Respiratory monitoring using wireless technologies has garnered significant attention for its potential in healthcare, smart cockpits, and various applications. Though extensively studied, existing systems face practical challenges in adapting to new data domains without substantial customization efforts. Current solutions attempt to address this limitation through domain-independent feature extraction or cross-domain feature translation, employing either knowledge-based sensing models or data-driven neural networks. However, these approaches typically require additional data collection or model retraining for new domains, significantly hindering their practical deployment. This paper proposes RF-Carer, a fully zero-effort cross-domain respiration monitoring system. Our key innovation lies in building an explainable propagation model to transform any heterogeneous signals under unknown domains into a unified form in the signal processing layer. To further address accidental irrelevant factors, we propose to align the feature spaces while suppressing the noisy ones with contrastive learning. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to 12 domains with 57 cases like unconstrained movements, unknown users, untrained environments, etc.. To the best of our knowledge, RF-Carer is the first zero-effort cross-domain respiration monitoring work with wireless RF signals and would be a fundamental step toward real-world deployments.

Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models with minimal labeled data. mmADA enhances adaptation by introducing Rényi Entropy-based uncertainty estimation to identify and label the most informative target samples. Additionally, it leverages contrastive learning and pseudo-labeling to refine feature alignment using unlabeled data. Evaluations with a TI IWR1443BOOST radar across multiple users, positions, and environments show that mmADA achieves over 90% accuracy in various cross-domain settings. Comparisons with five baselines confirm its superior adaptation performance, while further tests on unseen users, environments, and two additional open-source datasets validate its robustness and generalization.

Wireless respiratory monitoring has garnered significant attention for its potential in various applications. However, existing systems face practical challenges in adapting to new data domains without substantial customization efforts. Current solutions attempt to address this limitation through domain-independent feature extraction or cross-domain feature translation, employing either knowledge-based sensing models or data-driven neural networks. However, these approaches typically require additional data collection or model retraining for new domains, significantly hindering their practical deployment. This paper proposes RF-Carer, a fully zero-effort cross-domain respiration monitoring system. Our key innovation lies in building an explainable propagation model to transform any heterogeneous signals under unknown domains into a unified form in the signal processing layer. To further address accidental irrelevant factors, we propose to align the feature spaces while suppressing the noisy ones with contrastive learning. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to unknown scenarios with unconstrained user movements, postures, positions, etc. To the best of our knowledge, RF-Carer is the first zero-effort cross-domain respiration monitoring work with wireless RF signals and would be a fundamental step toward real-world deployments.Chen

The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations such as low lighting conditions, occlusions, and privacy concerns. To address these challenges, we introduce mmYodar+, a novel mmWave-based automatic human detection system. Our system processes mmWave signals to generate a 3D point cloud, which is then transformed into a 2D radar image for easier visualization and analysis. To enhance human profiling, we filter the point cloud using biometric information and expand human-related points in the image based on radar angle resolution, incorporating color to improve the differentiation. Additionally, we employ a deep mutual learning (DML) framework, enabling efficient human detection using a lightweight DNN. Experimental results show that mmYodar+ achieves an average precision of 96.29% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human detection.

Contactless mmWave-based human activity recognition (HAR) is essential for various applications, yet most existing approaches often assume consistent environments. Integrating domain adaptation offers a promising solution to this challenge. This prevailing paradigm works well when the source and target data are centralized on a single server while learning to adapt. However, in more universal and practical situations, such as personal health records, users’ biometric information, and financial issues, the raw data is typically protected by different privacy-preserving policies and is stored by multiple parties. Additionally, labeling RF signals in the target domain is a non-trivial and labor-intensive task for most end-users. To address these problems, this paper introduces FMDA, a federated multi-source domain adaptation framework for mmWave-based HAR. FMDA assesses the contribution of each source and performs weighted parameter aggregation for knowledge transfer. This facilitates unsupervised training of the target HAR model without requiring access to any source domain data. Moreover, the model is optimized by minimizing the generalization gaps between the source and target models, benefiting all participants during the learning process and enhancing overall performance. Extensive experiments demonstrate the effectiveness of FMDA. The results indicate that in the target domain, FMDA achieves comparable performance to supervised learning approaches, while also enhancing the efficacy of source domain models to varying degrees.

