because the tags may change their motion status any time, we need to continuously update the motion status within a limited time interval. Therefore, our objective in designing a moving tag detection scheme is to improve both the time efficiency in tag inventory and the accuracy in detecting the motion status of all tags. 1) The average time for each cycle of tag inventory should be sufficiently reduced to achieve the real￾time requirement for large scale RFID systems. 2) There are two kinds of errors in the problem: a) False positive errors: the stationary tags are identified as moving tags. b) False negative errors: the moving tags are identified as stationary tags. Both of the two errors should be effectively reduced in detecting the motion status. B. System Framework In order to effectively detect the motion status of all tags, we exploit the relationship between the physical-layer features and the motion status of tags. The following two physical￾layer features are investigated: 1) Phase profile: it is the phase value of an RF signal. The phase value from the tag’s response changes even if the tag is moved with a small distance. 2) Backscatter link frequency (BLF): it is the frequency of the tag-to-reader link, which determines the tag’s data rate in