Xiuzhen Guo

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/

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-to-head comparison with Apple Watch 8 further demonstrates MagWear’s consistently high performance in different user conditions.

This paper proposes Meta-Speaker, an innovative speaker capable of projecting audible sources into the air with a high level of manipulability. Unlike traditional speakers that emit sound waves in all directions, Meta-Speaker can manipulate the granularity of the audible region, down to a single point, and can manipulate the location of the source. Additionally, the source projected by Meta-Speaker is a physical presence in space, allowing both humans and machines to perceive it with spatial awareness. Meta-Speaker achieves this by leveraging the fact that air is a nonlinear medium, which enables the reproduction of audible sources from ultrasounds. Meta-Speaker comprises two distributed ultrasonic arrays, each transmitting a narrow ultrasonic beam. The audible source can be reproduced at the intersection of the beams. We present a comprehensive profiling of Meta-Speaker to validate the high manipulability it offers. We prototype Meta-Speaker and demonstrate its potential through three applications: anchor-free localization with a median error of 0.13 m, location-aware communication with a throughput of 1.28 Kbps, and acoustic augmented reality where users can perceive source direction with a mean error of 9.8 degrees.

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.

We present the design and implementation of EarVoice, a lightweight mobile service that enables hands-free voice assistant activation on commodity earphones. EarVoice comprises two design modules: one for joint speech detection and primary user identification that explores the attributes of the air channel and in-body audio pathway to differentiate between the primary user and others nearby; and another for accurate wakeup word enhancement, which employs a "copy, paste, and adapt" approach to reconstruct the missing high-frequency component in speech recordings. To minimize false positives, enhance agility, and preserve privacy, we deploy EarVoice on a dongle where the proposed signal processing algorithms are streamlined with a gating mechanism to permit only the primary user’s speech to enter the pairing device (e.g., a smartphone) for wakeup word recognition, preventing unintended disclosure of ambient conversations. We implemented the dongle on a 4-layer PCB board and conducted extensive experiments with 23 participants in both controlled and uncontrolled scenarios. The experiment results show that EarVoice achieves around 90% wakeup word recognition accuracy in stationary scenarios, which is on par with the high-end, multi-sensor fusion-based Airpods Pro earbud. EarVoice’s performance drops to 84% on mobile cases, slightly worse than Airpods (around 90%).

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.

WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor environments, WiFi signals are everywhere, but very few of them are usable for accurate localization. As a result, the localization accuracy in practice is far from being satisfactory. This paper presents Bifrost, a novel hardwaresoftware co-design for accurate indoor localization. The core idea of Bifrost is to reinvent WiFi signals, so as to provide sufficient LoS signals for localization. This is realized by exploiting the dispersion effect of signals emitted by the leaky wave antenna (LWA). We present a low-cost plug-in design of LWA that can generate orthogonal polarized signals: On one hand, LWA disperses signals of different frequencies to different angles, thus providing Angle-of-Arrival (AoA) information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. In the software layer, fine-grained information in Channel State Information (CSI) is exploited to cope with multipath and noise. We implement Bifrost and evaluate its performance under various settings. The results show that the median localization error of Bifrost is 0.81m, which is 52.35% less than that of SpotFi, a state-of-the-art approach. SpotFi, when combined with Bifrost to work in the realistic settings, can reduce the localization error by 33.54%.

Earables are embedded devices that can be placed in, on, or around the ear to sense human motions and physiological activities over an extended period of time. However, today’s earable design principle heavily relies on dedicated sensors (e.g., accelerometer, gyroscope, proximity sensor), which inevitably adds cost, weight, and power consumption to earable devices, constituting a critical bottleneck in their wide adoption. Moreover, the tight coupling of sensors with onboard microcontrollers makes existing earables difficult to program, raising the barrier of entry to earable computing.In this poster, we describe HeadFi II, a stand-alone earable computing platform that integrates sensing and computing into a tiny hardware device with low power and computation footprint. We describe the design guideline and technical challenges as well as potential solutions. We believe this project would open up a new dimension of cutting-edge research and exciting educational opportunities. The developed solutions will lead to considerable advancements in both low-power hardware designs and efficient earable sensing algorithms, lowering the barrier of entry to earable computing by providing the research community with a versatile earable platform.

