Xiuzhen Guo

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate and respiration rate of mobile users with diverse skin tones. MagWear’s contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Secondly, leveraging insights derived from this mathematical model, we present a softwarehardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. We have implemented a prototype of MagWear on a two-layer PCB board and followed IRB protocols to conduct system evaluations. Our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate and 1.79% for respiration rate. The head-tohead comparison with Apple Watch 8 further demonstrates MagWear’s consistently high performance in different user conditions.

Backscatter is an enabling technology for battery-free sensing in industrial IoT applications. For the purpose of full coverage of numerous tags in the deployment area, one often needs to deploy multiple readers, each of which is to communicate with tags within its communication range. But the actual backscattered signals from a tag are likely to reach a reader outside its communication range, causing undesired interference. Conventional approaches for interference avoidance, either TDMA or CSMA based, separate the readers’ media accesses in the time dimension and suffer from limited network throughput. In this paper, we propose TRIDENT, a novel backscatter tag design that enables interference avoidance with frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a TRIDENT tag is able to detect its channel condition and adaptively adjust the frequency band and the power of its backscattered signals, so that all the readers in the network can operate concurrently without being interfered. We implement TRIDENT and evaluate its performance under various settings. The results demonstrate that TRIDENT enhances the network throughput by 3.18×, compared to the TDMA based scheme.

The elderly over 65 accounts for 80% of COVID deaths in the United States. In response to the pandemic, the federal, state governments, and commercial insurers are promoting video visits, through which the elderly can access specialists at home over the Internet, without the risk of COVID exposure. However, the current video visit practice barely relies on video observation and talking. The specialist could not assess the patient’s health conditions by performing auscultations. This paper tries to address this key missing component in video visits by proposing Asclepius, a hardware-software solution that turns the patient’s earphones into a stethoscope, allowing the specialist to hear the patient’s fine-grained heart sound (i.e., PCG signals) in video visits. To achieve this goal, we contribute a low-cost plug-in peripheral that repurposes the earphone’s speaker into a microphone and uses it to capture the patient’s minute PCG signals from her ear canal. As the PCG signals suffer from strong attenuation and multi-path effects when propagating from the heart to ear canals, we then propose efficient signal processing algorithms coupled with a data-driven approach to de-reverberate and further correct the amplitude and frequency distortion in raw PCG receptions. We implement Asclepius on a 2-layer PCB board and follow the IRB protocol to evaluate its performance with 30 volunteers. Our extensive experiments show that Asclepius can effectively recover Phonocardiogram (PCG) signals with different types of earphones. The objective blind testing and subjective interview with five cardiologists further confirm the clinical efficacy and efficiency of our system. PCG signal samples, benchmark results, and cardiologist interviews can be found at: https://asclepius-system.github.io/

Backscatter is an enabling technology for battery-free sensing in today’s Artificial Intelligence of Things (AIOT). Building a backscatter-based sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier’s traffic. In order to address the above issues, in this paper, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency. Leveraging a specially designed circuit with a varactor diode, this design avoids using a microprocessor to interface between the radio and the sensor, and directly converts the analog sensor signal into the phase of RF (radio frequency) signal. By carefully designing the reference circuit on the tag and precisely locating the extra long training field (LTF) section of a WiFi packet, Leggiero embeds the analog phase value into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement Leggiero design and evaluate its performance under varied settings. The results show that the power consumption of the Leggiero tag (excluding the power of the peripheral sensor module) is 30μW at a sampling rate of 400Hz, which is 4.8\texttimes and 4\texttimes lower than the state-of-the-art WiFi backscatter schemes. The uplink throughput of Leggiero is suficient to support a variety of sensing applications, while keeping the WiFi carrier’s throughput performance unaffected.

The ever-developing Internet of Things (IoT) brings the prosperity of wireless sensing and control applications. In many scenarios, different wireless technologies coexist in the shared frequency medium as well as the physical space. Such wireless coexistence may lead to serious cross-technology interference (CTI) problems, e.g., channel competition, signal collision, and throughput degradation. Compared with traditional methods like interference avoidance, tolerance, and concurrency mechanism, direct and timely information exchange among heterogeneous devices is therefore a fundamental requirement to ensure the usability, inter-operability, and reliability of the IoT. Under this circumstance, Cross-Technology Communication (CTC) technique thus becomes a hot topic in both academic and industrial fields, which aims at directly exchanging data among heterogeneous devices that follow different standards. This paper comprehensively summarizes the CTC techniques and reveals that the key challenge for CTC lies in the heterogeneity of IoT devices, including the incompatibility of technical standards and the asymmetry of connection capability. Based on the above finding, we present a taxonomy of the existing CTC works (packet-level CTCs and physical-level CTCs) and compare the existing CTC techniques in terms of throughput, reliability, hardware modification, and concurrency.

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6–74.2X less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92–678X. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RF-Transformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer.

