Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome the traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in Wi-Fi-based localization by 80% (median & 90^{th} the percentile) across the 2000 sq. ft. spanning two different spaces.

Instant delivery is an important urban service in recent years because of the increasing demand. An important issue for delivery platforms is to keep updating the status of couriers especially the real-time locations, which is challenging when they are in an indoor environment. We argue the previous indoor localization techniques cannot be applied in the instant delivery scenario because they require extra deployed infrastructures and extensive labor work. In this work, we perform the courier's indoor localization transparently without extra actions of couriers by existing data from the platform including order progress reports and couriers' trajectories. Specifically, we present TransLoc to localize couriers by addressing two challenges including uncertain reporting behaviors and uncertain indoor mobility behaviors. Our key idea lies in two insights (i) couriers' behaviors are consistent in indoor/outdoor environments; (ii) localization, as a spatial inference problem, could be converted to a temporal inference problem. We evaluate our system on 565 couriers from an instant delivery company, which shows improvement over two baselines by 64% and 72%, and achieves a competitive result compared to a label-extensive baseline. As a case study, we apply TransLoc to optimize the order dispatching strategy, which reduces the delivery time by 24%.

Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, energy efficiency, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of carefully deployed PIR sensors. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these data-driven approaches require to collect abundant training data and suffer from environmental change. In this paper, we propose PIRATES, a PIR-based device-free localization system based on the raw data of PIR sensors. The key of PIRATES is to extract a new type of location information named the azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrated that PIRATES can achieve higher location accuracy than the state-of-the-art approaches. In addition, the information of the azimuth change can be easily incorporated with other information of PIR signals to improve the localization accuracy.

Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity - they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.

Global Positioning System is a widely adopted localization technique. With the increasing demand for small satellites, the need for a low-power GPS for satellites is also increasing. To enable many state-of-the-art applications, the exact position of the satellites is necessary. However, building low-power GPS receivers which operate in low earth orbit pose significant challenges. This is mainly due to the high speed (~7.8km/s) of small satellites. While duty-cycling the receiver is a possible solution, the high relative Doppler shift between the GPS satellites and the small satellite contributes to the increase in Time To First Fix (TTFF), thus increasing the energy consumption. Further, if the GPS receiver is tumbling along with the small satellite on which it is mounted, longer TTFF may lead to no GPS fix due to disorientation of the receiver antenna. In this paper, we elucidate the design of a low-cost, low-power GPS receiver for small satellite applications. We also propose an energy optimization algorithm called F3 to improve the TTFF which is the main contributor to the energy consumption during cold start. With simulations and in-orbit evaluation from a launched nanosatellite with our μGPS and high-end GPS simulators, we show that up to 96.16% of energy savings (consuming only ~ 1/25th energy compared to the state of the art) can be achieved using our algorithm without compromising much (~10m) on the navigation accuracy. The TTFF achieved is at most 33s.

Electric toothbrushes are widely used for home oral care, but many users do not achieve desired hygiene results due to insufficient brushing coverage or incorrect brushing techniques. Existing electric toothbrushing monitoring systems fail to detect these issues because they cannot achieve fine-grained position tracking. In this paper, we present a novel electric toothbrushing monitoring system called MET that tracks brushing coverage for all the 15 surfaces of teeth and detects different types of incorrect brushing techniques. This design is inspired by our observation that the motor inside an electric toothbrush generates a unique magnetic field, which can serve as a reliable signal for position and orientation tracking. MET is the first system that tracks both the position and orientation of an unmodified electric motor using magnetic inductive sensing. Experiments with fourteen users show that the average toothbrushing surface recognition accuracy of MET is 85.3%. Moreover, MET is robust to user location changes and posture variations and does not require any training from the users. Experimental results also demonstrate our significant advantages over existing commercial systems.

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer.In-N-Out can continuously charge the medical implant residing in deep tissues with consistently near-optimal power, even when the implant moves around inside the human body. To accomplish this, we exploit the structure of a distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile,we can keep radiation exposure levels on other body parts very low, reducing the risk of overheating. We prototype In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Through extensive experimentation, we show that In-N-Out achieves 0.37 mW average charging power in-side a 10 cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices from outside the body. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4×–18.1×power gain when the implant is stationary, and 5.3×–7.4×power gain when the implant is in motion.

We present the design, implementation, and evaluation of μmedIC, a fully-integrated wireless and batteryless micro-implanted sensor. The sensor powers up by harvesting energy from RF signals and communicates at near-zero power via backscatter. In contrast to prior designs which cannot operate across various in-body environments, our sensor can self-reconfigure to adapt to different tissues and channel conditions. This adaptation is made possible by two key innovations: a reprogrammable antenna that can tune its energy harvesting resonance to surrounding tissues, and a backscatter rate adaptation protocol that closes the feedback loop by tracking circuit-level sensor hints. We built our design on millimeter-sized integrated chips and flexible antenna substrates, and tested it in environments that span both in-vitro (fluids) and ex-vivo (tissues) conditions. Our evaluation demonstrates μmedIC’s ability to tune its energy harvesting resonance by more than 200 MHz (i.e., adapt to different tissues) and to scale its bitrate by an order of magnitude up to 6Mbps, allowing it to support higher data rate applications (such as streaming low-res images) without sacrificing availability. This rate adaptation also allows μmedIC to scale its energy consumption by an order of magnitude down to 350 nanoWatts. These capabilities pave way for a new generation of networked micro-implants that can adapt to complex and time-varying in-body environments.