Falls pose a significant risk to the health and wellbeing of older adults, driving the development of various fall detection systems. Existing solutions have explored wearable and vision sensors, while non-invasive RF-based approaches have raised a growing interest due to their convenience and privacy considerations. Despite major advancements in RF-based passive estimation, current approaches still face challenges in handling complex real-world scenarios. They often lack the ability to generalize to new domains (i.e., people, position, environment), and struggle to accurately detect and localize a fallen person in the presence of unknown activities from nearby objects (e.g., pet animal and robot vacuum cleaner) or persons. To address these challenges, we present mm-Fall, a novel mmWave-based non-invasive fall detection system that utilizes Range-Angle (RA) energy maps to separate and localize multiple moving targets, and further accurately estimate their states. Unlike previous approaches, mm-Fall is capable of working with new domains and effectively distinguishing falls from non-fall motions that may appear similar. Additionally, it performs well in challenging conditions, such as poor lighting and occluded scenarios. Our design of mm-Fall is evaluated in 13 environments with over 16 individuals performing 24+ types of motions. The results demonstrate an impressive average recall of 0.969 and precision of 0.996 in detecting falls, whether involving single or multiple moving targets simultaneously. The code and dataset will be made publicly available.

This paper introduces a pioneering framework for music representation learning, which harnesses knowledge graph embeddings to enrich genre classification. Leveraging metadata from publicly available datasets like FMA and OpenMIC-2018, the constructed knowledge graph delineates intricate relationships among genres, artists, and instruments, offering valuable insights for genre representation. Within this framework, we propose two models tailored for distinct genre classification scenarios: fixed-set genre classification and open-set genre classification. These models exploit the knowledge graph to unveil correlations among different genres and integrate this knowledge into the audio representation. Notably, our approach is the first to merge audio data with high-level knowledge for music genre classification. Experimental results demonstrate that our proposed methods outperform state-of-the-art approaches, achieving an average genre classification accuracy of 68.07% on the FMA-medium dataset and 42.4% for open-set classification on the FMA-large dataset.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bound to the tag’s movement, e.g., the localization of tags. However, it imposes inevitable uncertainty on many sensing tasks relying on the features extracted from the RFID signals. These traditional sensing measurements limit the fidelity of RFID sensing fundamentally and prevent its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150-dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware. It requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines the correlation between different genres from the knowledge graph and embeds such correlation in audio representation. To our knowledge, KGenre is the first method fusing the audio with high-level knowledge for music genre classification. Experimental results demonstrate the embedded knowledge can effectively enhance the audio feature representation, and the genre classification performance surpasses the state-of-the-art methods.

The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations such as low lighting conditions, occlusions, and privacy concerns. To overcome these limitations, we propose a novel automatic object detection system, called mmYodar, which utilizes millimeter-wave (mmWave) radar signals. Our system collects mmWave signals and calculates a 3D point cloud, which is transformed into a radar image for easier visualization and analysis. To improve the system's human profiling capability, we expand the corresponding points in the image with color based on the radar angle resolution. Then, a designed deep mutual learning framework is employed to detect human objects from the expanded image. Experimental results show that mmYodar achieves nearly real-time detection with an average precision of 90.35% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human object detection. Our code and dataset are available at https:llgithub.comlbrave20005lmmYodar.

Radio frequency identification (RFID)-assisted management systems have been widely applied in warehousing, logistics, retailing, etc. In these scenarios, RFID-aided applications, e.g., object tracking and human behavior sensing, rely on a high-efficiency tag reading to realize accurate analyses and timely responses. However, serious tag collisions in those large-scale RFID systems will inevitably lead to significant decreases in the tag reading rates. To meet the strict timeliness requirements of those practical applications, we aim to treat the individual reading rate for each item tag differently and focus more attention on those user-interactive ones. However, due to unpredictable user behaviors, it is impractical to infer the user-interactive tags in advance. In addition, keeping focusing on them for continuous monitoring despite user movements and multipath-prevalent environments is also challenging. To solve these problems, we propose Spotlight, the first concurrent rate-adaptive reading system in passive RFIDs. Spotlight screens the ID-agnostic user-interactive tags by proposing a multichannel feature for narrow-band RFID systems without any hardware or protocol modification and achieves rate-adaptive reading by implementing real-time MU-MIMO beamforming. Substantial experiments with 1000+ COTS RFID tags exhibit that Spotlight outperforms the commercial reader by$2.7\times $and the SDR-based reader by$6.12\times $. In addition, Spotlight first proposes the online parallel decoding method to realize concurrency among multiple users, which breaks the commercial protocol’s throughput ceiling (37%) and achieves up to 59% throughputs.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. These services require short-latency and low-overhead implementation in network and computing devices and load balance on edge clouds. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This article presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED by associating a virtual position with each data by hashing, and storing the data at the edge server connected to the switch whose position is the nearest to the position of the data in the virtual space. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring ‘multi-path-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and study three major RFID sensing applications: tag localization, device calibration and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of device calibration and human behaviour sensing.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bounded to the tag’s own movement, e.g., the localization of tags. However, it imposes inevitable uncertainty to many sensing tasks relying on the features extracted from the RFID signals, which limits the fidelity of RFID sensing fundamentally and prevents its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of the RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150 dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware, requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

