This paper proposes a novel address mapping mechanism for multi-robot communication and collaboration systems, named SPing. SPing addresses each robot with a dynamicphysical-world address- an encoding of the robot's physical location - rather than a pre-assigned digital-world ID (e.g., the IP address). This enables a “see-and-point” communication mode for robots: a robot can establish an immediate connection pointing to any other robot it intends to collaborate with in its visual field, without relying on a pre-existing multi-robot network. This on one hand improves the robustness and usefulness of multi-robot systems in uncertain and unstructured environments where network infrastructures are unavailable. On the other hand, it makes the robots' communication behavior tightly coupled with and more supportive of the collaboration tasks in the physical world. We build an end-to-end prototype of SPing and evaluate its performance in both static and mobile scenarios. The results show that SPing can always establish a connection precisely pointing to the target receiver with an average matching rate of 99.58%, and a spatial resolution of 0.3 m$\sim$0.5m.

This paper tries to answer a question: “Can we achieve spatial-selective transmission on IoT devices?” A positive answer would enable more secure data transmission among IoT devices. The challenge, however, is how to manipulate signal propagation without relying on beamforming antenna arrays which are usually unavailable on low-end IoT devices. We give an affirmative answer by introducing SpotSound, a novel acoustic communication system that exploits the diversity of multi-path indoors as a naturalbeamformer. By judiciously controlling the way how the information is embedded into the signal, SpotSound can make the signal decodable only when the signal propagates along a certain multipath channel. Since the multipath channel decorrelates rapidly over the distance between receivers, SpotSound can ensure the signal is decodable only at the target position, achieving precise physical isolation. SpotSound is a purely software-based solution that can run on most IoT devices where speakers and microphones are widely used. We implement SpotSound on Raspberry Pi connected with COTS microphone and speaker. Experimental results show that SpotSound could precisely focus its signal on spots with customized sizes ranging from 0.04m2to 0.5m2.

This paper presents μTag, an ultra-low-power backscatter sensor that supports high-rate sensing of a large number of targets simultaneously. The core of μTag is an RF “gene editing” technique that embeds both the identity of the sensor and the real-time motion or vibration state of the attached target intensively in the transient features of the sensor’s RF signal, in a collision-resilient manner. We provide practical techniques which i) generate such “genetic signal” with purely analog and extremely simple circuits; and ii) separate the signals from a large scale of sensors reliably. Our experimental results show that our design can support concurrent tracking of 150 targets with a 12 kHz per-tag sampling rate. We also demonstrate with multiple sensing applications that μTag can achieve high-speed and large-scale motion tracking, rotation frequency sensing, and contactless sensing. The PCB power consumption of μTag is 38∼107 μW, according to the operating frequency of the tag. Our ASIC simulation based on the 40 nm CMOS process shows that the power consumption can be further reduced to 0.13∼0.52 μW.

Parallelizing passive Radio Frequency Identification (RFID) reading is an arguably crucial, yet unsolved challenge in modern IoT applications. Existing approaches remain limited to time-division operations and fail to read multiple tags simultaneously. In this paper, we introduce QuinID, the first frequency-division multiple access (FDMA) RFID system to achieve fully parallel reading. We innovatively exploit the frequency selectivity of the tag antenna rather than a conventional digital FDMA, bypassing the power and circuitry constraint of RFID tags. Specifically, we delicately design the frequency-selective antenna based on surface acoustic wave (SAW) components to achieve extreme narrow-band response, so that QuinID tags (i.e., QuinTags) operate exclusively within their designated frequency bands. By carefully designing the matching network and canceling various interference, a customized QuinReader communicates simultaneously with multiple QuinTags across distinct bands. QuinID maintains high compatibility with commercial RFID systems and presents a tag cost of less than 10 cents. We implement a 5-band QuinID system and evaluate its performance under various settings. The results demonstrate a fivefold increase in read rate, reaching up to 5000 reads per second.