The radio range of backscatter systems continues growing as new wireless communication primitives are continuously invented. Nevertheless, both the bit error rate and the packet loss rate of backscatter signals increase rapidly with the radio range, thereby necessitating the cooperation between the access point and the backscatter tags through a feedback loop. Unfortunately, the low-power nature of backscatter tags limits their ability to demodulate feedback signals from a remote access point and scales down to such circumstances. This paper presents Saiyan, an ultra-low-power demodulator for long-range LoRa backscatter systems. With Saiyan, a backscatter tag can demodulate feedback signals from a remote access point with moderate power consumption and then perform an immediate packet re-transmission in the presence of packet loss. Moreover, Saiyan enables rate adaption and channel hopping – two PHY-layer operations that are important to channel efficiency yet unavailable on long-range backscatter systems. We prototype Saiyan on a two-layer PCB board and evaluate its performance in different environments. Results show that Saiyan achieves 3.5–5× gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of Saiyan is around 93.2 µW. Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan.

LoRaWAN has emerged as an appealing technology to connect IoT devices but it functions without explicit coordination among transmitters, which can lead to many packet collisions as the network scales. State-of-the-art work proposes various approaches to deal with these collisions, but most functions only in high signal-to-interference ratio (SIR) conditions and thus does not scale to real scenarios where weak receptions are easily buried by stronger receptions from nearby transmitters. In this paper, we take a fresh look at LoRa’s physical layer, revealing that its underlying linear chirp modulation fundamentally limits the capacity and scalability of concurrent LoRa transmissions. We show that by replacing linear chirps with their non-linear counterparts, we can boost the throughput of concurrent LoRa transmissions and empower the LoRa receiver to successfully receive weak transmissions in the presence of strong colliding signals. Such a non-linear chirp design further enables the receiver to demodulate fully aligned collision symbols — a case where none of the existing approaches can deal with. We implement these ideas in a holistic LoRaWAN stack based on the USRP N210 software-defined radio platform. Our head-to-head comparison with two stateof-the-art research systems and a standard LoRaWAN baseline demonstrates that CurvingLoRa1 improves the network throughput by 1.6–7.6⇥ while simultaneously sacrificing neither power efficiency nor noise resilience.

Wireless sensing has great significance in Internet of Things (IoT) applications and has attracted substantial research interests in academia. In this study, we propose Palantir, a first-of-its-kind long-range sensing system based on the LoRa backscatter technology. By utilizing the ON-OFF-Keying modulated backscatter signals, Palantir can perform fine-grained long-range cyclist sensing. Our findings show that sensing is more susceptible to channel quality than communication. Hence, the design of Palantir particularly addresses the critical challenges of signal processing, such as amplitude instability, frequency offset, clock drift, spectrum leakage, and multiplicative noise. We implement Palantir and evaluate its performance by conducting comprehensive benchmark experiments. A prototype is also built and a case study of respiration monitoring in the real world is implemented. Results demonstrate that Palantir can perform accurate sensing at a range up to 100 m, which is twice that of state-of-the-art approaches. The median deviation of the detected motion period is as low as 0.2%.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-ofthe-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5–199.4 Kbps data rate at various distances, 10.4–52.4× higher than PLoRa.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physicallevel CTC by emulating the standard time-domain waveform of the receiver. This method faces the challenges of inherent unreliability due to the imperfect emulation. Different from analog emulation, we propose a novel concept named digital emulation, which stems from the following insight: The receiver relies on phase shift rather than the phase itself to decode signals. Instead of emulating the original time-domain waveform, the sender emulates the phase shift associated with the desired signals. Clearly there are multiple different phase sequences that correspond to the same signs of phase shifts. Digital emulation has flexibility in setting the phase values in the emulated signals, which is effective in reducing emulation errors and enhancing the reliability of CTC. The key point of digital emulation is generic and applicable to a set of CTCs, where the transmitter has a wider bandwidth for emulation and the receiver decoding is based on the phase shift. In this paper, we implement our proposal as WIDE, a physical-level CTC via digital emulation from WiFi to ZigBee. We conduct extensive experiments to evaluate the performance of WIDE. The results show that WIDE significantly improves the Packet Reception Ratio (PRR) from 41.7% to 86.2%, which is 2× of WEBee’s, an existing representative physical-level CTC.

Cross-Technology Communication (CTC) emerges as a technology to enable direct communication across different wireless technologies. The state of the art on CTC employs physical-level emulation. Due to the protocol incompatibility and the hardware restriction, there are intrinsic emulation errors between the emulated signals and the legitimate signals. Unresolved emulation errors hurt the reliability of CTC and the achievable throughput, but how to improve the reliability of CTC remains a challenging problem. Taking the CTC from WiFi to BLE as an example, this work first presents a comprehensive understanding of the emulation errors. We then propose WEB, a practical CTC approach that can be implemented with commercial devices. The core design of WEB is split encoding: based on the probabilistic distribution of emulation errors, the WiFi sender manipulates its payload to maximize the successful decoding rate at the BLE receiver. We implement WEB and evaluate its performance with extensive experiments. Compared to two existing approaches, WEBee and WIDE, WEB reduces the SER (Symbol Error Rate) by 54.6% and 42.2%, respectively. For the first time in the community, WEB achieves practically effective CTC from WiFi to BLE, with an average throughput of 522.2 Kbps.