Identifying item-item and user-item interactions is an essential requirement of many ubiquitous computing applications. Recently methods of physically altering RFID tag hardware have been proposed to enable recognizing certain interactions. However, they do not address the problem that when a large number of tags exist in the environment and concurrent interactions may happen, the system may not be able to identify these interactions accurately or efficiently. We propose ChopTags, a low-cost and accurate interaction identification using passive RFID tags, with applications including automatic chess notation, shipment storage tracking, interactive libraries/retail stores/classrooms, and smart conference badges to track the attendees who had conversations. Each ChopTags module contains a passive tag chip and an antenna that are separated and can only be read when the chip is in contact with an antenna (from another pairing ChopTags module). ChopTags costs cheap hardware to scale to many users and items, achieves near 100% accuracy in complex environments, and is easy to use for children, seniors, and others who have difficulty of operating smart devices. We resolve a number of challenges of using ChopTags including improving query throughput/accuracy and identifying concurrent interactions. We build two prototypes based on ChopTags: 1) a chess auto-notation system and 2) a tag array for user interactions. ChopTags allows tracking the moves of 96 tag modules for the chess game with almost 100% accuracy and no prior work can achieve this.

We study a new problem, refined localization, in this article. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber–physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, prelearning process, and high computation overhead. Also, vision-based approaches cannot differentiate objects with similar colors and shapes. This article presents a new refined localization system, called Trio, which uses passive radio frequency identification (RFID) tags for low cost and easy deployment. Trio utilizes RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., < 1 cm errors for several types of main stream tags.

As the ultra high frequency (UHF) passive radio frequency identification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily access the tags, e.g., retrieve or modify IDs or other data in the memory, via standard commands. To deal with this type of attack, we propose a physical-layer tag protection framework, namely Arbitrator2.0, that involves two operating mode, i.e., one is to passively listen on RF channels and identify unauthorized readers, the other is working as normal reader to access tag information but resilient to one-antenna eavesdropper. Our solution does not need to modify RFID tags or the underlying communication standards. In this study, we have implemented a prototype Arbitrator2.0 over the universal software radio peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. The results show that Arbitrator2.0 can effectively diminish the unauthorized access attacks and prevent eavesdropping.

Edge computing promises a dramatic reduction in the network latency and the traffic volume, where many edge servers are placed at the edge of the Internet. Furthermore, those edge servers cache data to provide services for edge users. The data sharing among those edge servers can effectively shorten the latency to retrieve the data and further reduce the network bandwidth consumption. The key challenge is to construct an efficient data indexing mechanism no matter how the data is cached in the edge network. Although this is essential, it is still an open problem. Moreover, existing methods such as the centralized indexing and the DHT indexing in other fields fail to meet the performance demand of edge computing. This paper presents a COordinate-based INdexing (COIN) mechanism for the data sharing in edge computing. COIN maintains a virtual space where switches and data indexes are associated with their coordinates. Then, COIN distributes data indexes to indexing edge servers based on those coordinates. The COIN is effective because any query request from an edge server can be responded when the data has been stored in the edge network. More importantly, COIN is efficient in both routing path lengths and forwarding table sizes for publishing/querying data indexes. We implement COIN in a P4 prototype. Experimental results show that COIN uses 59% shorter path length and 30% less forwarding table entries to retrieve data indexes compared to using Chord, a well-known DHT solution.

