Radio frequency identification (RFID) technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the commercial off-the-shelf (COTS) RFID products with detected phases. Our studies show that the phase is a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until finding out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12:8cm with variance of 3:8 cm.

For today's computer users, the mouse plays such an important role that it dominates the interaction interface in personal computer for nearly half a century since it was invented. However, the mouse is gradually unfit for the demand of modern 3D display techniques, e.g. 3D-projection or -screen, for the reason that the relevant interactions are confined in a surface. Although some new methods such as computer vision based techniques attempt to bridge the human-computer barrier, they suffer from many limitations such as ambiguity in multitargets and dependence on light. This paper presents a battery-free device called Tagball for 3D human-computer interaction via RFID tags. Tagball devises a control ball, on which N passive tags are attached, for users to generate two basic kinds of interactive commands: translation and rotation. Instead of locating N tags independently, we model the ball as a whole in a more cooperative way under the circumstance that their geometric relationships are known in advance. In addition, we consider the phase values measured by M RF antennas for these N tags as observations of the ball state. Our key innovations are the studies on motion behaviors of a group of tags by using Extended Kalman Filter, and the implementation based on purely Commercial Off-The-Shelf (COTS) RFID products. The systematical evaluation shows that Tagball traces the ball translation to 1.5cm and identifies ball orientation to 1.8° in 3D space.

To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of `deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.

Through-wall tracking has gained a lot of attentions in civilian applications recently. Many applications would benefit from such device-free tracking, e.g. elderly people surveillance, intruder detection, gaming, etc. In this work, we present a system, named Tadar, for tracking moving objects without instrumenting them us- ing COTS RFID readers and tags. It works even through walls and behind closed doors. It aims to enable a see-through-wall technology that is low-cost, compact, and accessible to civilian purpose. In traditional RFID systems, tags modulate their IDs on the backscatter signals, which is vulnerable to the interferences from the ambient reflections. Unlike past work, which considers such vulnerability as detrimental, our design exploits it to detect surrounding objects even through walls. Specifically, we attach a group of RFID tags on the outer wall and logically convert them into an antenna array, receiving the signals reflected off moving objects. This paper introduces two main innovations. First, it shows how to eliminate the flash (e.g. the stronger reflections off walls) and extract the reflections from the backscatter signals. Second, it shows how to track the moving object based on HMM (Hidden Markov Model) and its reflections. To the best of our knowledge, we are the first to implement a through-wall tracking using the COTS RFID systems. Empirical measurements with a prototype show that Tadar can detect objects behind 5" hollow wall and 8" concrete wall, and achieve median tracking errors of 7.8cm and 20cm in the X and Y dimensions.

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~10 cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.

Indoor navigation has received much attention in academics and industry in recent years. Previous methods often attempt to locate users with various localization algorithms in combination with an indoor map, so they need expensive infrastructures deployed in advance. In this study, we propose to utilize existing indoor objects attached RFID tags and the reader to navigate the user to the destination, without need of any extra hardware. The key insight is that the personal movement takes an impact on the Doppler frequency shift values collected from the indoor objects when getting close to the tag. Such local human-item spatial relation is leveraged to infer the users position and further navigate user to destination step by step. We implement a prototype navigation system, called RollCaller, and conduct comprehensive experiments to examine its performance.

RFID technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. For example, the RSS based methods are vulnerable to both distance and tag orientation, while AOA based methods put a strict constraint on the antennas' spacing that reader's directional antennas are too large to meet. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the COTS RFID products with detected phase. Our study shows that the phase is indeed a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until filtering out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12.8cm with variance of 3.8cm.

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~ 10cm) can be precisely perceived, and the accuracy is up to 92.34% while the false positive is suppressed under 0.5%.