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 good tradeoff between transmission range and link throughput. 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. The design of Aloba completely unleashes the backscatter tag’s ability in OOK modulation and achieves flexible data rate at different transmission range. We implement Aloba and conduct head-to-head comparison with the state-of-the-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, 10.4–52.4X higher than PLoRa.

Time-Slotted Channel Hopping (TSCH) is a popular link-layer pro-tocol defined in the IEEE 802.15.4e standard that improves the reli-ability and throughput of wireless sensor networks by exploiting diversity in both time and frequency. Despite the body of literature proposing several scheduling schemes for TSCH, a gap yet to be filled is the design of an effective way to deal with internal and external interference, which are both known to strongly affect communication performance. In fact, existing works either make use of a fixed schedule (and hence cannot cope with interference), or re-quire extra control traffic (and hence increase energy consumption). In this paper, we present SmarTiSCH, an interference-aware en-gine for IEEE 802.15.4e-based networks that retains the simplicity and energy-efficiency of autonomous scheduling, while increasing the awareness as well as robustness to both internal and external interference. With SmarTiSCH, the nodes in the network infer the presence of interference and react to it without the need of extra control traffic. Specifically, SmarTiSCH enables each node to infer the interference by passively observing existing data exchanges. It then lets a pair of nodes exchange information and mutually agree on a proper strategy to mitigate interference without the need of extra transmissions. We implement SmarTiSCH in Contiki-NG and evaluate its performance on a testbed of 20 off-the-shelf IEEE 802.15.4 devices based on the nRF52840. Our results show that SmarTiSCH increases the reliability of transmissions by up to 2.9 times compared to state-of-the-art approaches in the presence of interference, while even lowering the duty cycle by 54.3%.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. Recent works achieve physical-level 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 the phase shift to decode symbols rather than the shape of analog time-domain waveform. There are lots of phase sequences which satisfy the requirement of phase shift. The distortions of these phase sequences after WiFi emulation are different. We have the opportunity to select an appropriate phase sequence with the relatively small emulation errors to achieve a reliable 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.

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. Saiyan is based on an observation that a frequency-modulated chirp signal can be transformed into an amplitude-modulated signal using a differential circuit. Moreover, we redesign a LoRa backscatter tag which integrates Saiyan and a ring oscillator-based modulator. The LoRa backscatter tag enables re-transmission, rate adaption and channel hopping – three PHY-layer operations that are important to channel efficiency yet unavailable on existing long-range backscatter systems. We prototype Saiyan and the LoRa backscatter tag on two PCB boards and evaluate their performance in different environments. Results show that Saiyan achieves 3.5– 5\times gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of Saiyan and the LoRa backscatter tag are around 93.2 μW and 94.7 μW . Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan.

This paper presents, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals in the PHY layer. By leveraging the nonlinear characteristics of the negative impedance, also achieves a cross-frequency backscatter design that enables IoT devices in harmonic frequency bands to communicate with each other. We implement a PCB prototype of on 2.4 GHz ISM band and conduct extensive experiments. We leverage the software defined platform USRP to transmit the carrier signal and receive the backscatter signal to verify the efficacy of our design. Our extensive field studies show that 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.

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.