The Wi-Fi-enabled ultra-low power communication system exhibits high asymmetry between uplink and downlink speeds. The uplink can reach up to 1 Mbps, while the downlink throughput is around 100 Kbps. In this paper, we present Wook, a novel high throughput downlink system to empower Commercial Off-The-Shelf (COTS) Wi-Fi devices to transmit high-speed OOK messages. The key innovation underpinning Wook is its ability to achieve sub-symbol level modulation, allowing a single OFDM symbol to carry multiple OOK bits. This is done by profoundly exploring the Wi-Fi PHY layer and identifying optimal input payload to achieve fine-grained Wi-Fi waveform manipulation. We fabricate a PCB prototype and employ the COTS Wi-Fi router to implement the entire system. Experimental results show that with a simulated IC power consumption 76.6μW, Wook achieves a data rate of up to 1.1 Mbps, an 8.9X improvement over state-of-the-art systems. Moreover, even at a communication distance of 95 m, Wook maintains a throughput of 82.9 Kbps.

This paper presents FaB, a frequency-agile backscatter system that can optionally leverage OFDM signals on different bands as carriers for backscatter communication. Compared with existing backscatter systems that are tailored to a specific frequency band, a frequency-agile backscatter yields two critical benefits: i) it can leverage the increased availability of “free rides” across a broad range of frequency band to improve its transmission efficiency; and ii) it becomes compatible with mainstream wireless communication standards, making it applicable to heterogeneous wireless networks. Based on these two features, FaB’s circuits can be migrated to various types of backscatter communication nodes without any modification, significantly reducing design and deployment costs. To show the efficacy of our design, we implement a PCB prototype of FaB and showcase its capability of leveraging OFDM Wi-Fi and LTE signals as carrier waves. Our extensive field studies show that FaB’s multi-band modulator can produce an error vector magnitude of under -15dB in any band below 6GHz with a precision of 10mV.

LoRa backscatter achieves long-range communication at the cost of only tens of micro-watts of power when implemented in integrated circuits (ICs), which makes it a potential enabler for massive IoT. However, despite the above advantages, we find that the current tag design wastes approximately 72% of the chip functional area due to the use of large-size inductors in the impedance loads array. This inefficiency significantly increases the cost per chip during mass production. To address this die area issue, we propose OsTAG, a novel LoRa backscatter design that eliminates all inductors in the impedance loads array while maintaining the same quantization resolution. The key innovation lies in the creation of virtual impedance using oversampling. Realizing such design requires overcoming the technical challenges of mitigating approximation error and managing oversampling clock imperfections. To validate our design, we develop prototype and an IC, demonstrating promising results in terms of both performance and efficiency. OsTAG is expected to cost only 28% of the existing chip area while achieving comparable hundred-meter level communication range.

This paper presents μTag, an ultra-low-power backscatter sensor that supports high-frequency sensing of a large number of targets simultaneously. The core of μTag is an RF "gene editing" technique that embeds both the identity of the sensor and the real-time motion state of the attached target intensively in the transient features of the sensor's RF signal, in a collision-resilient manner. We provide practical techniques which i) generate such "genetic signal" with purely analog and extremely simple circuits; and ii) separate the signals from a large scale of sensors reliably. Our experimental results show that our design can support concurrent tracking of 150 targets with a 12kHz per-tag sampling rate. We also demonstrate with multiple sensing applications that μTag can achieve high-speed and large-scale motion tracking and rotation frequency sensing. The PCB power consumption of μTag is 38~107μW, according to the operating frequency of the tag. Our ASIC simulation based on the 40nm CMOS process shows that the power consumption can be further reduced to 0.13~0.52μW.

This paper presents FaB, a frequency-agile backscatter system that can optionally leverage OFDM signals on different bands as carriers for backscatter communication. Compared with existing backscatter systems that are tailored to a specific frequency band, a frequency-agile backscatter yields two critical benefits: i) it can leverage the increased availability of "free rides" across a broad range of frequency band to improve its transmission efficiency; and ii) it becomes compatible with mainstream wireless communication standards, making it applicable to heterogeneous wireless networks. Based on these two features, FaB's circuits can be migrated to various types of backscatter communication nodes without any modification, significantly reducing design and deployment costs. To show the efficacy of our design, we implement a PCB prototype of FaB and showcase its capability of leveraging OFDM Wi-Fi and LTE signals as carrier waves. Our extensive field studies show that FaB's multi-band modulator can produce an error vector magnitude of under -15dB in any band below 6GHz with a precision of 10mV.

