Abstract. Battery lifetime has become one of the top usability concerns of mobile systems. While many endeavors have been devoted to improving battery lifetime, they have fallen short in understanding how users interact with batteries. In response, we have conducted a systematic user study on battery use and recharge behavior, an important aspect of user-battery interaction, on both laptop computers and mobile phones. Based on this study, we present three important findings: 1) most recharges happen when the battery has substantial energy left, 2) a considerable portion of the recharges are driven by context (location and time), and those driven by battery levels usually occur when the battery level is high, and 3) there is great variation among users and systems. These findings indicate that there is substantial opportunity to enhance existing energy management policies, which solely focus on extending battery lifetime and often lead to excess battery energy upon recharge, by adapting the aggressiveness of the policy to match the usage and recharge patterns of the device. We have designed, deployed, and evaluated a user-and statistics-driven energy management system, Llama, to exploit the battery energy in a user-adaptive and user-friendly fashion to better serve the user. We also conducted a user study after the deployment that shows Llama effectively harvests excess battery energy for a better user experience (brighter display) or higher quality of service (more application data) without a noticeable change in battery lifetime.

SUMMARYJavelin is a Java-based infrastructure for global computing. This paper presents Javelin++, an extension of Javelin, intended to support a much larger set of computational hosts. Contributions to scalability and fault tolerance are presented. This is the focus of the paper. Two scheduling schemes are presented: probabilistic work stealing and deterministic work stealing. The distributed deterministic work stealing is integrated with a distributed deterministic eager scheduler, which is one of the paper's primary original contributions. An additional fault tolerance mechanism is implemented for replacing hosts that have failed or retreated. A Javelin++ API is sketched, then illustrated on a raytracing application. Performance results for the two schedulers are reported, indicating that Javelin++, with its broker network, scales better than the original Javelin.

Homes powered fully or partially by renewable sources such as solar are becoming more widely adopted, however energy management strategies in these environments are lacking. This paper presents the first results of a study that explores home automation techniques for achieving better utilization of energy generated by renewable technologies. First, using a network of off-the-shelf sensing devices, we observe that energy generation and consumption in an off-grid home is both variable and predictable. Moreover, we find that reactive energy management techniques are insufficient to prevent critical battery situations. We then present a recommendation based system for helping users to achieve better utilization of resources. Our study demonstrates the feasibility of three recommendation components: an early warning system that allows users of renewable technologies to make more conservative decisions when energy harvested is predicted to be low; a task rescheduling system that advises users when high-power appliances such as clothes dryers should be run to optimize overall energy utilization; and an energy conservation system that identifies sources of energy waste and recommends more conservative usage.

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Quadriplegia and paraplegia are disabilities that result from injuries to the spinal cord and neuromuscular disorders such as cerebral palsy. Patients suffering from quadriplegia have varied levels of impaired motor movements, hence, performing quotidian tasks like controlling home appliances is challenging for quadriplegics. The use of hand and eye gestures to perform these tasks is a plausible remedy, but available solutions often assume considerable limb movement, are not fit for long-term use, and may not be applicable to quadriplegics with varied range of motor impairments. To address this problem, we present the design, implementation, and evaluation of a multi-sensor gesture recognition system that uses comfortable and low power wearable sensors. We have designed an EOG-based headband using textile electrodes and a glove that uses flex sensors and an accelerometer to detect eye and hand gestures. The gestures are used to control appliances remotely in a home setting and we show that they have good accuracy, latency, and energy consumption characteristics.