Yanzi Zhu

Reusing 60GHz Radios for Mobile Radar Imaging

The future of mobile computing involves autonomous drones, robots and vehicles. To accurately sense their surroundings in a variety of scenarios, these mobile computers require a robust environmental mapping system. One attractive approach is to reuse millimeterwave communication hardware in these devices, e.g. 60GHz networking chipset, and capture signals reflected by the target surface. The devices can also move while collecting reflection signals, creating a large synthetic aperture radar (SAR) for high-precision RF imaging. Our experimental measurements, however, show that this approach provides poor precision in practice, as imaging results are highly sensitive to device positioning errors that translate into phase errors. We address this challenge by proposing a new 60GHz imaging algorithm, {\em RSS Series Analysis}, which images an object using only RSS measurements recorded along the devices trajectory. In addition to object location, our algorithm can discover a rich set of object surface properties at high precision, including object surface orientation, curvature, boundaries, and surface material. We tested our system on a variety of common household objects (between 5cm--30cm in width). Results show that it achieves high accuracy (cm level) in a variety of dimensions, and is highly robust against noises in device position and trajectory tracking. We believe that this is the first practical mobile imaging system (re)using 60GHz networking devices, and provides a basic primitive towards the construction of detailed environmental mapping systems.

Noncoherent mmWave Path Tracking

Millimeter (mm) wave picocellular networks have the potential for providing the 1000X capacity increase required to keep up with the explosive growth of mobile data. However, maintaining beams towards mobile users and adapting to frequent blockage, requires efficient, dynamic path tracking algorithms. In this paper, we develop and experimentally demonstrate a novel noncoherent compressive strategy for this problem, and compare it with conventional hierarchical and exhaustive beam scanning. To the best of our knowledge, this is the first experimental demonstration of practical, scalable path estimation for mmWave/60GHz picocells. Our results indicate the feasibility of sub-second path tracking with low overhead on todays mmWave hardware, and open up a rich space for design of 5G mmWave networks.

Empirical Validation of Commodity Spectrum Monitoring

We describe our efforts to empirically validate a distributed spectrum monitoring system built on commodity smartphones and embedded low-cost spectrum sensors. This system enables real-time spectrum sensing, identifies and locates active transmitters, and generates alarm events when detecting anomalous transmitters. To evaluate the feasibility of such a platform, we perform detailed experiments using a prototype hardware platform using smartphones and RTL dongles. We identify multiple sources of error in the sensing results and the end-user overhead (i.e. smartphone energy draw). We propose and implement a variety of techniques to identify and overcome errors and uncertainty in the data, and to reduce energy consumption. Our work demonstrates the basic viability of user-driven spectrum monitoring on commodity devices.

60GHz Mobile Imaging Radar

Mobile computing is undergoing a significant shift. Where traditional mobile networks revolved around users and their movements, new networks often center around autonomous mobile agents. These include semi-autonomous drones on military missions, vacuum robots search for dirt at home, intelligent cars that deliver us to work, and first responder robots that find and rescue victims in disasters. A critical challenge limiting these autonomous devices is the lack of accurate sensing systems, e.g. a mobile imaging system that captures the position, shape and surface material of nearby objects. These devices often require high levels of accuracy, and operate under tight constraints: in low-light conditions or moving at moderate speeds. These constraints dramatically reduce the set of possible solutions, eliminating traditional imaging systems that rely on visible light or specialized hardware. In this paper, we present early results in our efforts to design and evaluate a digital imaging radar system using reflections from 60GHz wireless beams. By using user mobility to emulate a virtual antenna array with large aperture, we build virtual antennas with large aperture and high precision. We describe details of our design, including mechanisms for object detection, object imaging, and controlling precision. Our experiments on a real 60GHz testbed show that we can achieve high precision (~1 cm) imaging with as little user movement as half a meter, as well as added potential for using loss profiles to infer the surface material on detected objects.

Identifying Value in Crowdsourced Wireless Signal Measurements

While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and 1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSS-weighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.

Binary stochastic implementation of digital logic

Stochastic computing refers to a mode of computation in which numbers are treated as probabilities implemented as 0/1 bit streams, which essentially is a unary encoding scheme. Previous work has shown significant reduction in area and increase in fault tolerance for low to medium resolution values (6-10 bits). However, this comes at very high latency cost. We propose a novel hybrid approach combining traditional binary with unary stochastic encoding, called binary stochastic. Similar to the binary representation, it is a positional number system, but instead of only 0/1 digits, the digits would be fractions. We show how simple logic such as adders and multipliers can be implemented, and then show more complex function implementations such as the gamma correction function and functions such as tanh, absolute and exponentiation using both combinational and sequential binary stochastic logic. Our experiments show significant reduction in latency compared to unary stochastic, while using significantly smaller area compared to binary implementations on FPGAs.

Adversarial Localization against Wireless Cameras

This paper identifies and empirically evaluates the effectiveness of adversarial localization attacks against wireless IoT devices, e.g., wireless security cameras in the home. We use experiments in home and office settings to show that attackers can accurately pinpoint the location of WiFi cameras, using a small amount of stealthy, passive, exterior measurements coupled with unsupervised learning techniques. We also show that current defenses have minimal impact against these attacks, and are also easily circumvented via countermeasures. Thus significant work is needed to develop robust defenses against these attacks.

Trimming the Smartphone Network Stack

Network transmissions are the cornerstone of most mobile apps today, and a main contributor to energy consumption. We use a componentized energy model to quantify energy use by device, and observe significant energy consumption by the CPU in network operations. We assert that optimizing network operations in the CPU can produce significant energy savings, and explore the impact of two potential approaches: one-copy data moves and offloading the network stack to the basestation.