This paper presents the design, implementation, and evaluation of TaGroup, a fast, fine-grained, and robust grouping technique for RFIDs. It can achieve a nearly 100% accuracy in distinguishing multiple groups of closely located RFIDs, within only a few seconds. It would benefit many inventory tracking applications, such as self-checkout in retails and packaging quality control in logistics. We make two technical innovations. First, we propose a novel method which can measure the channels between multiple pairs of commercial RFID tags simultaneously, and then estimate the proximity relations between them based on the channel information. Second, we introduce a spatio-temporal graph model which captures a full picture of proximity relations among all the tags, based on which TaGroup can perform a robust grouping of the tags. These two designs together boost the grouping speed and accuracy of TaGroup. Our experiments show that in grouping 120 tags into 4 closely located groups, TaGroup can achieve a nearly 100% accuracy, at the cost of only 2 seconds.

This paper presents dual-band Wi-Fi backscatter (DBscatter), which is the first system supporting 2.4GHz and 5GHz Wi-Fi backscatter simultaneously in a single tag. Our key insight is that most existing Wi-Fi devices communicate in the clean 5GHz band. The 5GHz band provides more chances for ”free riding” with less interference, while the 2.4GHz band presents better NLoS performance. DBscatter combines the strengths of the existing 2.4GHz band with the unexplored 5GHz backscatter in a unified design, developing a robust and high-throughput ambient Wi-Fi backscatter system. We make the following technical contributions: (1) We design a dual-band RF frontend to support dual-band Wi-Fi signals. (2) We propose a tag data demodulation algorithm, which merges the common phase error in multi-antenna received signals, improving the tag transmission reliability while reducing the number of required receivers. (3) We build a prototype of DBscatter system using COTS FPGAs and SDRs. Compared to TiScatter and FreeRider, DBscatter boosts Wi-Fi backscatter throughput by 3.74X and 7.35X, and energy efficiency by 1.78X and 1.38X respectively.

Along with the proliferation of IoT devices, people have a lot of concerns on the privacy issues brought by them. Existing solutions, employing encryption or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. To address this issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on a fine-grained control of signal’s wave shape, Rustle generates a series of mutually uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper’s error rate on detecting the signal is higher than 80%.

We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee’s unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots. The idea of TiSee is to stick an RFID tag on the robot (or its gripper) and use it as a moving “antenna” to locate the tags on the target. The core of this design is a novel technique which can build a “channel” between two commercial RFID tags. Such an inter-tag channel is proved to be highly sensitive to the change in inter-tag distance and is resistant to multipath. By leveraging this channel and the mobility of the robot, we emulate an antenna array and use it for fine-grained localization and orientation estimation. Our experiments show that TiSee achieves a median accuracy of 9.5mm and 3.1° in 3D localization and orientation estimation. TiSee brings an eye-in-hand “camera” to miniature robots, supporting them to perform agile tasks in dark, cluttered, and occluded settings.

This paper tries to answer a question: "Can we achieve spatial-selective transmission on IoT devices?" A positive answer would enable more secure data transmission among IoT devices. The challenge, however, is how to manipulate signal propagation without relying on beamforming antenna arrays which are usually unavailable on low-end IoT devices.

This paper presents the design, implementation, and evaluation of TaGroup, a fast, fine-grained, and robust grouping technique for RFIDs. It can achieve a nearly 100% accuracy in distinguishing multiple groups of closely located RFIDs, within only a few seconds. It would benefit many inventory tracking applications, such as self-checkout in retails and packaging quality control in logistics.

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.

Reconfigurable reflective arrays can be used to program the radio propagation environment in order to form favorable wireless channel conditions. Previous designs have used large-scale arrays containing hundreds to thousands of reflecting elements located external to the receiving node, with the reflection coefficients of all array elements managed by a controller. However, these designs can be costly to deploy and are challenging to quickly adapt to the time-varying nature of wireless channels caused by mobility.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric calledVibration Signal-to-Noise Ratio(VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept ofMulti-Signal Consolidation(MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves$3.946\%$relative amplitude error and$0.02487\%$relative frequency error in median. Typically, the average amplitude error is only$3.174um$when measuring the$100um$-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by$69.21\%$and$97.99\%$respectively.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to any one of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of$\text{7.61}~cm$, a mean orientation error of 9.83 degrees, a mean tracking error of$\text{6.12}~cm$, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with “useful” saliency information, sensing the user's head movement, performs opportunistic calibration using only those “useful” frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm (2.884$^{\circ }$) which decreases to 0.99cm (1.891$^{\circ }$) with historical information and 0.57cm (1.089$^{\circ }$) with an indicator.