Voice assistants such as Amazon Echo (Alexa) and Google Home use microphone arrays to estimate the angle of arrival (AoA) of the human voice. This paper focuses on adding user localization as a new capability to voice assistants. For any voice command, we desire Alexa to be able to localize the user inside the home. The core challenge is two-fold: (1) accurately estimating the AoAs of multipath echoes without the knowledge of the source signal, and (2) tracing back these AoAs to reverse triangulate the user’s location. We develop VoLoc, a system that proposes an iterative align-and-cancel algorithm for improved multipath AoA estimation, followed by an error-minimization technique to estimate the geometry of a nearby wall reflection. The AoAs and geometric parameters of the nearby wall are then fused to reveal the user’s location. Under modest assumptions, we report localization accuracy of 0.44 m across different rooms, clutter, and user/microphone locations. VoLoc runs in near real-time but needs to hear around 15 voice commands before becoming operational.

Respiratory diseases have been a significant public health challenge. Efficient disease evaluation and monitoring call for daily spirometry tests, as an effective way of pulmonary function testing, out of clinic. This requirement, however, is hard to be satisfied due to the large size and high costs of current spirometry equipments. In this paper, we present SpiroSonic, a new system design that uses commodity smartphones to support complete, accurate yet reliable spirometry tests in regular home settings with various environmental and human factors. SpiroSonic measures the humans' chest wall motion via acoustic sensing and interprets such motion into lung function indices, based on the clinically validated correlation between them. We implemented SpiroSonic as a smartphone app, and verified SpiroSonic's monitoring error over healthy humans as <3%. Clinical studies further show that SpiroSonic reaches 5%-10% monitoring error among 83 pediatric patients. Given that the error of in-clinic spirometry is usually around 5%, SpiroSonic can be reliably used for disease tracking and evaluation out of clinic.

This paper aims to use modern earphones as a platform for acoustic augmented reality (AAR). We intend to play 3D audio-annotations in the user’s ears as she moves and looks at AAR objects in the environment. While companies like Bose and Microsoft are beginning to release such capabilities, they are intended for outdoor environments. Our system aims to explore the challenges indoors, without requiring any infrastructure deployment. Our core idea is two-fold.(1) We jointly use the inertial sensors (IMUs) in earphones and smartphones to estimate a user’s indoor location and gazing orientation. (2) We play 3D sounds in the earphones and exploit the human’s responses to (re)calibrate errors in location and orientation. We believe this fusion of IMU and acoustics is novel, and could be an important step towards indoor AAR. Our system, EAAR, is tested on 7 volunteers invited to an AAR exhibition – like a museum – that we set up in our building’s lobby and lab. Across 60 different test sessions, the volunteers browsed different subsets of 24 annotated objects as they walked around. Results show that EAAR plays the correct audio-annotations with good accuracy. The user-feedback is encouraging and points to further areas of research and applications.

Temperature sensor is one of the most widespread technologies in the IoT era. Wireless temperature monitoring systems are convenient to deploy and can drive mass applications in the fields of smart home, transportation and logistics. Currently, wireless temperature monitoring products are based on microelectronic and semiconductor components, which are not cost-effective (e.g., a few dollars) and more importantly, generate electronic wastes. In this work, we present ThermoWave, a new paradigm of wireless temperature monitoring that is ecological, battery-less, and ultra-low cost. Specifically, ThermoWave is on the basis of the thermal scattering effect on millimeter-wave (mmWave) signals. Specifically, cholesteryl materials align their molecular patterns at different environmental temperatures, and this temperature-induced pattern change will be modulated and sensed by the scattered mmWave signals. There are three functional modules in the ThermoWave system. The ThermoTag is a cholesteryl material inked film or paper tag that can be conveniently attached to the object of interest to monitor temperature changes. Each ThermoTag costs less than 0.01 dollars. {The temperature modulated mmWave scattering will be received by a mmWave-radar based ThermoScanner and demodulated by a software-based temperature decoder ThermoSense, which includes a model-based method (i.e., ThermoDot) for point temperature estimation and a data-driven method (i.e., ThermoNet) for thermal imaging.} We prototype and evaluate the ThermoWave system performance in both controlled and real-world setups. Experimental results show that the ThermoWave achieves the precision of ± 1.0 \degree° F in the range of 30 ° F to 120 ° F in a controlled setup. We also investigate the performance in real-world applications, and the ThermoWave can reach the ± 3.0 ° F precision in the temperature estimation. {We also test and discuss sustainability, durability, robustness, and cost-effectiveness of the ThermoWave in both design and experiments.

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.

Millimeter-wave (mmWave) networking represents a core technology to meet the demanding bandwidth requirements of emerging connected vehicles. However, the feasibility of mmWave vehicle-to-everything (V2X) connectivity has long been questioned. One major doubt lies in how the highly directional mmWave links can sustain under high mobility. In this paper, we present the first comprehensive reality check of mmWave V2X networks. We deploy an experimental testbed to mimic a typical 5G V2X scenario, and customize a COTS mmWave radio to enable microscopic investigation of the channel and the link. We further construct a high-fidelity 3D ray-tracer to reproduce the mmWave characteristics at scale. With this tool set, we study the mmWave V2X coverage, mobility and blockage, codebook/beam management, and spatial multiplexing. Our measurement debunks some common misperceptions of mmWave V2X networks. In particular, due to the constrained roadway network structures, we find the beam management can be handled easily by the often-denounced beam scanning schemes, as long as the codebook is properly designed. Blockage can be almost eliminated through proper gNB deployment and cooperation. Highly effective spatial multiplexing can be realized even without sophisticated MIMO radios. Our work points to possible ways to realize efficient and reliable mmWave networks under high mobility, while maintaining the simplicity of standard network protocols.