People around the world are more connected today than ever before. By making phone calls, sending text messages and participating in online chats, mobile users are frequently interacting with their social connections through multiple communication channels. This trend is expected to continue with the emergence of immensely popular communication apps on mobile devices. Intuitively, these interactions on users' mobile phones can reveal valuable information regarding their social relationship with their phone contacts. Understanding such relationship can help provide new services and improve users' mobile experience. In this paper, we explore the opportunity to deeply understand these social relationship through mining mobile communication data. By building an on-device mining framework called Commsense, we show that automatically learning and understanding such relationship can efficiently support useful applications such as categorizing mobile contacts, identifying their relative importance, and automatically managing mobile contacts with very little human interference.

Radio Frequency IDentification (RFID) deployments are becoming increasingly popular in both industrial and consumer-oriented settings. To effectively exploit and operate such deployments, important challenges must be addressed, from managing RFID data streams to handling limitations in reader accuracy and coverage. Furthermore, deployments that support pervasive computing raise additional issues related to user acceptance and system utility. To better understand these challenges, we conducted a four-week study of a building-scale EPC Class-1 Generation-2 RFID deployment, the "RFID Ecosystem", with 47 readers (160 antennas) installed throughout an 8,000 square meter building. During the study, 67 participants having over 300 tags accessed the collected RFID data through applications including an object finder and a friend tracker and several tools for managing personal data. We found that our RFID deployment produces a very manageable amount of data overall, but with orders of magnitude difference among various participants and objects. We also find that the tag detection rates tend to be low with high variance across the type of tag, participant and object. Users need expert guidance to effectively mount their tags and are encouraged by compelling applications to wear tags more frequently. Finally, probabilistic modeling and inference techniques promise to enable more complex applications by smoothing over gaps and errors in the data, but must be applied with care as they add significant computational and storage overhead.

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).

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 users to specify events of interest using a graphical interface with an intuitive visual language. Cascadia also effectively extracts these events from data in spite of the unreliability of RFID technology and the inherent ambiguity in event extraction. We demonstrate Cascadia’s 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.

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.

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.

Wizard of Oz (WOz) testing has shown promise as an effective way to test location-enhanced applications. However, it is challenging to conduct a location-based WOz test because of the dynamic nature of target settings in the field. In particular, continuous location tracking, a major task in such a test, requires a wizard to frequently update a user's location to simulate a location system. This imposes a heavy task load on a wizard. To ease wizards' tasks for location tracking, we designed two techniques, Directional Crossing and Steering, and conducted a field experiment to investigate the performance of the two techniques. A quantitative analysis shows that Directional Crossing and Steering significantly lowered a wizard's task load for location tracking without sacrificing accuracy.