Secure Device-to-Device (D2D) communication is important for the Internet-of-Things (IoT) devices. Key agreement between devices is the important first step in building a secure D2D channel. Due to the lack of third-party certification, key agreement for IoT devices has to rely on the untrusted channel between them, which makes the pairing process vulnerable to attacks such as eavesdropping, jamming, and predictable channel attack. We solve this problem with a novel key agreement method named EchoKey, where two nearby devices can generate a symmetric key independently, leveraging the ambient sound signal that can be locally collected by devices. The intuition of this idea is that the propagation delays of the ambient sounds and the echoes will carry fine-grained information about the spatial context of the receiving device, which can be transformed to a special key for that device. So, nearby devices which have similar spatial context will generate similar echo profiles. We implement a prototype of EchoKey and evaluate its performance in resisting different attacks. The results tell that, with EchoKey, the key agreement process is even undetectable by an attacker which is only 50cm away from the pairing devices, and is thus resistant to all attacks mentioned above.

Along with the proliferation of Internet of Things (IoT) devices, people have a lot of concerns on the privacy issues brought by those devices. Existing solutions, employing strong encryption al-gorithms or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. In order to address the issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise signal. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on the weak transitivity of signal's correlation and a fine-grained control of signal's wave shape, Rustle generates a series of mutu-ally uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper's error rate on detecting the signal is higher than 80%.

Secure Device-to-Device (D2D) communication is important for the Internet-of-Things (IoT) devices. Key agreement between devices is the important first step in building a secure D2D channel. Due to the lack of third-party certification, key agreement for IoT devices has to rely on the untrusted channel between them, which makes the process vulnerable to attacks such as eavesdropping, jamming, and predictable channel attack. We solve this problem with a novel key agreement method named EchoKey, where two nearby devices can generate symmetric key independently, leveraging the ambient sound signal that can be locally collected by devices. The intuition of this idea is that the propagation delays of the ambient sounds and its echoes will carry fine-grained information about the spatial context of the receiving device, which can be transformed to a special key for that device. So, nearby devices which have similar spatial context will generate similar keys. We implement a prototype of EchoKey and evaluate its performance in resisting different attacks. The results tell that, with EchoKey, the key agreement process is even undetectable by an attacker which is only 50cm away from the pairing devices, and is thus resistant to all attacks mentioned above.

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%.

In this paper, we for the first time show how to enable software-defined PHY (SD-PHY) to achieve agile reprogrammability in wireless backscatter networks. This can facilitate innovations in this field by relieving researchers from unnecessary engineering work. With SD-PHY, the tag's PHY-layer behavior can be neatly defined by configuring a set of parameters, which allows the common hardware to generate backscattered signals complying with various wireless protocols. The SD-PHY architecture is based on the key insight that the tag's PHY-layer behavior is essentially determined by reflection coefficient sequence.

We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee's unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots.

OFDMA downlink design allowing parallel processing OFDM subcarriers is adopted by a number of commercial wireless standards such as LTE, 5G, and 802.11ax. However, the widespread adoption of OFDMA downlink on low-end IoT devices is stymied due to the existing digital receiver framework's ≈100mW power consumption, which is mainly incurred by LO+mixer, ADC, and complex digital processing. In this paper, we present an ultra-low-power OFDMA downlink demodulation design, which achieves ≈100 μW receiving power. Our basic idea is to transform the current digital demodulation approach into the analog one based on filtering, which avoids those power-hungry components. We achieve this by proposing a series of novel RF front-end hardware designs: 1) a μW-level two-stage mixing scheme that enables adjustable and precise subcarrier filtering, 2) a quartz crystal-based filter circuit incurring negligible insertion loss, and 3) a passive phase-to-envelope conversion technique enabling low-power non-coherent phase demodulation. We build a prototype to verify the proposed schemes. Experimental and IC simulation results show that: our new design can achieve 130 -- 1500 times power savings depending on the number of subcarriers that need to be processed in parallel, compared with the traditional all-digital design.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to anyone of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of 7.61 cm, a mean orientation error of 9.83 degrees, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

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.

