Continuous mobile sensing applications (CMSAs) run 24 hours in the background, consuming a significant amount of energy. Due to CMSA's dynamic behavior according to the users' context, it is very difficult to predict the energy consumption of CMSA. User trace-based solutions have been proposed to effectively estimate the energy consumption of CMSA, but they suffer burdensome user trace collection. We propose Scenethesizer, a scenario-based energy estimation system, which generates and augments the user traces for unseen application usage scenarios and estimate the energy consumption of CMSA based on the generated user traces.

The automotive navigation often embarrasses drivers by providing guidance that is awkward, incomprehensible, or almost impossible to follow. We point out the lack of on-the-spot awareness as the key reason behind this situation. The current navigation does not consider a driver's characteristics such as driving ability, as well as the detailed conditions of the current driving environment. In this paper, we explore the cases of embarrassment related to navigation usage. We collected a total of 56 cases of embarrassing moments from three drivers' experiences and derived 8 categories of embarrassing moments.

High-end mobile GPUs are now becoming an integral part of mobile devices. However, a mobile GPU constitutes a major portion of power consumption on the devices, and mobile games top as the most popular class of graphics applications. This paper presents the design and implementation of RAVEN, a novel, on-the-fly frame rate scaling system for mobile gaming applications. RAVEN utilizes human visual perception of graphics change to opportunistically achieve power saving without degrading user experiences. The system develops a light-weight frame comparison technique to measure and predict perception-aware frame similarity. It also builds a low resolution virtual display which clones the device screen for performing similarity measurement at a low-power cost. It is able to work on an existing commercial smartphone and support applications from app stores without any modifications. It has been implemented on Nexus 5X, and its performance has been measured with 13 games. The system effectively reduces the overall power consumption of mobile devices while maintaining satisfactory user experiences. The power consumption is reduced by 21.78% on aver-age and up to 34.74%.

A mobile GPU constitutes the majority of power consumption on a mobile device and mobile games top as the most popular class of graphics applications. In this demo, we present FROG, a novel frame rate optimization system for mobile gaming applications. FROG makes use of human visual perception to graphics and regulates application's frame rendering process on-the-fly for maximizing power saving without degrading the user experience. The system works on an existing commercial smartphone and support the legacy gaming applications from app stores without requiring any changes from applications

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

Emerging continuous sensing apps introduce new major factors governing phones' overall battery consumption behaviors: (1) added nontrivial persistent battery drain, and more importantly (2) different battery drain rate depending on the user's different mobility condition. In this paper, we address the new battery impacting factors significant enough to outdate users' existing battery model in real life. We explore an initial approach to help users understand the cause and effect between their physical activity and phones' battery life. To this end, we present Sandra, a novel mobility-aware smartphone battery information advisor, and study its potential to help users redevelop their battery model. We perform an extensive explorative study and deployment for 30 days with 24 users. Our findings reveal what they essentially learned, and in which situations they found Sandra very helpful. We share the lessons learned to help in the design of future mobility-aware battery advisors.

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.

Recently, a number of continuous sensing applications have been actively proposed in research communities and commercially released in the market. However, due to their unique power characteristics, user behavior-dependent battery drain, they bring new challenges for users' power management on these applications. In this demonstration, we present a comprehensive approach to support users' power management for continuous sensing applications. First, at pre-installation time, we provide an instant, personalized power estimation of a continuous sensing application. Without exhaustive trial and error, users can decide judiciously to install a certain application or not. Second, at runtime, we provide mobility-aware battery information. With this information, users can better estimate the phone's remaining battery life based on their imminent mobility conditions and take necessary actions in advance such as carrying an additional battery or minimizing the use of applications.