Though widely used in industrial and logistic applications, current passive Radio Frequency Identification (RFID) technology still has a fundamental limitation: Individual users who do not carry any reader find it difficult to interact with tagged items, such as retrieving their digital profiles and requesting certain associations with them. Recent proposals to improve the user–item interaction experience rely on special hardware, such as a smartphone-based RFID scanner. This work presents a promising approach to allowing each user to interact with a tagged item using only one passive tag, which is named the Tag Mutual Identification Interface (TagMii). TagMii requires a user to put one’s user tag in physical proximity with an item tag to express certain interactions between the user and item. The key idea behind TagMii is to utilize two experimental observations: (1) inductive coupling for detecting interaction events, and (2) channel similarity for determining the actual interacting tags. We implement TagMii using commodity off-the-shelf RFID devices and conduct experiments in complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. The results show that TagMii provides accurate mutual identification. TagMii is a completely new approach for user–item interactions in pervasive environments and enables many user-friendly Internet of Things applications with low cost and convenience.

Presents corrections to the acknowledgement section for the above named article.

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that achieves mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video and keeps tracking it accurately over time.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multi-path-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal-and multi-person cases, respectively.

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

Though being widely used in industrial and logistic applications, current passive RFID technology still has a fundamental limitation: Individual users, who do not carry any reader, are difficult to interact with tagged items, such as retrieving their digital profiles and requesting certain association with them. Recent proposals to improve user-item interaction experience rely on special hardware such as smartphone based RID scanner. This work presents a promising approach to allow each user to interact with tagged item using only one passive tag, which is named Tag Mutual Identification Interface (TagMii). TagMii requires a user to put her user tag in a physical proximity with an item tag to express certain interactions between the user and item. The key idea behind TagMii is to utilize two experimental observations: 1) inductive coupling for detecting interaction events, and 2) channel similarity for determining the actual interacting tags. We implement TagMii using commodity off-the-shelf RID devices and conduct experiments in complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. The results show that TagMii provides accurate mutual identification. TagMii is a completely new approach for user-item interactions in pervasive environments and enables many user-friendly IoT applications with low cost and convenience.

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that archives mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices, in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video aWe thank the anonymous reviewers for their suggestions and comments.nd keeps tracking it accurately over time.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multipath-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID, a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localization system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., <; 1 cm errors for several types of main stream tags.

As the Ultra High Frequency (UHF) passive Radio Frequency IDentification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily modify the tags via standard commands, e.g., IDs or other data in the memory. To deal with this type of attack, we propose a physical-layer RF signal based reader authentication solution, namely Arbitrator, that involves passively listening on RF channels, analyzing the communication signals, identifying unauthorized readers and jamming the commands from such readers. Our solution does not need to modify RFID devices or the underlying communication standards, hence fully compatible with the existing RFID infrastructure. In this study, we have implemented a prototype Arbitrator over the Universal Software Radio Peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. Our results show that Arbitrator can detect unauthorized RFID readers with high accuracy, and thus effectively diminish the unauthorized access attacks.

We provide the first solution to an important question, "how a physical-layer authentication method can defend against signal replay attacks''. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.

There have been increasing interests in exploring the sensing capabilities of RFID beyond its basic identification task, to reveal more information of tagged objects and the physical world. Phase is a crucial physical feature for many RFID sensing applications, such as tag localization. Most existing methods rely on a low multipath environment for accurate phase measurement. Unfortunately, practical environments are highly likely to be multipath-revalent, especially for indoors. This paper presents the first work to extract "clean" phase measurement of RFID tags in multipath-prevalent environments. We propose CPEX (Clean Phase EXtraction) based on theoretical modeling, which is also validated via experimental results of our prototype implementation. We studied the performance of CPEX on tag localization. CPEX can achieve median errors of 4.3-6.4cm (in different setups), which is the most accurate 3D tag localization result in multipath-prevalent environments.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations the relative locations of objects are more important than their absolute locations. However, state-of-art relative localization methods need continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a relative localization approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device. Hence our method is called Human Movement based Relative Localization (HMRL). The basic idea of HMRL is that when people pass between reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMRL can obtain the order of tags along a specific horizontal direction. HMRL can also get the order of tags in a vertical direction using hyperbolic positioning. We implement HMRL with commodity off-the-shelf RFID devices. The experimental results show that HMRL achieves high accuracy for relative localization of passive tags.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect arm reaching by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of human movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID an commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios in both horizontal and vertical deployment modes. The experimental results show that APID can achieve high detection accuracy.

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.