In this paper, we propose a Dynamic Speed Warping(DSW) algorithm to enable one-shot learning for device-free gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains that have a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users, while only use one training sample of each gesture type from four training users.

With the flourish of the smart devices and their applications, controlling devices using gestures has attracted increasing attention for ubiquitous sensing and interaction. Recent works use acoustic signals to track hand movement and recognize gestures. However, they suffer from low robustness due to frequency selective fading, interference and insufficient training data. In this work, we propose RobuCIR, a robust contact-free gesture recognition system that can work under different usage scenarios with high accuracy and robustness. RobuCIR adopts frequency-hopping mechanism to mitigate frequency selective fading and avoid signal interference. To further increase system robustness, we investigate a series of data augmentation techniques based on a small volume of collected data to emulate different usage scenarios. The augmented data is used to effectively train neural network models and cope with various influential factors (e.g., gesture speed, distance to transceiver, etc.). Our experiment results show that RobuCIR can recognize 15 gestures and outperform state-of-the-art works in terms of accuracy and robustness.

Human activity recognition (HAR) has become increasingly essential due to its potential to support a broad array of applications, e.g., elder care, and VR games. Recently, some pioneer WiFi-based HAR systems have been proposed due to its privacy-friendly and device-free characteristics. However, their crucial limitation lies in ignoring the inevitable impact of co-channel interference (CCI), which degrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel HAR system to exploit interference-independent phase component, NLPEV (Nonlinear Phase Error Variation), of Channel State Information (CSI) to cope with the impact of CCI. We provide a rigorous analysis of NLPEV data with respect to its stability and otherness. Validated by our experiments, this phase component across subcarriers is invariant to various CCI scenarios, while different for distinct motions. Based on the analysis, we use NLPEV data to perform HAR in CCI scenarios. Extensive experiments demonstrate that PhaseAnti can reliably recognize activity in various CCI scenarios. Specifically, PhaseAnti achieves 95% recognition accuracy rate (RAR) on average, which improves up to 16% RAR in the presence of CCI. Moreover, the recognition speed is 9× faster than the state-of-the-art solution.

In this paper, we propose WiHF, which first simultaneously enables real-time cross-domain gesture recognition and user identification with Wi-Fi, a fundamental step towards ubiquitous device-free sensing. The key innovation of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, which contains both unique gesture characteristics and personalized user performing styles. To achieve real-time processing, based on the seam carving algorithm, we develop a heuristic method to extract the motion change pattern. Taking the motion change pattern as input, a deep neural network (DNN) is adopted for gesture recognition and user identification tasks. In DNN, we apply splitting and merging schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset which contains 6 user and 8 gestures performed across 5 locations and orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art methods, but the processing time is reduced by 30\(\times\).

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show the improvements tend to improve with the network size and that our solution is close to optimal in average.

This work enhances the machine-to-human communication between electronic toll collection (ETC) systems and drivers by providing an AM broadcast service to deployed ETC systems. This study is the first to show that ultra-high radio frequency identification signals can be received by an AM radio receiver due to the presence of the nonlinearity effect in the AM receiver. Such a phenomenon allows the development of a previously infeasible cross-technology and cross-frequency communication, called Tagcaster, which converts an ETC reader to an AM station for broadcasting short messages (e.g., charged-fees and traffic forecast) to drivers at tollbooths. The key innovation in this work is the engineering of Tagcaster over off-the-shelf ETC systems using shadow carrier and baseband whitening without the need for hardware nor firmware changes. This feature allows zero-cost rapid deployment in existing ETC infrastructure. Two prototypes of Tagcaster are designed, implemented and evaluated over four general and five vehicle-mounted AM receivers (e.g, Toyota, Audi, and Jetta). Experiments reveal that Tagcaster can provide good-quality (PESQ>2) and stable AM broadcasting service with a 30 m coverage range. Tagcaster remarkably improves the user experience at ETC stations and two-thirds volunteer drivers rate it with a score of 4+ out of 5.