The proliferation of IoT applications brings the demand of ubiquitous connections among heterogeneous wireless devices. Cross-Technology Communication (CTC) is a significant technique to directly exchange data among heterogeneous devices that follow different standards. By exploiting a side-channel like frequency, amplitude, or temporal modulation, the existing works enable CTC but have limited performance under channel noise. In this article, we propose WiZig, a novel CTC technique from WiFi to ZigBee that employs modulations in both the amplitude and temporal dimensions to optimize the throughput over a noisy channel. We establish a theoretical model of the energy communication channel to clearly understand the channel capacity. We then devise an online rate adaptation algorithm to adjust the modulation strategy according to the channel condition. Based on the theoretical model, WiZig controls the number of encoded energy amplitudes and the length of a receiving window, so as to optimize the CTC throughput. We implement a prototype of WiZig on a software radio platform and a commercial ZigBee device. The evaluation shows that WiZig achieves a throughput of 153.85bps with less than 1% symbol error rate in a real environment.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made substantial progress, but CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables communication from ZigBee to WiFi. ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). Moreover, we extend ZigFi to multiple-to-one concurrent transmissions. We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state of the arts.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate, respiration rate, and blood pressure of users. 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 software-hardware 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. Following IRB protocols, 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 (HR), 1.79% for respiration rate (RR), 3.35% for systolic blood pressure (SBP), and 3.89% for diastolic blood pressure (DBP). MagWear can also be extended to detect anemia and blood oxygen saturation, which is also our ongoing work. Code and hardware schematics can be found at: https://github.com/tanwork/MagWear.

Wi-Fi is the de facto standard for providing wireless access to the Internet in the 2.4 GHz ISM band. Tens of billions of Wi-Fi devices (e.g., smartphones) have been shipped worldwide with limited types of wireless radios operating only when Wi-Fi connectivity is available, making it challenging to access data in heterogeneous IoT devices. However, the direct connection between Wireless Personal Area Network (WPAN) technologies, such as Bluetooth, and Wi-Fi presents challenges due to the inherent distinct physical layer. In our work, a novel communication method called BlueWi has been introduced, which serves as a cross technology communication method that enables BLE devices to establish connections and engage in communication with Wi-Fi based WPAN networks. We let BLE signals hitchhike on ongoing Wi-Fi signals, enabling Wi-Fi to recognize specific BLE signal waveforms in the frequency domain. By analyzing the decoded Wi-Fi payload, BlueWi can retrieve the BLE data, ensuring this method remains fully compatible with existing commodity Wi-Fi hardware. The direct sequence spread spectrum scheme is appended to handle general BLE frames and can be considered as “COPY” operation, which allows for better correlation and detection of the signal at the receiver. Evaluations conducted using both USRP and commodity devices have demonstrated that BlueWi can achieve concurrent wireless communication from BLE commercial chips to Wi-Fi networks with a frame reception rate exceeding 96%.

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.

Research on Cross-technology communication (CTC) has made rapid progress in recent years, but how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. We in this paper propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the process of CTC. We further design a light-weight link estimation approach to estimate C-LQI and in turn the PRR over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative error of link estimation respectively by 46% and 53% and saves the communication cost by 90%.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver’s standard. Physical-level CTC means considerable processing complexity at the transmitter, which doesn’t apply to the communication from a low-end transmitter to a high-end receiver, e.g. from ZigBee to WiFi. This paper presents transmitter-transparent cross-technology communication, which leaves the processing complexity solely at the receiver side and therefore makes a critical advance toward bidirectional high-throughput CTC. We implement our proposal as LEGO-Fi, the communication from ZigBee to WiFi. The key technique inside is cross-demapping, which stems from two key technical insights: (1) A ZigBee packet leaves distinguishable features when passing the WiFi modules. (2) Compared to ZigBee’s simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6Kbps, which is respectively 13000× and 1200× faster than FreeBee and ZigFi, the two existing ZigBee-to-WiFi CTC approaches.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.

Cross- Technology Communication (CTC) is an emerging technique to enable the direct communication among different wireless technologies. A main category of the existing proposals on CTC propose to modulate packets at the sender side, and demodulate them into 1 and 0 bits at the receiver side. The performance of those proposals is likely to degrade in a densely coexisting environment. Solely judged according to the received signal strength, a symbol 0 that is modulated as packet absence is generally indistinguishable from dynamic interference. In this paper, we propose StripComm, interference-resilient CTC in coexisting environments. A sender in StripComm adopts an interference-resilient coding scheme that contains both presence and absence of packets in one symbol. The receiver strips the interference from the interested signal by exploiting the self-similarity of StripComm signals. We prototype StripComm with commercial WiFi, ZigBee devices and a software radio platform. The theoretical and experimental evaluation demonstrate that StripComm offers a data rate up to 1.1K bps with a SER (Symbol Error Rate) lower than 0.01 and a data rate of 0.89K bps even against strong interference.

