Showing papers by "Neal Patwari published in 2017"

ARTI: An Adaptive Radio Tomographic Imaging System

Abstract: Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method.

Simultaneous Power-Based Localization of Transmitters for Crowdsourced Spectrum Monitoring

Abstract: The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

On-Wall, Wide Bandwidth E-Shaped Patch Antenna for Improved Whole-Home Radio Tomography

Abstract: Tagless identification and tracking with through-wall received signal strength-based radio tomographic imaging (RTI) allows emergency responders to learn where people are inside of a building before entering the building. Use of directional antennas in RTI nodes focuses RF power along the link line, improving system performance. However, antennas placed on a building’s exterior wall can be detuned by their close proximity to the dielectric, thus sending power across wider angles and resulting in less accurate imaging. In this paper, we improve through-wall RTI by using an E-shaped patch antenna we design to be mounted to an exterior wall. Along with its directionality, the E-shaped patch antenna is designed to avoid impedance mismatches when brought into close proximity of a dielectric material, thus increasing radiation through the exterior wall and along the link line. From our experiments, we demonstrate that the E-shaped patch antenna can reduce the median root mean square localization error by up to 43% when compared to microstrip patch and dipole antennas. For equal error performance, the E-shaped patch antenna allows an RTI system to reduce power and bandwidth usage by using fewer nodes and measuring on fewer channels.

On Log-Normality of RSSI in Narrowband Receivers Under Static Conditions

Abstract: A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.

EpiFi: An In-Home Sensor Network Architecture for Epidemiological Studies

Abstract: We design and build a system called EpiFi, which allows epidemiologists to easily design and deploy experiments in homes. The focus of EpiFi is reducing the barrier to entry for deploying and using an in-home sensor network. We present a novel architecture for in-home sensor networks configured using a single configuration file and provide: a fast and reliable method for device discovery when installed in the home, a new mechanism for sensors to authenticate over the air using a subject's home WiFi router, and data reliability mechanisms to minimize loss in the network through a long-term deployment. We work collaboratively with pediatric asthma researchers to design three studies and deploy EpiFi in homes.

RSS Models for Respiration Rate Monitoring

Abstract: Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The implications of the model are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

Demo Abstract: IASA - Indoor Air Quality Sensing and Automation

Abstract: Using an air purifying system can remove indoor air pollutants, but because it increases electric power utilization, results in broader increases in air pollution. To explore the tradeoff between energy consumption and healthful air, we demonstrate the indoor air sensing and automation (IASA) system, an internet-of-things system. The IASA system uses an air quality sensor, gateway device, and smart thermostat to control the fan in a home’s heating and cooling system. When fine particulate matter is high, the system operates the fan to pull air through a furnace filter and remove the pollution from the indoor air. We describe our system design, deployment, and collected data. To date, we have collected 861,000 air quality measurements with IASA.

Focusing through walls: An E-shaped patch antenna improves whole-home radio tomography

Abstract: Tagless identification and tracking with through-wall received signal strength-based radio tomographic imaging (RTI) allows emergency responders to learn where people are inside of a building before entering the building. Use of directional antennas in RTI nodes focuses RF power along the link line, improving system performance. However, antennas placed on a building's exterior wall can be detuned by their close proximity to the dielectric, thus sending power across wider angles and resulting in less accurate imaging. In this paper, we improve through-wall RTI by using an E-shaped patch antenna we design to be mounted to an exterior wall. Along with its directionality, the E-shaped patch antenna is designed to avoid impedance mismatches when brought into close proximity of a dielectric material, thus increasing radiation through the exterior wall and along the link line. From our experiments, we demonstrate that the E-shaped patch antenna can reduce the median root mean square localization error by up to 43% when compared to microstrip patch and omnidirectional antennas. For equal error performance, the E-shaped patch antenna allows an RTI system to reduce power and bandwidth usage by using fewer nodes and measuring on fewer channels.

Poster: Link Line Crossing Speed Estimation with Narrowband Signal Strength

Abstract: We present results from a system which uses received signal strength (RSS) measurements to estimate the speed at which a person is walking when they cross the link line. While many RSS-based device-free localization systems can detect a line crossing, this system estimates additionally the speed of crossing, which can provide significant additional information to a tracking system. Further, unlike device-free RF sensors which occupy tens of MHz of bandwidth, this system uses a channel of about 10 kHz. Experiments with a person walking from 0.3 to 1.8 m/s show the system can measure walking speed within 0.05 m/s RMS error.

RFID Localization in Wireless Sensor Networks

Abstract: Received signal strength (RSS)-based localization of people and assets through RFID has significant benefits for logistics, security and safety. However, the accuracy of RFID localization in wireless sensor networks suffers from unrealistic antenna gain pattern assumption, and the human body has a major effect on the gain pattern of the RFID badge that the person is wearing. In this book chapter, the gain pattern due to the effect of the human body is experimentally measured and modeled. A method is presented to estimate the model parameters from multiple RSS measurements. Two joint orientation and position estimators, four-dimensional (4D) maximum likelihood estimation (MLE) algorithm and alternating gain and position estimation (AGAPE) algorithm, are proposed to estimate the orientation and the position of the badge using RSS measurements from anchor nodes. A Bayesian lower bound on the mean squared error of the joint estimation is derived and comparedwith the Cramer-Rao boundwith an isotropic gain pattern. Both theoretical and experimental results show that the accuracy of position estimates can be improved with orientation estimates included in the localization system.