Software defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowdsourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address these needs, we design and develop a compact, portable, untethered, and inexpensive SDR we callSitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. We present results from a user study involving more than 100 participants to evaluate Sitara in a hypothetical large-scale crowdsourced spectrum monitoring application. We also present a comparative analysis of Sitara to related crowdsensing systems with a particular emphasis on the role of incentives and user participation.

In this paper, we show how beacon-based indoor localization and additional environmental fingerprints like magnetic field data can be used to both accelerate and increase the robustness of Augmented Reality (AR) relocalization. We show how the combination of Visual Inertial Odometry (VIO) and beacons can be used to construct a dense indoor magnetic field map that can act as a fine-grained calibration for compasses to quickly determine a mobile device's orientation. Unique to our approach is that we leverage accurate VIO trajectories to provide full vector magnetic field mapping that can be collected and used with devices placed in any orientation. We demonstrate a system running on an iPhone that can acquire location with 80th percentile 3D accuracy of 27cm in LOS and 46cm in NLOS, and our magnetic field mapping approach can instantly estimate orientation with 80th percentile accuracy of 11.7 degrees. We demonstrate an end-to-end system for generating a magnetic field map and subsequently localizing and tracking mobile users using beacons and VIO. This has the side effect of enabling multi-user (even cross-platform) AR applications, as all users can now be localized with respect to a common global reference without any sharing of visual feature maps.

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.

In this demonstration, we present OpenChirp, an open-source Low-Power Wide-Area Networking (LPWAN) infrastructure. OpenChirp is a management framework that provides data context, storage, visualization, and access control over the web. At the physical layer of the system, we present LPRAN, a low-cost high-performance software-defined radio hardware platform that can receive signals up to -30 dB below the noise floor. Using our LPRAN hardware, it is possible to operate on raw I/Q streams in the cloud to perform collaborative tasks across multiple gateways such as jointly decoding weak signals and localization.

Low-Power Wide-Area Networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.

We specify and evaluate a new software-defined clock network architecture, Stitch. We use Stitch to derive all subsystem clocks from a single local oscillator (LO) on an embedded platform, and enable efficient radio frequency synchronization (RFS) between two nodes' LOs. RFS uses the complex baseband samples from a low-power low-cost narrowband transceiver to drive the frequency difference between the two devices to less than 3 parts per billion (ppb). Recognizing that the use of a wideband channel to measure clock frequency offset for synchronization purposes is inefficient, we propose to use a separate narrowband radio to provide these measurements. However, existing platforms do not provide the ability to unify the local oscillator across multiple subsystems. We demonstrate Stitch with a reference hardware implementation on a research platform. We show that, with Stitch and RFS, we are able to achieve dramatic efficiency gains in ultra-wideband (UWB) time synchronization and ranging. We demonstrate the same UWB ranging accuracy in state-of-the-art systems but with 59% less utilization of the UWB channel.

We demonstrate multi-user persistent Augmented Reality (AR) on mobile devices with a novel technique that provides nearly instant acquisition of location and orientation. Visual Inertial Odometry (VIO) provides accurate position and orientation tracking relative to device start-up for AR applications. Unfortunately, the tracking is local to the AR session of a single user and is not anchored in a global coordinate system. In order to provide all devices an accurate location in a common frame of reference, we utilize UWB nodes that range to the devices. To avoid the long startup time required to compute the device's orientation, we propose a novel technique that utilizes previously recorded magnetic field information to rapidly calibrate the compass. In order to simplify setup, we demonstrate automatic mapping of beacon locations and surveying of magnetic field by a pedestrian walking around the test area with a mobile device.

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

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.

Detecting and locating outdoor border crossing events is valuable information in curbing drug trafficking, reducing poaching, and protecting high-asset equipment and goods. However, border sensing is notoriously challenging, prone to false alarms and missed detections, with serious consequences. Weather events, like rain and wind, make it even more challenging to maintain a low level of missed detections and false alarms. In this paper, we propose and test an automated system of wireless sensors which uses received signal strength (RSS) measurements to localize where a border crossing occurs. In addition, we develop new RSS-based statistical models and methods that can quickly be initialized and updated by using link RSS statistics to adapt to time-varying RSS changes due to weather events. These models are implemented in two new classifiers that localize border crossings with few missed detections and false alarms. We validate our proposed methods by implementing one of the classifiers in a three month long deployment of a solar-powered, real-time system that captures images of the border for ground truth validation. Furthermore, over 75 hours of RSS measurements are collected with an emphasis on collection during weather events, like rain and wind, during which we expect our classifiers to perform the worst. We demonstrate that the proposed classifiers outperform four other baseline classifiers in terms of false alarm probability by 1 to 4 orders of magnitude, and in terms of the misclassification probability by 1 to 2 orders of magnitude.

We introduce an platform architecture and algorithm to frequency synchronize multiple devices. The platform allows clock unification among oscillator, microcontroller, and radio. The platform accesses complex baseband samples from the radio, estimates the carrier frequency offset, and iteratively drives the main local oscillator (LO) frequency difference between two devices to zero.

The respiration rate of a person provides critical information about their well-being. Conventionally, contact sensing is used for breathing monitoring; however, it is expensive, uncomfortable, and immobile. In-home non-contact breathing monitoring is now possible via Doppler radar and motion capture video sensors, yet these technologies are limited in mobility, among other limitations. When monitoring a patient who is free to move around his or her home, it is dificult to scale current non-contact sensors to cover the large area. Our RUBreathing sensor system uses RF received signal strength (RSS) in a network to estimate breathing rate in real-time with high accuracy over a wide area. In this demonstration, we show the sensor continuously estimating a patient's respiration rate from non-contact RSS measurements between wireless devices.

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively “guess-and-retest”, i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously “dial it in”, i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.

Current sensor network nodes are not designed for a rapid succession of large-scale deployments. While hundreds of nodes have been deployed in static networks (e.g. ExScale, GreenOrb) large-scale, mobile, repetitive deployments are difficult to manage. We developed a new platform, the WREN, to be a low-cost, easy to maintain and rapid to deploy, wireless sensing node for human contact network measurements.