We consider optimizing the control of the wide-area link of a home router based on the needs of individual users instead of assuming a canonical user. A careful user study clearly demonstrates that measured end-user satisfaction with a given set of home network conditions is highly variable - user perception and opinion of acceptable network performance is very different from user to user. To exploit this fact we design, implement, and evaluate a prototype system, EmNet, that incorporates direct user feedback from a simple user interface layered over existing web content. This feedback is used to dynamically configure a weighted fair queuing (WFQ) scheduler on the wide-area link. We evaluate EmNet in terms of the measured satisfaction of end-users, and in terms of the bandwidth required. We compare EmNet with an uncontrolled link (the common case today), as well as with statically configured WFQ scheduling. On average, EmNet is able to increase overall user satisfaction by 20% over the uncontrolled network and by 12% over static WFQ. EmNet does so by only increasing the average application bandwidth by 6% over the static WFQ scheduler.

Sensor networks have the potential to empower domain experts from a wide range of fields. However, presently they are notoriously difficult for these domain experts to program, even though their applications are often conceptually simple. We address this problem by applying the BASIC programming language to sensor networks and evaluating its effectiveness. BASIC has proven highly successful in the past in allowing novices to write useful programs on home computers. Our contributions include a user study evaluating how well novice (no programming experience) and intermediate (some programming experience) users can accomplish simple sensor network tasks in BASIC and in TinyScript (a principally event-driven high-level language for node-oriented programming) and an evaluation of power consumption issues in BASIC. 45--55% of novice users can complete simple tasks in BASIC, while only 0--17% can do so in TinyScript. In both languages, users generally are most successful using imperative loop-oriented programming. The use of an interpreter, such as our BASIC implementation, has little impact on the power consumption of applications in which computational demands are low. Further, when in final form, BASIC can be compiled to reduce power consumption even further.

Any architectural optimization aims at satisfying the end user. However, modern architectures execute with little to no knowledge about the individual user. If architectures could determine whether their users are satisfied, they could provide higher efficiency; improved reliability, reduced power consumption, increased security, and a better user experience. A major reason for this limitation is their input devices. Specifically, the traditional input devices (e.g., the mouse and keyboard) provide limited information about the user. In this paper, we make a case for the addition of new biometric input devices for providing the computer information about the userpsilas physiological traits. We explore three biometric devices as potential sensors: an eye tracker, a galvanic skin response (GSR) sensor, and force sensors. We first present two user studies that explore the link between the sensor readings and user satisfaction when the performance of the processor is varied as a video game is being played. In the first study, we drastically drop the processor clock frequency at a set point in the game. In the second study, we set the clock frequency to randomly-selected levels during game play. Both studies show that there are significant changes in human physiological traits as performance decreases. More importantly, we show that physiological changes correlate strongly to the satisfaction levels reported by the users. Based upon these observations, we construct a Physiological Traits-based Power-management (PTP) system that can be applied to existing dynamic voltage and frequency scaling (DVFS) schemes. We apply PTP to a typical CPU-utilization-based adaptive DVFS policy and evaluate our scheme using a third user study. An aggressive version of our PTP scheme reduces the total system power consumption of a laptop by up to 33.3% for an application averaged across users (18.1% averaged across three applications), while a conservative version reduces the total system power consumption by up to 25.6% across users (11.4% averaged across three applications).