The proliferation of loT applications drives the need of ubiquitous connections among heterogeneous wireless devices. Cross-Technology Communication (CTC) is a significant technique to directly exchange information among heterogeneous devices that follow different standards. By exploiting a side-channel like frequency, amplitude or temporal modulation, the existing works enable CTC but have limited performance under channel noise. In this paper, we propose WiZig, a novel CTC technique that employs modulation techniques in both the amplitude and temporal dimensions to optimize the throughput over a noisy channel. We establish a theoretical model of the energy communication channel to clearly understand the channel capacity. We then devise an online rate adaptation algorithm to adjust the modulation strategy according to the channel condition. Based on the theoretical model, WiZig can accurately control the number of encoded energy amplitudes and the length of a receiving window, so as to optimize the CTC throughput. We implement a prototype of WiZig on a software radio platform and a commercial ZigBee device. The evaluation show that WiZig achieves a throughput of 153.85 bps with less than 1 % symbol error rate in a real environment. The results demonstrate that WiZig realizes efficient and reliable CTC under varied channel conditions.

More and more home appliances are now connected to the Internet, thus enabling various smart home applications. However, a critical problem that may impede the further development of smart home is overlooked: Small appliances account for the majority of home appliances, but they receive little attention and most of them are cut off from the Internet. To fill this gap, we propose MotorBeat, an acoustic communication approach that connects small appliances to a smart speaker. Our key idea is to exploit direct current (DC) motors, which are common components of small appliances, to transmit acoustic messages. We design a novel scheme named Variable Pulse Width Modulation (V-PWM) to drive DC motors. MotorBeat achieves the following 3C goals: (1) Comfortable to hear, (2) Compatible with multiple motor modes, and (3) Concurrent transmission. We implement MotorBeat with commercial devices and evaluate its performance on three small appliances and ten DC motors. The results show that the communication range can be up to 10 m.

We present MAF, a novel acoustic sensing approach that leverages the commodity hardware in bone conduction earphones for hand-to-face gesture interactions. Briefly, by shining audio signals with bone conduction earphones, we observe that these signals not only propagate along the surface of the human face but also dissipate into the air, creating an acoustic field that envelops the individual’s head. We conduct benchmark studies to understand how various hand-to-face gestures and human factors influence this acoustic field. Building on the insights gained from these initial studies, we then propose a deep neural network combined with signal preprocessing techniques. This combination empowers MAF to effectively detect, segment, and subsequently recognize a variety of hand-to-face gestures, whether in close contact with the face or above it. Our comprehensive evaluation based on 22 participants demonstrates that MAF achieves an average gesture recognition accuracy of 92% across ten different gestures tailored to users’ preferences.

Cross-technology interference is a major threat to the dependability of low-power wireless communications. Due to power and bandwidth asymmetries, technologies such as Wi-Fi tend to dominate the RF channel and unintentionally destroy low-power wireless communications from resource-constrained technologies such as ZigBee, leading to severe coexistence issues. To address these issues, existing schemes make ZigBee nodes individually assess the RF channel’s availability or let Wi-Fi appliances blindly reserve the medium for the transmissions of low-power devices. Without a two-way interaction between devices making use of different wireless technologies, these approaches have limited scenarios or achieve inefficient network performance. This paper presents BiCord, a bidirectional coordination scheme in which resource-constrained wireless devices such as ZigBee nodes and powerful Wi-Fi appliances coordinate their activities to increase coexistence and enhance network performance. Specifically, in BiCord, ZigBee nodes directly request channel resources from Wi-Fi devices, who then reserve the channel for ZigBee transmissions on-demand. This interaction continues until the transmission requirement of ZigBee nodes is both fulfilled and understood by Wi-Fi devices. This way, BiCord avoids unnecessary channel allocations, maximizes the availability of the spectrum, and minimizes transmission delays. We evaluate BiCord on off-the-shelf Wi-Fi and ZigBee devices, demonstrating its effectiveness experimentally. Among others, our results show that BiCord increases channel utilization by up to 50.6% and reduces the average transmission delay of ZigBee nodes by 84.2% compared to state-of-the-art approaches.

