Today's smartphone application (hereinafter `app') markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users' awareness. We propose PowerForecaster to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps' power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel power emulator as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users' behaviors and emulating the target app's power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Especially, CoMon+ improves the negotiation method proposed in our earlier work, CoMon [30], to exploit multiple processing plans enabled by various personal sensing devices; each plan can be alternatively used for cooperation, which in turn will maximize overall power saving. We implement a CoMon+ prototype and show that it provides significant benefit for mobile sensing applications, e.g., saving 27-71 percent of smartphone power consumption depending on cooperation cases. Also, our deployment study shows that CoMon+ saves an average 19.7 percent of battery under daily use of a prototype application compared to the case without CoMon+ running.

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.

In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans for application requests and selectively applies them according to resource availability and demands at runtime. Through the selection, it resolves resource contention among applications and helps them efficiently share resources. With such system-level supports, applications become capable of providing long-running services under dynamic circumstances with scarce resources. Also, the platform can host a number of applications stably, exploiting its full resource capacity. We build an Orchestrator prototype on off-the-shelf mobile devices and sensor motes and show its effectiveness in terms of application supportability and resource use efficiency.

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

No abstract available.

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications.

No abstract available.

In this paper, we propose a novel efficient dataflow execution method for mobile context monitoring applications. As a key approach to minimize the execution overhead, we propose a new dataflow execution model, producer-oriented model. Compared to the conventional consumer-oriented model adopted in stream processing engines, our model significantly reduces execution overhead to process context monitoring dataflow reflecting unique characteristics of context monitoring. To realize the model, we develop DataBank, an execution container that takes charge of the management and delivery of the output data for the associated operator. We demonstrate the effectiveness of DataBank by implementing three useful applications and their dataflow graphs, i.e., MusicMap, FindMyPhone, and CalorieMonitor. Using the applications, we show that DataBank reduces the CPU utilization by more than 50%, compared to the methods based on the consumer-oriented model; DataBank enables more context monitoring applications to run concurrently.

In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive computing, a number of useful mobile context-aware applications have been proposed, but their actual deployment is slow due to complexity of context processing and heavy resource and battery usage. To address such challenges, we have been building MobiCon for many years, upon which diverse context-aware applications are developed and deployed without concerns about complexity of context processing and resource optimization.

Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent sensing workloads raise a new challenge incurring severe resource contention among themselves and with other foreground applications. To address the challenge, we develop SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications. As its key approach, we develop a novel sensing-flow-aware coordination. We first introduce the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams. Leveraging the identified frame structures, SymPhoney develops frame-based coordination and scheduling mechanisms, which effectively coordinates the resource use of concurrent contending applications and maximize their utilities even under severe resource contention. We implemented several sensing applications on top of the SymPhoney engine and performed extensive experiments, showing effective coordination capability of SymPhoney.

No abstract available.

In upcoming ubiquitous environment, many real-time sensing applications will emerge. These applications show different scale and characteristics on delivery demands. The applications commonly depend on real-time understanding on data from widely distributed data sources. Also, they have highly diverse and complex delivery demands in terms of data and delay, e.g., data value ranges of interest, spatial and temporal resolution and tolerable delay, etc. Due to the remarkable scale and complexity, however, existing data delivery schemes cannot support the applications effectively. We present a novel data delivery scheme to support real-time sensing applications. Our scheme provides efficient delivery paths over Internet-based delivery infrastructure, which is comprised of a collection of dispersed nodes forming an overlay network. It fully exploits the diversity of delivery demands on both data and delay requirements, thus achieving high level of service satisfaction and efficiency at the same time. Also, it distributes path management overhead to multiple nodes by adopting a hierarchical path management approach. We evaluate our scheme through a large-scale simulation. The results show that it achieves a high level of scalability and bandwidth efficiency.

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.