Device-free object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects, such as intruders, which do not carry any transceiver. However, existing device-free solutions mainly use special sensors or active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a newly observed phenomenon called critical state caused by interference among passive tags. We contribute to both theory and practice of this phenomenon by presenting a new interference model that precisely explains it and using extensive experiments to validate it. We design a practical Twins based intrusion detection system and implement a real prototype by commercial off-the-shelf RFID reader and tags. Experimental results show that Twins is effective in detecting the moving object, with very low location errors of 0.75 m in average (with a deployment spacing of 0.6 m).

Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on radio frequency identification (RFID) tags require nongeneric equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10 000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68% +. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior Identification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, a multi-RSS based tag localization protocol, and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

Device-free based object tracking provides a promising solution for many localization and tracking systems to monitor non-cooperative objects which do not carry any transceiver such as intruders. However, existing device-free solutions mainly use sensors and active RFID tags, which are much more expensive compared to passive tags. In this paper, we propose a novel motion detection and tracking method using passive RFID tags, named Twins. The method leverages a phenomenon called critical state caused by interference among passive tags. We theoretically explain this phenomenon via an interference model and conduct extensive experiment to validate it. We design a practical Twins based intrusion detection system and implement a real prototype with commercial off-the-shelf reader and tags. Experimental results show that Twins is effective in detecting the moving object, with low location errors of 0.75m in average.

In this paper, we develop novel physical-layer identification schemes for passive Radio Frequency IDentification (RFID) tags. Due to the collision among tags, existing RFID systems suffer from a low identification efficiency. In this paper, we propose to use the unique physical-layer information of tags as the identification basis. We design a batch identification scheme for passive tags. Our Scheme can fully utilize the collided signals to achieve efficient and trustworthy identification. Leveraging collided signals, we also propose an AoA-based spatial identification scheme for providing location service. Our scheme are seamlessly compatible with commercial off-the-shelf RFID devices. The initial result shows the feasibility of our proposals.

Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on RFID tags require non-generic equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among the pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10,000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68%+. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.