iBeacon protocol is widely deployed to provide location-based services. By receiving its BLE advertisements, nearby devices can estimate the proximity to the iBeacon or calculate indoor positions. However, the open nature of these advertisements brings vulnerability to impersonation attacks. Such attacks could lead to spam, unreliable positioning, and even security breaches. In this paper, we propose Wi-attack, revealing the feasibility of using WiFi devices to conduct impersonation attacks on iBeacon services. Different from impersonation attacks using BLE compatible hardware, Wi-attack is not restricted by broadcasting intervals and is able to impersonate multiple iBeacons at the same time. Effective attacks can be launched on iBeacon services without modifications to WiFi hardware or firmware. To enable direct communication from WiFi to BLE, we use the digital emulation technique of cross technology communication. To enhance the packet reception along with its stability, we add redundant packets to eliminate cyclic prefix error entirely. The emulation provides an iBeacon packet reception rate up to 66.2%. We conduct attacks on three iBeacon services scenarios, point deployment, multilateration, and fingerprint-based localization. The evaluation results show that Wi-attack can bring an average distance error of more than 20 meters on fingerprint-based localization using only 3 APs.

Cross-Technology Communication (CTC) is an emerging technology to support direct communication between wireless devices that follow different standards. In spite of the many different proposals from the community to enable CTC, the performance aspect of CTC is an equally important problem but has seldom been studied before. We find this problem is extremely challenging, due to the following reasons: on one hand, a link for CTC is essentially different from a conventional wireless link. The conventional link indicators like RSSI (received signal strength indicator) and SNR (signal to noise ratio) cannot be used to directly characterize a CTC link. On the other hand, the indirect indicators like PER (packet error rate), which is adopted by many existing CTC proposals, cannot capture the short-term link behavior. As a result, the existing CTC proposals fail to keep reliable performance under dynamic channel conditions. In order to address the above challenge, we in this paper propose AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels. Instead of reactively adjusting the CTC sender, AdaComm adopts online learning mechanism to adaptively adjust the decoding model at the CTC receiver. The self-adaptive decoding model automatically learns the effective features directly from the raw received signals that are embedded with the current channel state. With the lossless channel information, AdaComm further adopts the fine tuning and full training modes to cope with the continuous and abrupt channel dynamics. We implement AdaComm and integrate it with two existing CTC approaches that respectively employ CSI (channel state information) and RSSI as the information carrier. The evaluation results demonstrate that AdaComm can significantly reduce the SER (symbol error rate) by 72.9% and 49.2%, respectively, compared with the existing approaches.

Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410\texttimes in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.

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.

Research on cross-technology communication (CTC) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature (IQ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate (PRR) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

Opportunistic forwarding seizes early forwarding opportunities in duty-cycled networks to reduce delay and energy consumption. But increasingly serious Cross-Technology Interference (CTI) significantly counteracts the benefits of opportunistic forwarding. Existing solutions try to reserve the channel for low-power networks by implicit avoidance or explicit coordination but ignore the potential of high-power CTI’s superior capability. In this paper, we propose a new paradigm for low-power opportunistic forwarding in CTI environments. Instead of keeping high-power CTI devices silent, we directly involve them into the forwarding, as cross-technology forwarders. We design Portal to solve the challenges of realizing cross-technology opportunistic forwarding. To be transparent to the low-power networks, Portal adopts cross-technology rebroadcasting to enable the fast overhearing and forwarding of cross-technology data. To maximize the performance gain of using heterogeneous forwarders while minimizing the influence on legacy high-power traffic, we propose a post-forwarding forwarder selection and a traffic scheduling method. We also propose a feature-based ACK recognition method and a jamming-based ACK replying mechanism to forward the unreliable ACKs from asymmetric regions. Extensive experiments demonstrate that Portal not only avoids the CTI but also breaks through the existing performance limit.

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.