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, implicit saliency-aware calibration for continuous gaze tracking on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, and performs opportunistic calibration using only those "useful" frames. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm.

Recently, mmWave has been widely used in fine-grained sensing applications due to its short wavelength and large bandwidth. One mmWave device usually can measure the target's 1D micro-displacement along the line-of-sight (LOS) direction. In this work, we try to empower mmWave with the capability of measuring 2D micro-displacements. Our insight is that although the mmWave reflection from one path contains only 1D observation, the spatial separability of mmWave offers an opportunity to separate multipath reflections from the received signal. Combining the coherent observations from multipath reflections can restore the 2D orbit of the target. Based on this insight, we present GWaltz, a mmWave sensing system that manages to measure sub-mm-level 2D orbits of rotating machinery. In GWaltz, we first reveal the relationship between the rotor's movement and the observed ghost multipath reflections (GMRs) and then design a set of novel signal processing techniques to restore the rotor orbit from the poor-quality GMR signals. We implement GWaltz with a commercial mmWave radar, and our evaluation results show that it achieves an absolute error of about 8.42um when measuring 100um-diameter rotor orbits.

Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. The design of RED further addresses practical issues, such as parameterizing the RUM model, making it robust to dynamic and noisy environments, and considering how the doppler shift may affect our system. We implement RED with COTS RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.6 percent and the detection latency is 0.68 seconds in average.

Operating in unlicensed ISM bands, ZigBee devices often yield poor performance due to the interference from ever increasing wireless devices in the 2.4 GHz band. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to quantify and trace the relationship between interference and link qualities of the sender's outbound links. With such a link model, Smoggy-Link can translate low-cost interference information to the fine-grained spatiotemporal link state. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferers.

As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal's moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves 2 ~ 4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson's Chi-Square test to extract the collided signals' combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is 11.7× higher than that of the state-of-the-art approach.

Acoustic signal acts as an essential input to many systems. However, the pure acoustic signal is very difficult to extract, especially in noisy environments. Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. To realize such a system, we further address the fundamental challenge in distributed beamforming: aligning the signals captured by distributed and unsynchronized microphones. We implement ChordMics and evaluate its performance under both LOS and NLOS scenarios. The evaluation results tell that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB.

As the proliferation of backscatter-based applications, exploiting backscatter-based sensing becomes more important. Due to the requirement of accurate estimation of backscatter channels (phase and amplitude), which is often distorted when multiple signals collide with each other, existing works are generally limited to either parallel decoding of collided signals or with non-collided signals only. Motivated by our observation that a channel can be distorted during collisions, the movements of the ON-OFF Keying modulated signal still preserve channel properties of the respective tags, we propose the first approach to channel estimation of parallel 2backscattered signals, called Fireworks. We model the relationship between the channel and the signal moving trajectory in the In-phase and Quadrature (IQ) domain and implement this design in our lab. The results show that Fireworks is able to estimate up to five channels in parallel. When applied to the tracking application, Fireworks achieves 2~4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect the health and indicate anomalies of the objects. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. By introducing a Multi-Signal Consolidation (MSC) model to describe the properties of the reflected signals, we exploit the inherent consistency among those signals to accurately recover the vibration characteristics. We implement a prototype of mmVib, and the experiments show that this design achieves 8.2% relative amplitude error and 0.5% relative frequency error in median. Typically, the median amplitude error is 3.4um for the 100um-amplitude vibration. Compared to two existing approaches, mmVib reduces the 80th-percentile amplitude error by 62.9% and 68.9% respectively.

Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable and, hence, may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight; although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then, FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mb/s, which is 6× higher than the state of the art.

Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for improving the overall throughput by enabling concurrent transmissions of the backscattering tags. The state-of-the-art approaches for parallel backscatter assume that all the states of the collided signals are distinguishable in the In-phase and Quadrature (IQ) signal plane. In this paper, we disclose the superclustering phenomenon that makes the assumption untenable and significantly degrades the overall performance. Moreover, we observe that the indistinguishable states at different channels are not the same due to the intrinsic channel diversity. Motivated by the observation, we propose Canon, an approach that exploits the channel diversity of the backscatter tags for reliable parallel decoding. In Canon, we address two critical challenges: (i) designing the Multi-Carrier Backscatter (MCB) module to extract the collided signals simultaneously from multiple channels, (ii) designing the Multi-Channel Cluster Union (MCCU) algorithm to distinguish each state of the collided signals. The experiments demonstrate that Canon can achieve over 10 times higher throughput than the state-of-the-art approaches.