Industrial Scientific Medical (ISM) band has become more and more crowded due to the ever-growing size of many mainstream technologies, e.g., Wi-Fi, ZigBee and Bluetooth. Though they compete for limited spectrum resources leading to severe Cross Technology Interference (CTI), it also provides great opportunities to better utilize the scarce bandwidth resources. A fundamental question is how to ensure harmonious and effective operations for these networks? To exploit this issue, a novel global cooperation framework is proposed. In particular, our work enables direct and simultaneous Cross Technology Communication (CTC) from a single Wi-Fi to ZigBee, Bluetooth and Wi-Fi commodity devices sharing the same band. Compared to existing CTC approaches, our scheme improves the communication efficiency significantly, and hence is the foundation for effective global cooperation. Based on the proposed CTC scheme, a unified Media Access Control (MAC) approach is introduced to cooperate CTC message transmission and reception for heterogeneous devices with different MACs. Two proof-of-concepts applications, e.g. global synchronization and global CTI coordination are discussed to fully leverage the benefits of global cooperation. Extensive evaluations show that compared with existing schemes, the proposed framework achieves 13 times lower synchronization error and 9 times lower average packet delay in CTI intensive environments.

The increasing congestion of the RF spectrum is a key challenge for low-power wireless networks using concurrent transmissions. The presence of radio interference can indeed undermine their dependability, as they rely on a tight synchronization and incur a significant overhead to overcome packet loss. In this paper, we present Harmony, a new data collection protocol that exploits the benefits of concurrent transmissions and embeds techniques to ensure a reliable and timely packet delivery despite highly congested channels. Such techniques include, among others, a data freezing mechanism that allows to successfully deliver data in a partitioned network as well as the use of network coding to shorten the length of packets and increase the robustness to unreliable links. Harmony also introduces a distributed interference detection scheme that allows each node to activate various interference mitigation techniques only when strictly necessary, avoiding unnecessary energy expenditures while finding a good balance between reliability and timeliness. An experimental evaluation on real-world testbeds shows that Harmony outperforms state-of-the-art protocols in the presence of harsh Wi-Fi interference, with up to 50% higher delivery rates and significantly shorter end-to-end latencies, even when transmitting large packets.

RFID applications for taking inventory and processing transactions in point-of-sale (POS) systems improve operational efficiency but are not designed to provide insights about customers' interactions with products. We bridge this gap by solving the proximity grouping problem to identify groups of RFID tags that stay in close proximity to each other over time. We design DeepTrack, a framework that uses deep learning to automatically track groups of items carried by a customer during her shopping journey. This unearths hidden purchase behaviors helping retailers make better business decisions and paves the way for innovative shopping experiences such as seamless checkout (`a la Amazon Go). DeepTrack employs a recurrent neural network (RNN) with attention mechanisms, to solve the proximity grouping problem in noisy settings without explicitly localizing tags. We tailor DeepTrack's design to track not only mobile groups (products carried by customers) but also flexibly identify stationary tag groups (products on shelves). The key attribute of DeepTrack is that it only uses readily available tag data from commercial off-the-shelf RFID equipment. Our experiments demonstrate that, with only two hours training data, DeepTrack achieves a grouping accuracy of 98.18% (99.79%) when tracking eight mobile (stationary) groups.

Identification and tracking of multiple objects are essential in many applications. As a key enabler of automatic ID technology, RFID has got widespread adoption with item-level tagging in everyday life. However, restricted to the computation capability of passive RFID systems, tracking tags has always been a challenging task. Meanwhile, as a fundamental problem in the field of computer vision, object tracking in images has progressed to a remarkable state especially with the rapid development of deep learning in the past few years. To enable lightweight tracking of a specific target, researchers try to complement computer vision to existing RFID architecture and achieves fine granularity. However, such solution requires calibration of the camera's extrinsic parameters at each new setup, which is not convenient for usage. In this work, we propose Tagview, a pervasive identifying and tracking system that can work in various settings without repetitive calibration efforts. It addresses the challenge by skillfully deploying the RFID antenna and video camera at the identical position and devising a multi-target recognition schema with only image-level trajectory information. We have implemented Tagview with commercial RFID and camera devices and evaluated it extensively. Experimental results show that our method can archive high accuracy and robustness.

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a reliable BLE backscatter system that works with a single commodity receiver. It first introduces direct frequency shift modulation with the single tone generated by an excitation BLE device, making robust single-bit modulation possible. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink throughput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

A long-standing vision of backscatter communications is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatters toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further toward breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter links. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality by continuously adding redundant bits, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. We build a prototype PCB tag and test it in various outdoor and indoor environments. Our experiments show that our prototype achieves up to 10x throughput gain, or extends the range limit by 1.64x compared with the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.