Evan Welbourne

Mobile context inference using low-cost sensors

In this paper, we introduce a compact system for fusing location data with data from simple, low-cost, non-location sensors to infer a users place and situational context. Specifically, the system senses location with a GSM cell phone and a WiFi-enabled mobile device (each running Place Lab), and collects additional sensor data using a 2” x 1” sensor board that contains a set of common sensors (e.g. accelerometers, barometric pressure sensors) and is attached to the mobile device. Our chief contribution is a multi-sensor system design that provides indoor-outdoor location information, and which models the capabilities and form factor of future cell phones. With two basic examples, we demonstrate that even using fairly primitive sensor processing and fusion algorithms we can leverage the synergy between our location and non-location sensors to unlock new possibilities for mobile context inference. We conclude by discussing directions for future work.

Cascadia: A System for Specifying, Detecting, and Managing RFID Events

Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries). We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.

Challenges for Pervasive RFID-Based Infrastructures

The success of RFID in supply chain management is leading many to consider more personal and pervasive deployments of this technology. Unlike industrial settings, however, deployments that involve humans raise new and critical problems related to privacy, security, uncertainty, and a more diverse and evolving set of applications. At the University of Washington, we are deploying a building-wide RFID-based infrastructure with hundreds of antennas and thousands of tags. Our goal is to uncover the issues of pervasive RFID deployments and devise techniques for addressing these issues before such deployments become common place. In this paper, we present the challenges encountered and lessons learned during a smaller-scale pilot deployment of the system. We show some preliminary results and, for each challenge, discuss how we addressed it or how we are planning on addressing it

Access control over uncertain data

Access control is the problem of regulating access to secret information based on certain context information. In traditional applications, context information is known exactly, permitting a simple allow/deny semantics. In this paper, we look at access control when the context is itself uncertain. Our motivating application is RFID data management, in which the location of objects and people, and the associations between them is often uncertain to the system, yet access to private data is strictly defined in terms of these locations and associations. We formalize a natural semantics for access control that allows the release of partial information in the presence of uncertainty and describe an algorithm that uses a provably optimal perturbation function to enforce these semantics. To specify access control policies in practice, we describe UCAL, a new access control language for uncertain data. We then describe an output perturbation algorithm to implement access control policies described by UCAL. We carry out a set of experiments that demonstrate the feasibility of our approach and confirm its superiority over other possible approaches such as thresholding or sampling.

Specification and verification of complex location events with panoramic

We present the design and evaluation of Panoramic, a tool that enables end-users to specify and verify an important family of complex location events. Our approach aims to reduce or eliminate critical barriers to deployment of emerging location-aware business activity monitoring applications in domains like hospitals and office buildings. Panoramic does not require users to write code, understand complex models, perform elaborate demonstrations, generate test location traces, or blindly trust deterministic events. Instead, it allows end-users to specify and edit complex events with a visual language that embodies natural concepts of space and time. It also takes a novel approach to verification, in which events are extracted from historical sensor data traces and then presented with intelligible, hierarchical visualizations that represent uncertainty with probabilities. We build on our existing software for specifying and detecting events while enhancing it in non-trivial ways to facilitate event specification and verification. Our design is guided by a formative study with 12 non-programmers. We also use location traces from a building-scale radio frequency identification (RFID) deployment in a qualitative evaluation of Panoramic with 10 non-programmers. The results show that end-users can both understand and verify the behavior of complex location event specifications using Panoramic.

A demonstration of Cascadia through a digital diary application

The Cascadia system provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia allows application developers and even users to specify events of interest using either a declarative query language or a graphical interface with an intuitive visual language. Cascadia then effectively extracts these events from data in spite of the unreliability of RFID technology and the inherent ambiguity in event extraction. We demonstrate Cascadias technique through a digital diary application in the form of a calendar. Cascadia automatically populates the calendar with meaningful events for the user. We use data collected in a building-wide RFID deployment.

Musicology: Bringing Personal Music into Shared Spaces

The Musicology project examines the user, ecosystem, and technology aspects necessary to enable playback of personal music in public spaces. User surveys, field interviews, and a technology prototype inform a discussion relating to privacy and identity, content control, and power consumption (among others). Understanding how mobile devices can be used to present personal content, initially with audio but eventually through photographs and other media, has the potential to open up new avenues for personal expression, bringing the motivations behind online content sharing websites into a publicly social arena.