Eccentricity detection is a crucial issue for highspeed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and realtime RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. We implement RED with commercial-of-the-shelf RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.59% and the detection latency is 0.68 seconds in average.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson's Chi-Square test to extract the collided signals' combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is $11.7\times$ higher than that of the state-of-the-art approach.

The development of Internet-of-Things (IoT) technologies in recent years brings us unprecedented opportunities for innovations in the power industry. This demo abstract introduces our research and practice with Pavatar - IoT for the power industry. Pavatar includes a series of system deployments in the core sections of Global Energy Internet (GEI), for the purposes of automatic surveillance and remote diagnosis of ultra-high-voltage converter stations (UHVCSs). Pavatar incorporates technologies like lower-power or battery-free sensing, cross-technology communication, edge computing, machine learning, and enhances the user experience with 3D virtual reality. The deployed system significantly reduces the manpower cost and enhances the operational efficiency of the UHVCS.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property and privacy. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior andhoweach motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property, privacy, and public security. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion, instead of the entire motion, to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov Chain based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable, and hence may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight: although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals, and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mbps, which is 6x higher than the state-of-the-art.

Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.

The low-cost crystal oscillators embedded in wireless sensor nodes are prone to be affected by their working condition, leading to undesired variation of clock skew. To preserve synchronized clocks, nodes have to undergo frequent re-synchronization to cope with the time-varying clock skew, which in turn means excessive energy consumption. In this paper, we propose DualSync, a synchronization approach for low-power wireless networks under dynamic working condition. By utilizing time-stamp exchanges and local measurement of temperature and voltage, DualSync maintains an accurate clock model to closely trace the relationship between clock skew and the influencing factors. We further incorporate an error-driven mechanism to facilitate interplay between Inter-Sync and Self-Sync, so as to preserve high synchronization accuracy while minimizing communication cost. We evaluate the performance of DualSync across various scenarios and compare it with state-of-art approaches. The experimental results illustrate the superior performance of DualSync in terms of both accuracy and energy efficiency.

Intermittent connection of wireless links, caused by low duty-cycle radio operation, harsh working environment, movement of sensor nodes, etc., makes clock synchronization a challenging task. Prior synchronization approaches in wireless sensor networks (WSNs) typically require that nodes exchange time messages frequently with the reference clock, which is difficult in networks with low or intermittent connectivity. This poster presents RobSync, a robust design for clock synchronization in intermittent-connected wireless networks. Having recognized that clock skew is highly correlated to the voltage supply, we use the local voltage information as a reference for clock self-calibration, which helps reduce the frequency of time-stamp exchanges. To prevent a misuse of the voltage information, leading to error accumulation, a re-synchronization interval adjustment design is developed to make a trade-off between accuracy and energy consumption. We present the theory behind RobSync, and provide preliminary results by experiments to compare our approach and the recent approach.

In this paper, we propose a novel clock calibration approach, which addresses two key challenges for clock calibration in Wireless Sensor Networks: excessive communication overhead and the trade-off between accuracy and cost. To achieve this, our approach leverages the fact that the clock skew is highly correlated to temperature, which can serve as both an assistant for clock skew estimation and a regulatory factor for the duty-cycled design. Our approach is one order of magnitude more power-efficient than communication based approaches since the calibration largely relies on local temperature information. In addition, our approach provides a nice feature of self-adaptive period, which can substantially promote the system flexibility. We present the theory behind our approach, and provide preliminary results of a simulated comparison of our approach and some recent approaches.

This poster presents GuideLoc, a highly efficient aerial wireless localization system that uses directional antennas mounted on a mini Multi-rotor Unmanned Aerial Vehicle (UAV), to enable detecting and positioning of targets. Taking advantage of angle and signal strength information of frames transmitted from targets, GuideLoc can directly fly towards the targets with the minimum flight route and time. We implement a prototype of GuideLoc using ArduCopter and evaluate the performance by simulations and experiments. Experimental results show that GuideLoc achieves an average location accuracy of 2.7 meters and reduces flight distance more than 50% compared with other known wireless localization approaches using UAV.