Tomorrow's autonomous mobile devices need accurate, robust and real-time sensing of their operating environment. Today's solutions fall short. Vision or acoustic-based techniques are vulnerable against challenging lighting conditions or background noise, while more robust laser or RF solutions require either bulky expensive hardware or tight coordination between multiple devices. This paper describes the design, implementation and evaluation of Ulysses, a practical environmental imaging system using colocated 60GHz radios on a single mobile device. Unlike alternatives that require specialized hardware, Ulysses reuses low-cost commodity networking chipsets available today. Ulysses' new imaging approach leverages RF beamforming, operates on specular (direct) reflection, and integrates the device's movement trajectory with sensing. Ulysses also includes a navigation component that uses the same 60GHz radios to compute "safety regions" where devices can move freely without collision, and to compute optimal paths for imaging within safety regions. Using our implementation of a small robotic car prototype, our experimental results show that Ulysses images objects meters away with cm-level precision, and provides accurate estimates of objects' surface materials.

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.

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.

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.

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 device's 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.

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.