Clock synchronization is a key function in embedded wireless systems and networks. This issue is equally important and more challenging in IoT systems nowadays, which often include heterogeneous wireless devices that follow different wireless standards. Conventional solutions to this problem employ gateway-based indirect synchronization, which suffer low accuracy. This paper for the first time studies the problem of cross-technology clock synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices by direct cross-technology communication. Crocs decouples the synchronization signal from the transmission of a timestamp. By incorporating a barker-code based beacon for time alignment and cross-technology transmission of timestamps, Crocs achieves robust and accurate synchronization among WiFi and ZigBee devices, with the synchronization error lower than 1 millisecond. We further make attempts to implement different cross-technology communication methods in Crocs and provide insight findings with regard to the achievable accuracy and expected overhead.

The application of Wireless Sensor Networks (WSNs) often falls into unexpected poor performance conditions due to many factors such as complex network interactions, software bugs and incorrect configurations. Diagnosing such a network is challenging since it is difficult to obtain information from the network due to factors including (1) non-deterministic network interactions among motes, (2) difficulties in reconstructing the status of each individual mote, and (3) unavailability of the real environment information. To address these problems, we propose a diagnosis tool called ALog which analyzes the local logs and the source code to infer what happens in network. Based on the analysis, we further derive the states and possible problems accordingly. We implement ALog and evaluate its efficiency with two real case studies. The results demonstrate that ALog is accurate and applicable for diagnosing real sensor networks.

Federated learning (FL) is a machine learning technique that allows for on-site data collection and processing without sacrificing data privacy and transmission. Heterogeneity is a key challenge in federated settings. Recently, cross-technology communication (CTC) has emerged as a solution for Internet of Things (IoT) heterogeneity, enabling direct communication between different wireless devices without the need for hardware modifications or gateway intervention. For example, a sophisticated WiFi device can serve as a central coordinator for other heterogeneous devices, such as LoRa, ZigBee, Bluetooth, and LTE, leading to more efficient and ubiquitous cross-network information exchange. However, heterogeneous wireless technologies present different data transmission rates and computing resources, making it difficult to achieve high accuracy in predictions due to large amounts of multidimensional data, communication delays, transmission latency, limited processing capacity, and data privacy concerns. In this work, we propose an FL framework based on CTC for heterogeneous IoT applications, called FLCTC. To demonstrate the usability of FLCTC, we implemented FLCTC and a specific solution for forest fire prediction. FLCTC was concretely implemented as a federal deep learning based on long and short-term memory and used for forest fire prediction, addressing the challenge of data characterization in heterogeneous IoT networks. FLCTC promises to improve communication efficiency and prediction accuracy. Our platform-based evaluation results show that FLCTC is feasible, with a recall of 96% and an accuracy of 88%, offering valuable insights into the use of FL with CTC for heterogeneous IoT applications.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver’s standard. Physical-level CTC means considerable processing complexity at the transmitter, which does not apply to the communication from a low-end transmitter to a high-end receiver. This article proposes LEGO-Fi, which supports the CTC from ZigBee to WiFi and Bluetooth to WiFi. LEGO-Fi is a transmitter-transparent CTC technique, which leaves the processing complexity solely at the receiver side and therefore, makes a critical advance toward bidirectional high-throughput CTC. The key technique inside is cross-demapping, which stems from two key technical insights: 1) a ZigBee/Bluetooth packet leaves distinguishable features when passing the WiFi modules and 2) compared to ZigBee’s/Bluetooth’s simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee/Bluetooth packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6 Kb/s in practice, which is, respectively, 13000×, 1200×, and 7.5× faster than FreeBee, ZigFi, and SymBee, the three existing ZigBee-to-WiFi CTC approaches. For the CTC from Bluetooth to WiFi, LEGO-Fi achieves a throughput of 904.1 Kb/s.

The proliferation of Internet of Things (IoT) applications brings about the increasingly dense deployments of various wireless devices (e.g., Wi-Fi, ZigBee, Bluetooth, LoRa, etc.). The coexistence of heterogeneous wireless devices puts forward more stringent requirements for the adaptability and flexibility of the backscatter design [1]. In order to integrate seamlessly into heterogeneous wireless networks, the backscatter radio should be able to interplay directly with different technologies while maintaining ultra-low power consumption [2].