A s the effects of Moore's law cause computing systems to become cheaper and more plentiful, a new problem arises: increasingly, the bottleneck in computing is not its disk capacity, processor speed, or communication bandwidth, but rather the limited resource of human attention. Human attention refers to a user's ability to attend to his or her primary tasks, ignoring system-generated distractions such as poor performance and failures. By exploiting plentiful computing resources to reduce user distraction, Project Aura is creating a system whose effectiveness is considerably greater than that of other systems today. Aura is specifically intended for pervasive computing environments involving wireless communication, wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose.To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layer's ability to anticipate requests from a higher layer. In today's systems, each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention.

This paper presents and evaluates two location sensing algorithms that we have developed and demonstrated. We present comparative accuracy results, complexity of training the system, and total power consumption required to perform scanning. Our method reduces training complexity by a factor of eight, and yields noticeable better accuracy. The paper also introduces a location information privacy model and reports on user study results. Our results indicate that users expect two unique behaviors from the privacy system, an introvert model where privacy is preferred, and an extrovert model where availability of information is preferred.

T he effects of Moore's law are apparent everywhere: Chip density, processor speed, memory cost, disk capacity, and network bandwidth are improving relentlessly. As computing costs plummet, a resource that we have ignored until now becomes the limiting factor in computer systems-user attention, namely a person's ability to focus on his or her primary task.Distractions occur especially in mobile environments, because walking, driving, or other real-world interactions often preoccupy the user. A pervasivecomputing environment that minimizes distraction must be context aware, and a pervasive-computing system must know the user's state to accommodate his or her needs.Context-aware applications provide at least two fundamental services: spatial awareness and temporal awareness. Spatially aware applications consider a user's relative and absolute position and orientation. Temporally aware applications consider the time schedules of public and private events. With an interdisciplinary class of Carnegie Mellon University (CMU) students, we developed and implemented a context-aware, pervasive-computing environment that minimizes distraction and facilitates collaborative design. Our approachTo identify the types of distraction that occur during the design process, we created an activity-attention matrix-the Distraction Matrix (see Figure 1). The Distraction Matrix categorizes activities as information (active and passive), communication (artificial, formal, and informal), and creation (contribution). Subcategories specify the types of primary activity within each category. For example, receiving information is a type of active-information activity, and initiating communication is a type of artificialcommunication activity.We based each distraction's location on how long it interrupts a primary activity. We categorized interruption durations as snap, pause, tangent, and extended. A snap distraction is one you usually complete in a few seconds, such as checking your watch; it should not interrupt your primary activity. A pause distraction involves stopping the primary activity, switching to a related one, and then switching back within a few minutes. Pulling over to the side of the road and checking directions is an example. A tangent distraction, such as receiving an unrelated phone call, is of medium duration and is unrelated to your primary activity. An extended distraction, such as stopping at a motel and resting for the night, is a relatively long-term interruption of your primary activity. ApplicationsWe equipped the campus with 400 wireless-networking access points, enabling wireless coverage for the entire campus. To move distractions toward the Distraction Matrix's left (snap) side, we implemented a complementary set of interactive applications and services that support mobile team-design activities. (See the related sidebar for information on relevant work in context-aware computing.) Portable Help Desk. Because they have many meetings at various times and locations, students are often To minimize distractions, a p...

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Abstract--Carnegie Mellon University has developed a User-Centered Interdisciplinary Concurrent System Design Methodology (UICSM) that takes teams of electrical engineers, mechanical engineers, computer scientists, industrial designers, and human computer interaction students that work with an end-user to generate a complete prototype system during a four-month long course. The methodology is web-supported and defines intermediary design products that document the evolution of the design. These products are posted on the web so that even remote designers and end-users can participate in the design activities. The design methodology proceeds through three phases: conceptual design, detailed design, and implementation. End-users critique the design at each phase. In addition, simulated and real application tasks provide further focus for design evaluation. The methodology has been used by the class, in designing over a dozen wearable computers, with diverse applications ranging from inspection and maintenance of heavy transportation vehicles to augmented reality in manufacturing and plant operations. The methodology includes monitoring and evaluation of the design process. This methodology is illustrated through a description of developing pervasive computing applications in collaboration with IBM during the Spring 2000 course.